Anthropic API Integration Guide
2024-10-2410 turns183,244 charso1-mini
Summary
user is trying to integrate anthropic api into existing django application
Messages
starting from this code, adapt and convert the utils.py and other files as necessary to use the Anthropic API instead of XAi, the documentation for Anthropic can be found here, use the latest possible model when constructing the prompt https://docs.anthropic.com/en/home The Code-- import logging
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status, generics
from .serializers import PersonaSerializer, BlogPostSerializer
from .models import Persona, BlogPost
from .utils import generate_content
logger = logging.getLogger(__name__)
class AnalyzeWritingSampleView(APIView):
def post(self, request, *args, **kwargs):
logger.debug(f"Request data: {request.data}")
serializer = PersonaSerializer(data=request.data)
if serializer.is_valid():
persona = serializer.save()
return Response(PersonaSerializer(persona).data, status=status.HTTP_201_CREATED)
else:
logger.error(f"Serializer validation failed: {serializer.errors}")
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class GenerateContentView(APIView):
def post(self, request):
persona_id = request.data.get('persona_id')
prompt = request.data.get('prompt')
if not persona_id:
logger.warning('persona_id is required.')
return Response({'error': 'persona_id is required.'}, status=status.HTTP_400_BAD_REQUEST)
if not prompt:
logger.warning('prompt is required.')
return Response({'error': 'prompt is required.'}, status=status.HTTP_400_BAD_REQUEST)
try:
persona = Persona.objects.get(id=persona_id)
except Persona.DoesNotExist:
logger.warning(f"Persona with ID {persona_id} not found.")
return Response({'error': f'Persona with ID {persona_id} not found'}, status=status.HTTP_404_NOT_FOUND)
# Call generate_content to create the blog post content
blog_post_content = generate_content(persona.data, prompt)
if not blog_post_content:
logger.error('Failed to generate blog post content.')
return Response({'error': 'Failed to generate blog post content.'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
# Ensure content is split into title and body
lines = blog_post_content.strip().split('\n')
title = lines[0] if lines else 'Untitled'
content = '\n'.join(lines[1:]) if len(lines) > 1 else ''
# Create the BlogPost object and save it to the database
blog_post = BlogPost.objects.create(
persona=persona,
title=title,
content=content,
)
# Log for debugging purposes
logger.debug(f"Blog post created with title: {title}")
# Return the serialized blog post data
return Response(BlogPostSerializer(blog_post).data, status=status.HTTP_201_CREATED)
class PersonaListView(generics.ListAPIView):
queryset = Persona.objects.all()
serializer_class = PersonaSerializer
class PersonaDetailView(APIView):
def get(self, request, persona_id):
try:
persona = Persona.objects.get(id=persona_id)
except Persona.DoesNotExist:
logger.warning(f"Persona with ID {persona_id} not found.")
return Response({'error': 'Persona not found'}, status=status.HTTP_404_NOT_FOUND)
serializer = PersonaSerializer(persona)
return Response(serializer.data, status=status.HTTP_200_OK)
class BlogPostView(generics.ListAPIView):
queryset = BlogPost.objects.all().order_by('-created_at')
serializer_class = BlogPostSerializer
import logging
import requests
import json
import re
from decouple import config
logger = logging.getLogger(__name__)
XAI_API_KEY = config('XAI_API_KEY')
XAI_API_BASE = "https://api.x.ai/v1"
def analyze_writing_sample(writing_sample):
endpoint = f"{XAI_API_BASE}/chat/completions"
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {XAI_API_KEY}"
}
payload = {
"messages": [
{
"role": "system",
"content": "You are an assistant that analyzes writing samples."
},
{
"role": "user",
"content": f'''
Please analyze the writing style and personality of the given writing sample. Provide a detailed assessment of their characteristics using the following template. Rate each applicable characteristic on a scale of 1-10 where relevant, or provide a descriptive value. Return the results in a JSON format.
"name": "[Author/Character Name]",
"vocabulary_complexity": [1-10],
"sentence_structure": "[simple/complex/varied]",
"paragraph_organization": "[structured/loose/stream-of-consciousness]",
"idiom_usage": [1-10],
"metaphor_frequency": [1-10],
"simile_frequency": [1-10],
"tone": "[formal/informal/academic/conversational/etc.]",
"punctuation_style": "[minimal/heavy/unconventional]",
"contraction_usage": [1-10],
"pronoun_preference": "[first-person/third-person/etc.]",
"passive_voice_frequency": [1-10],
"rhetorical_question_usage": [1-10],
"list_usage_tendency": [1-10],
"personal_anecdote_inclusion": [1-10],
"pop_culture_reference_frequency": [1-10],
"technical_jargon_usage": [1-10],
"parenthetical_aside_frequency": [1-10],
"humor_sarcasm_usage": [1-10],
"emotional_expressiveness": [1-10],
"emphatic_device_usage": [1-10],
"quotation_frequency": [1-10],
"analogy_usage": [1-10],
"sensory_detail_inclusion": [1-10],
"onomatopoeia_usage": [1-10],
"alliteration_frequency": [1-10],
"word_length_preference": "[short/long/varied]",
"foreign_phrase_usage": [1-10],
"rhetorical_device_usage": [1-10],
"statistical_data_usage": [1-10],
"personal_opinion_inclusion": [1-10],
"transition_usage": [1-10],
"reader_question_frequency": [1-10],
"imperative_sentence_usage": [1-10],
"dialogue_inclusion": [1-10],
"regional_dialect_usage": [1-10],
"hedging_language_frequency": [1-10],
"language_abstraction": "[concrete/abstract/mixed]",
"personal_belief_inclusion": [1-10],
"repetition_usage": [1-10],
"subordinate_clause_frequency": [1-10],
"verb_type_preference": "[active/stative/mixed]",
"sensory_imagery_usage": [1-10],
"symbolism_usage": [1-10],
"digression_frequency": [1-10],
"formality_level": [1-10],
"reflection_inclusion": [1-10],
"irony_usage": [1-10],
"neologism_frequency": [1-10],
"ellipsis_usage": [1-10],
"cultural_reference_inclusion": [1-10],
"stream_of_consciousness_usage": [1-10],
"openness_to_experience": [1-10],
"conscientiousness": [1-10],
"extraversion": [1-10],
"agreeableness": [1-10],
"emotional_stability": [1-10],
"dominant_motivations": "[achievement/affiliation/power/etc.]",
"core_values": "[integrity/freedom/knowledge/etc.]",
"decision_making_style": "[analytical/intuitive/spontaneous/etc.]",
"empathy_level": [1-10],
"self_confidence": [1-10],
"risk_taking_tendency": [1-10],
"idealism_vs_realism": "[idealistic/realistic/mixed]",
"conflict_resolution_style": "[assertive/collaborative/avoidant/etc.]",
"relationship_orientation": "[independent/communal/mixed]",
"emotional_response_tendency": "[calm/reactive/intense]",
"creativity_level": [1-10],
"age": "[age or age range]",
"gender": "[gender]",
"education_level": "[highest level of education]",
"professional_background": "[brief description]",
"cultural_background": "[brief description]",
"primary_language": "[language]",
"language_fluency": "[native/fluent/intermediate/beginner]",
"background": "[A brief paragraph describing the author's context, major influences, and any other relevant information not captured above]"
Writing Sample:
{writing_sample}
'''
}
],
"model": "grok-beta",
"stream": False,
"temperature": 0
}
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status() # Raises HTTPError for bad responses
# Log the API response for debugging
logger.debug(f"OpenAI API response: {response.text}")
assistant_message = response.json()['choices'][0]['message']['content'].strip()
logger.debug(f"Assistant message: {assistant_message}")
# Extract JSON from the assistant's message
json_str = re.search(r'\{.*\}', assistant_message, re.DOTALL)
if json_str:
analyzed_data = json.loads(json_str.group())
else:
logger.error("No JSON object found in the response.")
return None
return analyzed_data
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return None
except json.JSONDecodeError as e:
logger.error(f"JSON decoding failed: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error: {e}")
return None
def generate_content(persona_data, prompt):
endpoint = f"{XAI_API_BASE}/chat/completions"
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {XAI_API_KEY}"
}
# Format the persona data into a readable string
characteristics = '\n'.join([
f"{key.replace('_', ' ').capitalize()}: {value}"
for key, value in persona_data.items()
if value is not None and key not in ['id', 'name']
])
decoding_prompt = f'''
You are to write a response in the style of {persona_data.get('name', 'Unknown Author')}, a writer with the following characteristics:
{characteristics}
Now, please write a response in this style about the following topic:
"{prompt}"
Begin with a compelling title that reflects the content of the post.
'''
payload = {
"messages": [
{"role": "system", "content": "You are an assistant that generates blog posts."},
{"role": "user", "content": decoding_prompt}
],
"model": "grok-beta",
"stream": False,
"temperature": 0
}
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status()
assistant_message = response.json()['choices'][0]['message']['content'].strip()
logger.debug(f"Assistant message: {assistant_message}")
return assistant_message
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return ''
except json.JSONDecodeError as e:
logger.error(f"JSON decoding failed: {e}")
return ''
except Exception as e:
logger.error(f"Unexpected error: {e}")
return ''
def save_blog_post(blog_post, title):
# Implement if needed
pass
from django.urls import path
from .views import (
AnalyzeWritingSampleView,
GenerateContentView,
PersonaListView,
PersonaDetailView,
BlogPostView
)
urlpatterns = [
path('analyze/', AnalyzeWritingSampleView.as_view(), name='analyze-writing-sample'),
path('generate-content/', GenerateContentView.as_view(), name='generate-content'),
path('personas/', PersonaListView.as_view(), name='persona-list'),
path('personas/<int:persona_id>/', PersonaDetailView.as_view(), name='persona-detail'),
path('blog-posts/', BlogPostView.as_view(), name='blogpost-list'),
]
from rest_framework import serializers
from .models import Persona, BlogPost
from .utils import analyze_writing_sample
import logging
logger = logging.getLogger(__name__)
class PersonaSerializer(serializers.ModelSerializer):
writing_sample = serializers.CharField(write_only=True)
class Meta:
model = Persona
fields = ['id', 'name', 'writing_sample', 'data']
read_only_fields = ['id', 'data']
def create(self, validated_data):
writing_sample = validated_data.pop('writing_sample')
logger.debug(f"Writing sample received: {writing_sample[:100]}...") # Log first 100 characters
# Analyze the writing sample
analyzed_data = analyze_writing_sample(writing_sample)
logger.debug(f"Analyzed data: {analyzed_data}")
if not analyzed_data:
logger.error("analyze_writing_sample returned empty or invalid data.")
raise serializers.ValidationError({"writing_sample": "Failed to analyze the writing sample."})
# Store the analyzed data
validated_data['data'] = analyzed_data
# Save persona
return Persona.objects.create(**validated_data)
class BlogPostSerializer(serializers.ModelSerializer):
persona = serializers.StringRelatedField() # Displays persona name
class Meta:
model = BlogPost
fields = ['id', 'persona', 'title', 'content', 'created_at']
from django.db import models
class Persona(models.Model):
name = models.CharField(max_length=100, blank=True, null=True)
data = models.JSONField(blank=True, null=True) # Stores analyzed writing sample data
def __str__(self):
return self.name
class BlogPost(models.Model):
id = models.BigAutoField(primary_key=True)
persona = models.ForeignKey(Persona, on_delete=models.CASCADE, related_name='blog_posts')
title = models.CharField(max_length=200, blank=True, null=True)
content = models.TextField()
created_at = models.DateTimeField(auto_now_add=True)
def __str__(self):
return self.title or f"BlogPost {self.id}"
"""
Django settings for backend project.
Generated by 'django-admin startproject' using Django 5.1.2.
For more information on this file, see
https://docs.djangoproject.com/en/5.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/5.1/ref/settings/
"""
# Ensure you have python-decouple installed:
# pip install python-decouple
import decouple
from decouple import config
from pathlib import Path
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/5.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'django-insecure-afq-u-)(^1od2wb_(4oke^ky00%p-0$+o%e6bfw0nucj30$0$r'
XAI_API_KEY = config('XAI_API_KEY')
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'rest_framework',
'core',
'corsheaders',
]
MIDDLEWARE = [
'corsheaders.middleware.CorsMiddleware',
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'backend.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'backend.wsgi.application'
# Database
# https://docs.djangoproject.com/en/5.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/5.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/5.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/5.1/howto/static-files/
STATIC_URL = 'static/'
# Default primary key field type
# https://docs.djangoproject.com/en/5.1/ref/settings/#default-auto-field
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
CORS_ALLOWED_ORIGINS = [
'http://localhost:3000',
'http://localhost:3001',
]
from django.contrib import admin
from django.urls import path, include
urlpatterns = [
path('admin/', admin.site.urls),
path('api/', include('core.urls')), # Prefix API URLs with /api/
]
import React from 'react';
import { BrowserRouter as Router, Routes, Route } from 'react-router-dom';
import UploadSample from './components/UploadSample';
import PersonaList from './components/PersonaList';
import GenerateContent from './components/GenerateContent';
import BlogPosts from './components/BlogPosts';
import NavBar from './components/NavBar';
const App: React.FC = () => {
return (
<Router>
<NavBar />
<div style={{ padding: '20px' }}>
<Routes>
<Route path="/" element={<UploadSample />} />
<Route path="/personas" element={<PersonaList />} />
<Route path="/generate" element={<GenerateContent />} />
<Route path="/blog-posts" element={<BlogPosts />} />
</Routes>
</div>
</Router>
);
};
export default App;
import axios from 'axios';
const instance = axios.create({
baseURL: 'http://localhost:8000/api/', // Adjust the baseURL if needed
});
export default instance;
// src/index.tsx
import React from 'react';
import ReactDOM from 'react-dom/client'; // Updated for React 18
import './index.css';
import App from './App';
const root = ReactDOM.createRoot(
document.getElementById('root') as HTMLElement
);
root.render(
<React.StrictMode>
<App />
</React.StrictMode>
);
import React, { useEffect, useState } from 'react';
import axios from '../axiosConfig'; // Adjust the path if necessary
import { CircularProgress, Typography, Box, Card, CardContent } from '@mui/material';
interface BlogPost {
id: number;
persona: string;
title: string;
content: string;
created_at: string;
}
const BlogPosts: React.FC = () => {
const [blogPosts, setBlogPosts] = useState<BlogPost[]>([]);
const [loading, setLoading] = useState<boolean>(true);
const [error, setError] = useState<string | null>(null);
useEffect(() => {
const fetchBlogPosts = async () => {
try {
const response = await axios.get('blog-posts/');
setBlogPosts(response.data);
} catch (err) {
console.error('Error fetching blog posts:', err);
setError('Failed to load blog posts.');
} finally {
setLoading(false);
}
};
fetchBlogPosts();
}, []);
if (loading) {
return (
<Box display="flex" justifyContent="center" alignItems="center" height="100vh">
<CircularProgress />
</Box>
);
}
if (error) {
return (
<Box display="flex" justifyContent="center" alignItems="center" height="100vh">
<Typography variant="h6" color="error">
{error}
</Typography>
</Box>
);
}
return (
<Box p={4}>
<Typography variant="h4" gutterBottom>
Output
</Typography>
{blogPosts.length === 0 ? (
<Typography variant="body1">No blog posts found.</Typography>
) : (
blogPosts.map((post) => (
<Card key={post.id} variant="outlined" sx={{ mb: 2 }}>
<CardContent>
<Typography variant="h5" gutterBottom>
{post.title || 'Untitled'}
</Typography>
<Typography variant="body2" paragraph>
{post.content}
</Typography>
<Typography variant="caption" color="text.secondary">
By: {post.persona} on {new Date(post.created_at).toLocaleString()}
</Typography>
</CardContent>
</Card>
))
)}
</Box>
);
};
export default BlogPosts;
import React, { useState } from 'react';
import axios from '../axiosConfig'; // Adjust the path if necessary
import { useSearchParams } from 'react-router-dom';
import { Box, Button, TextField, Typography, Alert, CircularProgress, Card, CardContent } from '@mui/material';
interface BlogPost {
id: number;
persona: string;
title: string;
content: string;
created_at: string;
}
const GenerateContent: React.FC = () => {
const [searchParams] = useSearchParams();
const personaIdParam = searchParams.get('personaId');
const personaId = personaIdParam ? Number(personaIdParam) : null;
const [prompt, setPrompt] = useState<string>('');
const [content, setContent] = useState<BlogPost | null>(null);
const [loading, setLoading] = useState<boolean>(false);
const [error, setError] = useState<string | null>(null);
const handleGenerate = async () => {
if (!prompt) {
setError('Please enter a prompt.');
return;
}
if (!personaId) {
setError('Invalid Persona ID.');
return;
}
setLoading(true);
setError(null);
try {
const response = await axios.post('generate-content/', {
persona_id: personaId,
prompt: prompt,
});
setContent(response.data);
setError(null);
setPrompt('');
} catch (err: any) {
console.error('Error generating content:', err);
if (err.response && err.response.data) {
setError(JSON.stringify(err.response.data));
} else {
setError('Failed to generate content.');
}
} finally {
setLoading(false);
}
};
return (
<Box p={4} maxWidth="600px" mx="auto">
<Typography variant="h4" gutterBottom>
Generate Content
</Typography>
{error && (
<Alert severity="error" sx={{ mb: 2 }}>
{error}
</Alert>
)}
<TextField
label="Prompt"
variant="outlined"
fullWidth
multiline
rows={4}
value={prompt}
onChange={(e) => setPrompt(e.target.value)}
placeholder="Enter a topic or prompt..."
required
sx={{ mb: 3 }}
/>
<Button
onClick={handleGenerate}
variant="contained"
color="primary"
disabled={loading}
fullWidth
>
{loading ? <CircularProgress size={24} /> : 'Generate Content'}
</Button>
{content && (
<Card variant="outlined" sx={{ mt: 4 }}>
<CardContent>
<Typography variant="h5" gutterBottom>
{content.title || 'Untitled'}
</Typography>
<Typography variant="body1">
{content.content}
</Typography>
</CardContent>
</Card>
)}
</Box>
);
};
export default GenerateContent;
import { AppBar, Toolbar, Tabs, Tab, Box } from '@mui/material';
import { Link, useLocation, LinkProps } from 'react-router-dom';
import { styled } from '@mui/system';
import { TabProps } from '@mui/material/Tab'; // Import TabProps
const NavBar: React.FC = () => {
const location = useLocation();
const currentPath = location.pathname;
const StyledTab = styled(Tab)<TabProps & LinkProps>(({ theme }) => ({ // Extend with LinkProps
color: '#ffffff',
fontWeight: 'bold',
textTransform: 'none',
'&.Mui-selected': {
color: '#ffffff',
backgroundColor: '#333333',
borderRadius: theme.spacing(0.5),
},
'&:hover': {
color: '#aaaaaa',
},
}));
const StyledAppBar = styled(AppBar)({
backgroundColor: '#000000',
boxShadow: 'none',
});
return (
<StyledAppBar position="static">
<Toolbar>
<Box sx={{ flexGrow: 1 }}>
<Tabs value={currentPath} TabIndicatorProps={{ style: { backgroundColor: '#ffffff' } }}>
<StyledTab label="Upload Sample" value="/" component={Link} to="/" />
<StyledTab label="Personas" value="/personas" component={Link} to="/personas" />
<StyledTab label="Blog Posts" value="/blog-posts" component={Link} to="/blog-posts" />
</Tabs>
</Box>
</Toolbar>
</StyledAppBar>
);
};
export default NavBar;
/* General Container Styling */
.persona-list-container {
max-width: 800px;
margin: 0 auto;
padding: 20px;
background-color: #f9f9f9;
border-radius: 10px;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}
/* Title Styling */
.title {
text-align: center;
font-size: 2rem;
margin-bottom: 20px;
color: #333;
}
/* Loading and Error Messages */
.loading,
.error,
.no-personas {
text-align: center;
font-size: 1.2rem;
color: #666;
}
/* Persona Card Container */
.persona-cards {
display: flex;
flex-wrap: wrap;
gap: 20px;
justify-content: center;
}
/* Persona Card Styling */
.persona-card {
background-color: #fff;
border: 1px solid #ddd;
border-radius: 8px;
padding: 20px;
width: calc(33.33% - 20px);
min-width: 200px;
text-align: center;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
transition: transform 0.2s, box-shadow 0.2s;
}
.persona-card:hover {
transform: translateY(-5px);
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
}
/* Persona Name Styling */
.persona-name {
font-size: 1.5rem;
margin-bottom: 15px;
color: #444;
}
/* Button Styling */
.generate-button {
background-color: #007bff;
color: #fff;
border: none;
padding: 10px 15px;
font-size: 1rem;
border-radius: 5px;
cursor: pointer;
transition: background-color 0.2s;
}
.generate-button:hover {
background-color: #0056b3;
}
import React, { useEffect, useState } from 'react';
import axios from '../axiosConfig'; // Adjust the path if necessary
import { useNavigate } from 'react-router-dom';
import './PersonaList.css'; // Import the CSS file for styling
interface Persona {
id: number;
name: string;
data: Record<string, any>;
}
const PersonaList: React.FC = () => {
const [personas, setPersonas] = useState<Persona[]>([]);
const [loading, setLoading] = useState<boolean>(true);
const [error, setError] = useState<string | null>(null);
const navigate = useNavigate();
useEffect(() => {
const fetchPersonas = async () => {
try {
const response = await axios.get('personas/');
setPersonas(response.data);
} catch (err) {
console.error('Error fetching personas:', err);
setError('Failed to load personas.');
} finally {
setLoading(false);
}
};
fetchPersonas();
}, []);
const handleSelectPersona = (personaId: number) => {
navigate(`/generate?personaId=${personaId}`);
};
if (loading) return <div className="loading">Loading...</div>;
if (error) return <div className="error">{error}</div>;
return (
<div className="persona-list-container">
<h2 className="title">Saved Personas</h2>
{personas.length === 0 ? (
<p className="no-personas">No personas found.</p>
) : (
<div className="persona-cards">
{personas.map((persona) => (
<div key={persona.id} className="persona-card">
<h3 className="persona-name">{persona.name}</h3>
<button
className="generate-button"
onClick={() => handleSelectPersona(persona.id)}
>
Generate Content
</button>
</div>
))}
</div>
)}
</div>
);
};
export default PersonaList;
import React, { useState } from 'react';
import axios from '../axiosConfig'; // Adjust the path if necessary
import { Box, Button, TextField, Typography, Alert, Stack } from '@mui/material';
const UploadSample: React.FC = () => {
const [name, setName] = useState('');
const [writingSample, setWritingSample] = useState('');
const [error, setError] = useState<string | null>(null);
const [success, setSuccess] = useState<string | null>(null);
const handleSubmit = async (event: React.FormEvent) => {
event.preventDefault();
const payload = {
name: name.trim(),
writing_sample: writingSample.trim(),
};
try {
console.log('Payload being sent:', payload);
const response = await axios.post('analyze/', payload);
console.log('Response received:', response.data);
setSuccess(`Persona "${response.data.name}" created successfully!`);
setError(null);
setName('');
setWritingSample('');
} catch (error: any) {
console.error('Error uploading writing sample:', error);
console.log('Error response:', error.response);
if (error.response && error.response.data) {
setError(JSON.stringify(error.response.data));
} else {
setError('An error occurred while uploading the writing sample.');
}
setSuccess(null);
}
};
return (
<Box p={4} maxWidth="600px" mx="auto">
<Typography variant="h4" gutterBottom>
Upload Writing Sample
</Typography>
{error && (
<Alert severity="error" sx={{ mb: 2 }}>
{error}
</Alert>
)}
{success && (
<Alert severity="success" sx={{ mb: 2 }}>
{success}
</Alert>
)}
<form onSubmit={handleSubmit}>
<Stack spacing={3}>
<TextField
label="Persona Name"
variant="outlined"
fullWidth
value={name}
onChange={(e) => setName(e.target.value)}
required
inputProps={{ maxLength: 100 }}
/>
<TextField
label="Writing Sample"
variant="outlined"
fullWidth
multiline
rows={6}
value={writingSample}
onChange={(e) => setWritingSample(e.target.value)}
required
/>
<Button type="submit" variant="contained" color="primary" size="large">
Submit
</Button>
</Stack>
</form>
</Box>
);
};
export default UploadSample;
To adapt your existing Django and React application to use the Anthropic API instead of the XAI API, you'll need to make several changes primarily in your `utils.py`, `settings.py`, and possibly in environment variables. Below is a comprehensive guide to help you transition smoothly.
## Overview of Changes
1. **Update Environment Variables:**
- Replace `XAI_API_KEY` with `ANTHROPIC_API_KEY`.
2. **Modify `settings.py`:**
- Update the API key reference.
3. **Refactor `utils.py`:**
- Change the API endpoint to Anthropic's.
- Update headers and payload structure according to Anthropic's API.
- Use the latest Anthropic model (e.g., `claude-2`).
- Adjust response parsing as per Anthropic's response format.
4. **Ensure Other References Are Updated:**
- Any other files referencing `XAI_API_KEY` should be updated to `ANTHROPIC_API_KEY`.
Let's go through each step in detail.
---
## 1. Update Environment Variables
First, ensure that your environment variables reflect the change from XAI to Anthropic.
### **.env File**
Replace or add the following line in your `.env` file:
```env
ANTHROPIC_API_KEY=your_anthropic_api_key_here
```
> **Note:** Ensure you **do not** include the `XAI_API_KEY` anymore unless it's needed for other purposes.
---
## 2. Modify `settings.py`
Update your Django settings to use the new Anthropic API key.
### **backend/settings.py**
```python
# Remove or comment out the old XAI_API_KEY
# XAI_API_KEY = config('XAI_API_KEY')
# Add the new Anthropic API key
ANTHROPIC_API_KEY = config('ANTHROPIC_API_KEY')
```
> **Security Tip:** Ensure that your `.env` file is **not** committed to version control. Add it to `.gitignore` if necessary.
---
## 3. Refactor `utils.py`
This is the core part of the migration. We'll update the `analyze_writing_sample` and `generate_content` functions to interact with Anthropic's API.
### **core/utils.py**
```python
import logging
import requests
import json
import re
from decouple import config
logger = logging.getLogger(__name__)
# Update the API key and base URL for Anthropic
ANTHROPIC_API_KEY = config('ANTHROPIC_API_KEY')
ANTHROPIC_API_BASE = "https://api.anthropic.com/v1"
# Define the latest Anthropic model
ANTHROPIC_MODEL = "claude-2" # Update this if a newer model is available
def analyze_writing_sample(writing_sample):
endpoint = f"{ANTHROPIC_API_BASE}/complete"
headers = {
"Content-Type": "application/json",
"X-API-Key": ANTHROPIC_API_KEY
}
prompt = f"""
Analyze the following writing sample and provide a detailed assessment of the author's characteristics. Use the template below and return the results in JSON format.
Template:
{{
"name": "[Author/Character Name]",
"vocabulary_complexity": [1-10],
"sentence_structure": "[simple/complex/varied]",
"paragraph_organization": "[structured/loose/stream-of-consciousness]",
"idiom_usage": [1-10],
"metaphor_frequency": [1-10],
"simile_frequency": [1-10],
"tone": "[formal/informal/academic/conversational/etc.]",
"punctuation_style": "[minimal/heavy/unconventional]",
"contraction_usage": [1-10],
"pronoun_preference": "[first-person/third-person/etc.]",
"passive_voice_frequency": [1-10],
"rhetorical_question_usage": [1-10],
"list_usage_tendency": [1-10],
"personal_anecdote_inclusion": [1-10],
"pop_culture_reference_frequency": [1-10],
"technical_jargon_usage": [1-10],
"parenthetical_aside_frequency": [1-10],
"humor_sarcasm_usage": [1-10],
"emotional_expressiveness": [1-10],
"emphatic_device_usage": [1-10],
"quotation_frequency": [1-10],
"analogy_usage": [1-10],
"sensory_detail_inclusion": [1-10],
"onomatopoeia_usage": [1-10],
"alliteration_frequency": [1-10],
"word_length_preference": "[short/long/varied]",
"foreign_phrase_usage": [1-10],
"rhetorical_device_usage": [1-10],
"statistical_data_usage": [1-10],
"personal_opinion_inclusion": [1-10],
"transition_usage": [1-10],
"reader_question_frequency": [1-10],
"imperative_sentence_usage": [1-10],
"dialogue_inclusion": [1-10],
"regional_dialect_usage": [1-10],
"hedging_language_frequency": [1-10],
"language_abstraction": "[concrete/abstract/mixed]",
"personal_belief_inclusion": [1-10],
"repetition_usage": [1-10],
"subordinate_clause_frequency": [1-10],
"verb_type_preference": "[active/stative/mixed]",
"sensory_imagery_usage": [1-10],
"symbolism_usage": [1-10],
"digression_frequency": [1-10],
"formality_level": [1-10],
"reflection_inclusion": [1-10],
"irony_usage": [1-10],
"neologism_frequency": [1-10],
"ellipsis_usage": [1-10],
"cultural_reference_inclusion": [1-10],
"stream_of_consciousness_usage": [1-10],
"openness_to_experience": [1-10],
"conscientiousness": [1-10],
"extraversion": [1-10],
"agreeableness": [1-10],
"emotional_stability": [1-10],
"dominant_motivations": "[achievement/affiliation/power/etc.]",
"core_values": "[integrity/freedom/knowledge/etc.]",
"decision_making_style": "[analytical/intuitive/spontaneous/etc.]",
"empathy_level": [1-10],
"self_confidence": [1-10],
"risk_taking_tendency": [1-10],
"idealism_vs_realism": "[idealistic/realistic/mixed]",
"conflict_resolution_style": "[assertive/collaborative/avoidant/etc.]",
"relationship_orientation": "[independent/communal/mixed]",
"emotional_response_tendency": "[calm/reactive/intense]",
"creativity_level": [1-10],
"age": "[age or age range]",
"gender": "[gender]",
"education_level": "[highest level of education]",
"professional_background": "[brief description]",
"cultural_background": "[brief description]",
"primary_language": "[language]",
"language_fluency": "[native/fluent/intermediate/beginner]",
"background": "[A brief paragraph describing the author's context, major influences, and any other relevant information not captured above]"
}}
Writing Sample:
{writing_sample}
"""
payload = {
"prompt": prompt,
"model": ANTHROPIC_MODEL,
"max_tokens_to_sample": 1500, # Adjust as needed
"temperature": 0.0, # Deterministic output
"stop_sequences": ["\n\n"]
}
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status() # Raises HTTPError for bad responses
# Log the API response for debugging
logger.debug(f"Anthropic API response: {response.text}")
assistant_message = response.json()['completion'].strip()
logger.debug(f"Assistant message: {assistant_message}")
# Extract JSON from the assistant's message
json_str = re.search(r'\{.*\}', assistant_message, re.DOTALL)
if json_str:
analyzed_data = json.loads(json_str.group())
else:
logger.error("No JSON object found in the response.")
return None
return analyzed_data
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return None
except json.JSONDecodeError as e:
logger.error(f"JSON decoding failed: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error: {e}")
return None
def generate_content(persona_data, prompt):
endpoint = f"{ANTHROPIC_API_BASE}/complete"
headers = {
"Content-Type": "application/json",
"X-API-Key": ANTHROPIC_API_KEY
}
# Format the persona data into a readable string
characteristics = '\n'.join([
f"{key.replace('_', ' ').capitalize()}: {value}"
for key, value in persona_data.items()
if value is not None and key not in ['id', 'name']
])
decoding_prompt = f"""
You are to write a blog post in the style of {persona_data.get('name', 'Unknown Author')}, a writer with the following characteristics:
{characteristics}
Now, please write a blog post about the following topic:
"{prompt}"
Begin with a compelling title that reflects the content of the post.
"""
payload = {
"prompt": decoding_prompt,
"model": ANTHROPIC_MODEL,
"max_tokens_to_sample": 1500, # Adjust as needed
"temperature": 0.7, # Adjust for creativity
"stop_sequences": ["\n\n"]
}
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status()
assistant_message = response.json()['completion'].strip()
logger.debug(f"Assistant message: {assistant_message}")
return assistant_message
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return ''
except json.JSONDecodeError as e:
logger.error(f"JSON decoding failed: {e}")
return ''
except Exception as e:
logger.error(f"Unexpected error: {e}")
return ''
```
### **Key Changes Explained:**
1. **API Endpoint and Headers:**
- Changed the base URL to `https://api.anthropic.com/v1`.
- Updated the `Authorization` header to use `X-API-Key` as per Anthropic's API.
2. **Model Selection:**
- Set `ANTHROPIC_MODEL` to `"claude-2"`. Ensure to use the latest available model from Anthropic.
3. **Payload Structure:**
- Anthropic's API uses a `prompt` field instead of `messages`.
- Added `max_tokens_to_sample` to control the response length.
- Used `temperature` to control creativity (0 for deterministic, higher for more creative responses).
- Defined `stop_sequences` to indicate where the AI should stop generating text.
4. **Response Parsing:**
- Anthropic returns the completion in the `completion` field instead of `choices`.
- Adjusted the JSON extraction accordingly.
5. **Error Handling:**
- Maintained robust error handling to catch and log various exceptions.
6. **Functionality:**
- The logic remains the same: analyzing writing samples and generating content based on persona data.
---
## 4. Update Any Other References to `XAI_API_KEY`
Ensure that any other part of your codebase that references `XAI_API_KEY` is updated to `ANTHROPIC_API_KEY`. From the provided code, `utils.py` seems to be the primary consumer, but double-check other modules if necessary.
For example, in your serializers or views, if there's any direct reference to `XAI_API_KEY`, update them accordingly. Based on your provided code, no other files directly reference the API key, but it's good to verify.
---
## 5. Verify and Test Your Changes
After making these changes, it's crucial to test the application thoroughly to ensure everything works as expected.
### **Steps to Test:**
1. **Migrate Environment Variables:**
- Ensure your `.env` file contains the `ANTHROPIC_API_KEY`.
- Remove or comment out the `XAI_API_KEY` if it's no longer needed.
2. **Install Any Required Dependencies:**
- If you haven't already, ensure that `python-decouple` is installed.
- Verify other dependencies are up to date.
```bash
pip install python-decouple
```
3. **Run Migrations:**
- Apply any pending migrations to ensure your database is in sync.
```bash
python manage.py migrate
```
4. **Start the Development Server:**
- Run your Django development server.
```bash
python manage.py runserver
```
5. **Test API Endpoints:**
- Use tools like Postman or cURL to test the `/api/analyze/` and `/api/generate-content/` endpoints.
- Ensure that writing samples are analyzed correctly and content is generated as expected.
6. **Test Frontend Functionality:**
- Navigate through your React application to upload writing samples, view personas, and generate blog posts.
- Check for any errors in the browser console or network requests.
7. **Monitor Logs:**
- Keep an eye on the server logs to identify any issues or errors during the API calls.
---
## Additional Recommendations
1. **Rate Limiting and Usage Monitoring:**
- Anthropic may have different rate limits and pricing compared to XAI. Monitor your usage to avoid unexpected costs.
2. **Caching Responses:**
- To optimize performance and reduce API calls, consider caching frequently requested analyses or content generation results.
3. **Enhance Error Messages:**
- Improve user feedback by providing more descriptive error messages based on different failure scenarios.
4. **Security Enhancements:**
- Ensure that your API keys are securely stored and accessed. Consider using more secure methods if deploying to production.
5. **Documentation:**
- Update your project's documentation to reflect the changes made during this migration.
---
## Complete Updated `utils.py`
For clarity, here's the complete updated `utils.py` file incorporating all the changes discussed:
```python
import logging
import requests
import json
import re
from decouple import config
logger = logging.getLogger(__name__)
# Update the API key and base URL for Anthropic
ANTHROPIC_API_KEY = config('ANTHROPIC_API_KEY')
ANTHROPIC_API_BASE = "https://api.anthropic.com/v1"
# Define the latest Anthropic model
ANTHROPIC_MODEL = "claude-2" # Update this if a newer model is available
def analyze_writing_sample(writing_sample):
endpoint = f"{ANTHROPIC_API_BASE}/complete"
headers = {
"Content-Type": "application/json",
"X-API-Key": ANTHROPIC_API_KEY
}
prompt = f"""
Analyze the following writing sample and provide a detailed assessment of the author's characteristics. Use the template below and return the results in JSON format.
Template:
{{
"name": "[Author/Character Name]",
"vocabulary_complexity": [1-10],
"sentence_structure": "[simple/complex/varied]",
"paragraph_organization": "[structured/loose/stream-of-consciousness]",
"idiom_usage": [1-10],
"metaphor_frequency": [1-10],
"simile_frequency": [1-10],
"tone": "[formal/informal/academic/conversational/etc.]",
"punctuation_style": "[minimal/heavy/unconventional]",
"contraction_usage": [1-10],
"pronoun_preference": "[first-person/third-person/etc.]",
"passive_voice_frequency": [1-10],
"rhetorical_question_usage": [1-10],
"list_usage_tendency": [1-10],
"personal_anecdote_inclusion": [1-10],
"pop_culture_reference_frequency": [1-10],
"technical_jargon_usage": [1-10],
"parenthetical_aside_frequency": [1-10],
"humor_sarcasm_usage": [1-10],
"emotional_expressiveness": [1-10],
"emphatic_device_usage": [1-10],
"quotation_frequency": [1-10],
"analogy_usage": [1-10],
"sensory_detail_inclusion": [1-10],
"onomatopoeia_usage": [1-10],
"alliteration_frequency": [1-10],
"word_length_preference": "[short/long/varied]",
"foreign_phrase_usage": [1-10],
"rhetorical_device_usage": [1-10],
"statistical_data_usage": [1-10],
"personal_opinion_inclusion": [1-10],
"transition_usage": [1-10],
"reader_question_frequency": [1-10],
"imperative_sentence_usage": [1-10],
"dialogue_inclusion": [1-10],
"regional_dialect_usage": [1-10],
"hedging_language_frequency": [1-10],
"language_abstraction": "[concrete/abstract/mixed]",
"personal_belief_inclusion": [1-10],
"repetition_usage": [1-10],
"subordinate_clause_frequency": [1-10],
"verb_type_preference": "[active/stative/mixed]",
"sensory_imagery_usage": [1-10],
"symbolism_usage": [1-10],
"digression_frequency": [1-10],
"formality_level": [1-10],
"reflection_inclusion": [1-10],
"irony_usage": [1-10],
"neologism_frequency": [1-10],
"ellipsis_usage": [1-10],
"cultural_reference_inclusion": [1-10],
"stream_of_consciousness_usage": [1-10],
"openness_to_experience": [1-10],
"conscientiousness": [1-10],
"extraversion": [1-10],
"agreeableness": [1-10],
"emotional_stability": [1-10],
"dominant_motivations": "[achievement/affiliation/power/etc.]",
"core_values": "[integrity/freedom/knowledge/etc.]",
"decision_making_style": "[analytical/intuitive/spontaneous/etc.]",
"empathy_level": [1-10],
"self_confidence": [1-10],
"risk_taking_tendency": [1-10],
"idealism_vs_realism": "[idealistic/realistic/mixed]",
"conflict_resolution_style": "[assertive/collaborative/avoidant/etc.]",
"relationship_orientation": "[independent/communal/mixed]",
"emotional_response_tendency": "[calm/reactive/intense]",
"creativity_level": [1-10],
"age": "[age or age range]",
"gender": "[gender]",
"education_level": "[highest level of education]",
"professional_background": "[brief description]",
"cultural_background": "[brief description]",
"primary_language": "[language]",
"language_fluency": "[native/fluent/intermediate/beginner]",
"background": "[A brief paragraph describing the author's context, major influences, and any other relevant information not captured above]"
}}
Writing Sample:
{writing_sample}
"""
payload = {
"prompt": prompt,
"model": ANTHROPIC_MODEL,
"max_tokens_to_sample": 1500, # Adjust as needed
"temperature": 0.0, # Deterministic output
"stop_sequences": ["\n\n"]
}
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status() # Raises HTTPError for bad responses
# Log the API response for debugging
logger.debug(f"Anthropic API response: {response.text}")
assistant_message = response.json()['completion'].strip()
logger.debug(f"Assistant message: {assistant_message}")
# Extract JSON from the assistant's message
json_str = re.search(r'\{.*\}', assistant_message, re.DOTALL)
if json_str:
analyzed_data = json.loads(json_str.group())
else:
logger.error("No JSON object found in the response.")
return None
return analyzed_data
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return None
except json.JSONDecodeError as e:
logger.error(f"JSON decoding failed: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error: {e}")
return None
def generate_content(persona_data, prompt):
endpoint = f"{ANTHROPIC_API_BASE}/complete"
headers = {
"Content-Type": "application/json",
"X-API-Key": ANTHROPIC_API_KEY
}
# Format the persona data into a readable string
characteristics = '\n'.join([
f"{key.replace('_', ' ').capitalize()}: {value}"
for key, value in persona_data.items()
if value is not None and key not in ['id', 'name']
])
decoding_prompt = f"""
You are to write a blog post in the style of {persona_data.get('name', 'Unknown Author')}, a writer with the following characteristics:
{characteristics}
Now, please write a blog post about the following topic:
"{prompt}"
Begin with a compelling title that reflects the content of the post.
"""
payload = {
"prompt": decoding_prompt,
"model": ANTHROPIC_MODEL,
"max_tokens_to_sample": 1500, # Adjust as needed
"temperature": 0.7, # Adjust for creativity
"stop_sequences": ["\n\n"]
}
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status()
assistant_message = response.json()['completion'].strip()
logger.debug(f"Assistant message: {assistant_message}")
return assistant_message
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return ''
except json.JSONDecodeError as e:
logger.error(f"JSON decoding failed: {e}")
return ''
except Exception as e:
logger.error(f"Unexpected error: {e}")
return ''
```
---
## Conclusion
By following the steps outlined above, you should be able to successfully transition your application from using the XAI API to the Anthropic API. Ensure thorough testing to validate that the new integration works seamlessly and meets your application's requirements.
If you encounter any issues or have further questions, feel free to ask!
o1-mini
Teflon Content Script injected
contentscript.bundle.js:15045 Logger init
chunk-NUMECXU6.js?v=5abf39a3:21551 Download the React DevTools for a better development experience: https://reactjs.org/link/react-devtools
page_all.js:2 init command html
page_all.js:2 init command http://localhost:5173/
page_all.js:2 init command clearCart
page_all.js:2 init command http://localhost:5173/
page_all.js:2 init command getOrdersListProduct
page_all.js:2 init command http://localhost:5173/
page_all.js:2 init command getTrackDetals
page_all.js:2 init command http://localhost:5173/
page_all.js:2 init command getOrderDetail
page_all.js:2 init command http://localhost:5173/
page_all.js:2 init command getReview
page_all.js:2 init command http://localhost:5173/
page_all.js:2 init command clearAddres
page_all.js:2 init command http://localhost:5173/
page_all.js:2 init command countAddress
page_all.js:2 init command http://localhost:5173/
page_all.js:2 add class expansion-alids-init
UploadSample.tsx:22 Payload being sent:
{name: 'Karamazov 2', writing_sample: 'rom active punishment herself, as the sinner is to…is heart. Tears positively\nglittered in his eyes.'}
UploadSample.tsx:23
POST http://localhost:8000/api/personas/ 400 (Bad Request)
dispatchXhrRequest @ axios.js?v=5abf39a3:1680
xhr @ axios.js?v=5abf39a3:1560
dispatchRequest @ axios.js?v=5abf39a3:2035
Promise.then
_request @ axios.js?v=5abf39a3:2222
request @ axios.js?v=5abf39a3:2141
httpMethod @ axios.js?v=5abf39a3:2269
wrap @ axios.js?v=5abf39a3:8
handleSubmit @ UploadSample.tsx:23
callCallback2 @ chunk-NUMECXU6.js?v=5abf39a3:3674
invokeGuardedCallbackDev @ chunk-NUMECXU6.js?v=5abf39a3:3699
invokeGuardedCallback @ chunk-NUMECXU6.js?v=5abf39a3:3733
invokeGuardedCallbackAndCatchFirstError @ chunk-NUMECXU6.js?v=5abf39a3:3736
executeDispatch @ chunk-NUMECXU6.js?v=5abf39a3:7014
processDispatchQueueItemsInOrder @ chunk-NUMECXU6.js?v=5abf39a3:7034
processDispatchQueue @ chunk-NUMECXU6.js?v=5abf39a3:7043
dispatchEventsForPlugins @ chunk-NUMECXU6.js?v=5abf39a3:7051
(anonymous) @ chunk-NUMECXU6.js?v=5abf39a3:7174
batchedUpdates$1 @ chunk-NUMECXU6.js?v=5abf39a3:18913
batchedUpdates @ chunk-NUMECXU6.js?v=5abf39a3:3579
dispatchEventForPluginEventSystem @ chunk-NUMECXU6.js?v=5abf39a3:7173
dispatchEventWithEnableCapturePhaseSelectiveHydrationWithoutDiscreteEventReplay @ chunk-NUMECXU6.js?v=5abf39a3:5478
dispatchEvent @ chunk-NUMECXU6.js?v=5abf39a3:5472
dispatchDiscreteEvent @ chunk-NUMECXU6.js?v=5abf39a3:5449
Show less
UploadSample.tsx:30 Error uploading writing sample:
AxiosError {message: 'Request failed with status code 400', name: 'AxiosError', code: 'ERR_BAD_REQUEST', config: {…}, request: XMLHttpRequest, …}
code
:
"ERR_BAD_REQUEST"
config
:
adapter
:
Array(3)
0
:
"xhr"
1
:
"http"
2
:
"fetch"
length
:
3
[[Prototype]]
:
Array(0)
baseURL
:
"http://localhost:8000/api/"
data
:
"{\"name\":\"Karamazov 2\",\"writing_sample\":\"ro
env
:
{FormData: ƒ, Blob: ƒ}
headers
:
AxiosHeaders {Accept: 'application/json, text/plain, */*', Content-Type: 'application/json', Authorization: 'Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ0b2…I6MX0.dt3YGw1yjd-2rEjoKGY-86_0j3BiF8aF5sYfVMTGDEE'}
maxBodyLength
:
-1
maxContentLength
:
-1
method
:
"post"
timeout
:
0
transformRequest
:
[ƒ]
transformResponse
:
[ƒ]
transitional
:
{silentJSONParsing: true, forcedJSONParsing: true, clarifyTimeoutError: false}
url
:
"personas/"
validateStatus
:
ƒ validateStatus(status)
xsrfCookieName
:
"XSRF-TOKEN"
xsrfHeaderName
:
"X-XSRF-TOKEN"
[[Prototype]]
:
Object
message
:
"Request failed with status code 400"
name
:
"AxiosError"
request
:
XMLHttpRequest {onreadystatechange: null, readyState: 4, timeout: 0, withCredentials: false, upload: XMLHttpRequestUpload, …}
response
:
{data: {…}, status: 400, statusText: 'Bad Request', headers: AxiosHeaders, config: {…}, …}
status
:
400
stack
:
"AxiosError: Request failed with status code 400\n at settle (http://localhost:5173/node_modules/.vite/deps/axios.js?v=5abf39a3:1230:12)\n at XMLHttpRequest.onloadend (http://localhost:5173/node_modules/.vite/deps/axios.js?v=5abf39a3:1593:7)\n at Axios.request (http://localhost:5173/node_modules/.vite/deps/axios.js?v=5abf39a3:2145:41)\n at async handleSubmit (http://localhost:5173/src/components/GhostWriter/UploadSample.tsx:35:24)"
[[Prototype]]
:
Error
handleSubmit @ UploadSample.tsx:30
await in handleSubmit
callCallback2 @ chunk-NUMECXU6.js?v=5abf39a3:3674
invokeGuardedCallbackDev @ chunk-NUMECXU6.js?v=5abf39a3:3699
invokeGuardedCallback @ chunk-NUMECXU6.js?v=5abf39a3:3733
invokeGuardedCallbackAndCatchFirstError @ chunk-NUMECXU6.js?v=5abf39a3:3736
executeDispatch @ chunk-NUMECXU6.js?v=5abf39a3:7014
processDispatchQueueItemsInOrder @ chunk-NUMECXU6.js?v=5abf39a3:7034
processDispatchQueue @ chunk-NUMECXU6.js?v=5abf39a3:7043
dispatchEventsForPlugins @ chunk-NUMECXU6.js?v=5abf39a3:7051
(anonymous) @ chunk-NUMECXU6.js?v=5abf39a3:7174
batchedUpdates$1 @ chunk-NUMECXU6.js?v=5abf39a3:18913
batchedUpdates @ chunk-NUMECXU6.js?v=5abf39a3:3579
dispatchEventForPluginEventSystem @ chunk-NUMECXU6.js?v=5abf39a3:7173
dispatchEventWithEnableCapturePhaseSelectiveHydrationWithoutDiscreteEventReplay @ chunk-NUMECXU6.js?v=5abf39a3:5478
dispatchEvent @ chunk-NUMECXU6.js?v=5abf39a3:5472
dispatchDiscreteEvent @ chunk-NUMECXU6.js?v=5abf39a3:5449
Show less
UploadSample.tsx:31 Error response:
{data: {…}, status: 400, statusText: 'Bad Request', headers: AxiosHeaders, config: {…}, …}
config
:
{transitional: {…}, adapter: Array(3), transformRequest: Array(1), transformResponse: Array(1), timeout: 0, …}
data
:
{writing_sample: 'Failed to analyze the writing sample.'}
headers
:
AxiosHeaders {content-length: '58', content-type: 'application/json'}
request
:
XMLHttpRequest
onabort
:
ƒ handleAbort()
onerror
:
ƒ handleError()
onload
:
null
onloadend
:
ƒ onloadend()
onloadstart
:
null
onprogress
:
null
onreadystatechange
:
null
ontimeout
:
ƒ handleTimeout()
readyState
:
4
response
:
"{\"writing_sample\":\"Failed to analyze the writing sample.\"}"
responseText
:
"{\"writing_sample\":\"Failed to analyze the writing sample.\"}"
responseType
:
""
responseURL
:
"http://localhost:8000/api/personas/"
responseXML
:
null
status
:
400
statusText
:
"Bad Request"
timeout
:
0
upload
:
XMLHttpRequestUpload {onloadstart: null, onprogress: null, onabort: null, onerror: null, onload: null, …}
withCredentials
:
false
[[Prototype]]
:
XMLHttpRequest
status
:
400
statusText
:
"Bad Request"
[[Prototype]]
:
Object
The `400 Bad Request` error you're encountering indicates that the server couldn't process the request due to client-side issues. In your case, the response `{ "writing_sample": "Failed to analyze the writing sample." }` suggests that the `analyze_writing_sample` function returned `None`, leading to a validation error in the serializer.
To effectively troubleshoot and resolve this issue, follow the steps below:
## 1. **Enhance Server-Side Logging**
To pinpoint the exact cause of the failure in the `analyze_writing_sample` function, it's crucial to add more detailed logging within the `utils.py` file. This will help you understand whether the request to the Anthropic API is failing and why.
### **Update `utils.py` with Detailed Logging**
Modify your `analyze_writing_sample` and `generate_content` functions to include more comprehensive logging. Here's how you can do it:
```python
import logging
import requests
import json
import re
from decouple import config
logger = logging.getLogger(__name__)
# Update the API key and base URL for Anthropic
ANTHROPIC_API_KEY = config('ANTHROPIC_API_KEY')
ANTHROPIC_API_BASE = "https://api.anthropic.com/v1"
# Define the latest Anthropic model
ANTHROPIC_MODEL = "claude-2" # Ensure this is the latest model available
def analyze_writing_sample(writing_sample):
endpoint = f"{ANTHROPIC_API_BASE}/complete"
headers = {
"Content-Type": "application/json",
"X-API-Key": ANTHROPIC_API_KEY
}
prompt = f"""
Analyze the following writing sample and provide a detailed assessment of the author's characteristics. Use the template below and return the results in JSON format.
Template:
{{
"name": "[Author/Character Name]",
"vocabulary_complexity": [1-10],
"sentence_structure": "[simple/complex/varied]",
"paragraph_organization": "[structured/loose/stream-of-consciousness]",
"idiom_usage": [1-10],
"metaphor_frequency": [1-10],
"simile_frequency": [1-10],
"tone": "[formal/informal/academic/conversational/etc.]",
"punctuation_style": "[minimal/heavy/unconventional]",
"contraction_usage": [1-10],
"pronoun_preference": "[first-person/third-person/etc.]",
"passive_voice_frequency": [1-10],
"rhetorical_question_usage": [1-10],
"list_usage_tendency": [1-10],
"personal_anecdote_inclusion": [1-10],
"pop_culture_reference_frequency": [1-10],
"technical_jargon_usage": [1-10],
"parenthetical_aside_frequency": [1-10],
"humor_sarcasm_usage": [1-10],
"emotional_expressiveness": [1-10],
"emphatic_device_usage": [1-10],
"quotation_frequency": [1-10],
"analogy_usage": [1-10],
"sensory_detail_inclusion": [1-10],
"onomatopoeia_usage": [1-10],
"alliteration_frequency": [1-10],
"word_length_preference": "[short/long/varied]",
"foreign_phrase_usage": [1-10],
"rhetorical_device_usage": [1-10],
"statistical_data_usage": [1-10],
"personal_opinion_inclusion": [1-10],
"transition_usage": [1-10],
"reader_question_frequency": [1-10],
"imperative_sentence_usage": [1-10],
"dialogue_inclusion": [1-10],
"regional_dialect_usage": [1-10],
"hedging_language_frequency": [1-10],
"language_abstraction": "[concrete/abstract/mixed]",
"personal_belief_inclusion": [1-10],
"repetition_usage": [1-10],
"subordinate_clause_frequency": [1-10],
"verb_type_preference": "[active/stative/mixed]",
"sensory_imagery_usage": [1-10],
"symbolism_usage": [1-10],
"digression_frequency": [1-10],
"formality_level": [1-10],
"reflection_inclusion": [1-10],
"irony_usage": [1-10],
"neologism_frequency": [1-10],
"ellipsis_usage": [1-10],
"cultural_reference_inclusion": [1-10],
"stream_of_consciousness_usage": [1-10],
"openness_to_experience": [1-10],
"conscientiousness": [1-10],
"extraversion": [1-10],
"agreeableness": [1-10],
"emotional_stability": [1-10],
"dominant_motivations": "[achievement/affiliation/power/etc.]",
"core_values": "[integrity/freedom/knowledge/etc.]",
"decision_making_style": "[analytical/intuitive/spontaneous/etc.]",
"empathy_level": [1-10],
"self_confidence": [1-10],
"risk_taking_tendency": [1-10],
"idealism_vs_realism": "[idealistic/realistic/mixed]",
"conflict_resolution_style": "[assertive/collaborative/avoidant/etc.]",
"relationship_orientation": "[independent/communal/mixed]",
"emotional_response_tendency": "[calm/reactive/intense]",
"creativity_level": [1-10],
"age": "[age or age range]",
"gender": "[gender]",
"education_level": "[highest level of education]",
"professional_background": "[brief description]",
"cultural_background": "[brief description]",
"primary_language": "[language]",
"language_fluency": "[native/fluent/intermediate/beginner]",
"background": "[A brief paragraph describing the author's context, major influences, and any other relevant information not captured above]"
}}
Writing Sample:
{writing_sample}
"""
payload = {
"prompt": prompt,
"model": ANTHROPIC_MODEL,
"max_tokens_to_sample": 1500, # Adjust as needed
"temperature": 0.0, # Deterministic output
"stop_sequences": ["\n\n"]
}
logger.debug(f"Sending request to Anthropic API: {payload}")
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status() # Raises HTTPError for bad responses
# Log the API response for debugging
logger.debug(f"Anthropic API response: {response.text}")
assistant_message = response.json().get('completion', '').strip()
if not assistant_message:
logger.error("No 'completion' field found in the Anthropic API response.")
return None
logger.debug(f"Assistant message: {assistant_message}")
# Extract JSON from the assistant's message
json_str = re.search(r'\{.*\}', assistant_message, re.DOTALL)
if json_str:
try:
analyzed_data = json.loads(json_str.group())
logger.debug(f"Extracted JSON data: {analyzed_data}")
return analyzed_data
except json.JSONDecodeError as json_err:
logger.error(f"JSON decoding failed: {json_err}")
return None
else:
logger.error("No JSON object found in the response.")
return None
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error: {e}")
return None
def generate_content(persona_data, prompt):
endpoint = f"{ANTHROPIC_API_BASE}/complete"
headers = {
"Content-Type": "application/json",
"X-API-Key": ANTHROPIC_API_KEY
}
# Format the persona data into a readable string
characteristics = '\n'.join([
f"{key.replace('_', ' ').capitalize()}: {value}"
for key, value in persona_data.items()
if value is not None and key not in ['id', 'name']
])
decoding_prompt = f"""
You are to write a blog post in the style of {persona_data.get('name', 'Unknown Author')}, a writer with the following characteristics:
{characteristics}
Now, please write a blog post about the following topic:
"{prompt}"
Begin with a compelling title that reflects the content of the post.
"""
payload = {
"prompt": decoding_prompt,
"model": ANTHROPIC_MODEL,
"max_tokens_to_sample": 1500, # Adjust as needed
"temperature": 0.7, # Adjust for creativity
"stop_sequences": ["\n\n"]
}
logger.debug(f"Sending request to Anthropic API for content generation: {payload}")
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status()
assistant_message = response.json().get('completion', '').strip()
if not assistant_message:
logger.error("No 'completion' field found in the Anthropic API response.")
return ''
logger.debug(f"Assistant message: {assistant_message}")
return assistant_message
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return ''
except Exception as e:
logger.error(f"Unexpected error: {e}")
return ''
```
### **Explanation of Enhancements:**
1. **Detailed Logging Before and After API Calls:**
- Logs the entire payload being sent to the Anthropic API.
- Logs the full response received from the API.
- Logs the extracted assistant message and the parsed JSON data.
2. **Robust Error Handling:**
- Checks if the `'completion'` field exists in the response.
- Catches and logs JSON decoding errors separately.
- Logs unexpected errors to aid in debugging.
3. **Ensuring JSON Extraction:**
- Uses `re.search` to find a JSON object within the assistant's message.
- Attempts to parse the JSON and logs the result or any decoding errors.
## 2. **Verify Environment Variables**
Ensure that the `ANTHROPIC_API_KEY` is correctly set in your `.env` file and that Django is loading it properly.
### **Steps:**
1. **Check `.env` File:**
Ensure your `.env` file contains the correct Anthropic API key:
```env
ANTHROPIC_API_KEY=your_anthropic_api_key_here
```
2. **Update `settings.py`:**
Confirm that your `settings.py` correctly references the new API key:
```python
# backend/settings.py
from decouple import config
ANTHROPIC_API_KEY = config('ANTHROPIC_API_KEY')
```
3. **Restart the Server:**
After making changes to environment variables, restart your Django server to apply the changes.
```bash
python manage.py runserver
```
## 3. **Test Anthropic API Independently**
Before integrating with your application, test the Anthropic API using a tool like **Postman** or **cURL** to ensure that your API key and requests are functioning correctly.
### **Using cURL:**
```bash
curl -X POST https://api.anthropic.com/v1/complete \
-H "Content-Type: application/json" \
-H "X-API-Key: your_anthropic_api_key_here" \
-d '{
"prompt": "Analyze the following writing sample and provide a detailed assessment of the author\'s characteristics. Return the results in JSON format.\n\nWriting Sample:\nShe was lost in thought, her mind wandering through memories long forgotten.",
"model": "claude-2",
"max_tokens_to_sample": 500,
"temperature": 0.0,
"stop_sequences": ["\n\n"]
}'
```
### **Expected Response:**
Anthropic should return a JSON object containing the `completion` field with the analysis. If you receive a valid response, it indicates that your API key and request format are correct.
### **Troubleshooting:**
- **Invalid API Key:** Ensure that your API key is correct and has the necessary permissions.
- **Model Availability:** Verify that the model you're using (`claude-2`) is available and correctly spelled.
- **Rate Limits:** Ensure you're not exceeding any rate limits imposed by Anthropic.
## 4. **Review and Adjust the Prompt**
The quality and structure of the prompt play a significant role in how well the AI can process and respond. Ensure that the prompt is clear, concise, and formatted correctly.
### **Suggestions:**
1. **Simplify the Prompt:**
Start with a simpler prompt to test the functionality and gradually add complexity.
```python
prompt = f"""
Analyze the following writing sample and provide a detailed assessment of the author's characteristics. Return the results in JSON format.
Writing Sample:
{writing_sample}
"""
```
2. **Validate JSON Structure:**
Ensure that the expected JSON structure is clearly defined and easy to parse.
3. **Include Clear Instructions:**
Make sure the AI understands that it should strictly follow the JSON format without additional text.
```python
prompt = f"""
Analyze the following writing sample and provide a detailed assessment of the author's characteristics. Use the exact JSON template provided below without adding any additional text.
Template:
{{
"name": "[Author/Character Name]",
"vocabulary_complexity": [1-10],
"sentence_structure": "[simple/complex/varied]",
...
}}
Writing Sample:
{writing_sample}
"""
```
4. **Limit the Scope:**
If the prompt is too extensive, consider breaking it down or simplifying to ensure the AI can handle it effectively.
## 5. **Inspect Server Logs**
With enhanced logging in place, reproduce the error by submitting a writing sample. Then, inspect the server logs to identify where the failure is occurring.
### **Steps:**
1. **Submit a Writing Sample:**
Use your React frontend to submit a writing sample as you did previously.
2. **Check Django Server Logs:**
Look for detailed logs corresponding to the `analyze_writing_sample` function. Key points to inspect:
- **Request Payload:** Ensure the prompt sent to Anthropic is correctly formatted.
- **API Response:** Check if Anthropic is returning a valid response or an error message.
- **JSON Parsing:** Verify if the JSON extraction is successful.
### **Example Log Entries:**
```plaintext
DEBUG:core.utils:Sending request to Anthropic API: {'prompt': '...', 'model': 'claude-2', ...}
DEBUG:core.utils:Anthropic API response: {...}
DEBUG:core.utils:Assistant message: {...}
DEBUG:core.utils:Extracted JSON data: {...}
```
If the logs show errors like "No JSON object found in the response" or "JSON decoding failed," it indicates issues with the response format or the content returned by Anthropic.
## 6. **Ensure Correct Serializer Configuration**
Verify that your `PersonaSerializer` correctly handles the `data` field, which stores the analyzed writing sample data.
### **Check `serializers.py`:**
```python
from rest_framework import serializers
from .models import Persona, BlogPost
from .utils import analyze_writing_sample
import logging
logger = logging.getLogger(__name__)
class PersonaSerializer(serializers.ModelSerializer):
writing_sample = serializers.CharField(write_only=True)
class Meta:
model = Persona
fields = ['id', 'name', 'writing_sample', 'data']
read_only_fields = ['id', 'data']
def create(self, validated_data):
writing_sample = validated_data.pop('writing_sample')
logger.debug(f"Writing sample received: {writing_sample[:100]}...") # Log first 100 characters
# Analyze the writing sample
analyzed_data = analyze_writing_sample(writing_sample)
logger.debug(f"Analyzed data: {analyzed_data}")
if not analyzed_data:
logger.error("analyze_writing_sample returned empty or invalid data.")
raise serializers.ValidationError({"writing_sample": "Failed to analyze the writing sample."})
# Store the analyzed data
validated_data['data'] = analyzed_data
# Save persona
return Persona.objects.create(**validated_data)
```
### **Ensure:**
- **`data` Field in Model:** The `data` field in the `Persona` model should accept JSON data.
```python
from django.db import models
class Persona(models.Model):
name = models.CharField(max_length=100, blank=True, null=True)
data = models.JSONField(blank=True, null=True) # Ensure this field is correctly defined
def __str__(self):
return self.name
```
- **Handling of `analyzed_data`:** Ensure that `analyzed_data` is a dictionary containing the expected fields as per the template.
## 7. **Validate Model and API Integration**
Ensure that the model and API integration are correctly aligned with Anthropic's specifications.
### **Confirm the Following:**
1. **Model Availability:**
Verify that the model `claude-2` is available and correctly named. If Anthropic has released a newer model, update accordingly.
2. **Endpoint Correctness:**
Ensure that the endpoint `https://api.anthropic.com/v1/complete` is correct. Refer to [Anthropic's API Documentation](https://docs.anthropic.com/en/home) to confirm.
3. **API Key Permissions:**
Ensure that your Anthropic API key has the necessary permissions to access the `complete` endpoint.
4. **Payload Parameters:**
Ensure that all required parameters are included in the payload and adhere to Anthropic's API requirements.
## 8. **Sample Testing Code**
To further assist you, here's a sample script you can run independently to test the `analyze_writing_sample` function. This can help isolate whether the issue is with the API integration or elsewhere in your application.
### **Create a Test Script (`test_anthropic.py`):**
```python
import logging
from decouple import config
from core.utils import analyze_writing_sample
# Configure logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
def main():
# Example writing sample
writing_sample = "She was lost in thought, her mind wandering through memories long forgotten."
# Analyze the writing sample
analyzed_data = analyze_writing_sample(writing_sample)
if analyzed_data:
logger.info("Analysis Successful:")
logger.info(analyzed_data)
else:
logger.error("Analysis Failed.")
if __name__ == "__main__":
main()
```
### **Run the Test Script:**
```bash
python test_anthropic.py
```
### **Expected Outcome:**
- **Success:** You should see detailed logs showing the request payload, the response from Anthropic, and the extracted JSON data.
- **Failure:** Detailed error logs indicating where the process failed (e.g., HTTP errors, JSON parsing issues).
## 9. **Review and Update `views.py`**
Ensure that your views are correctly handling the serializer and providing meaningful error messages.
### **Check `views.py`:**
```python
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status, generics
from .serializers import PersonaSerializer, BlogPostSerializer
from .models import Persona, BlogPost
from .utils import generate_content
import logging
logger = logging.getLogger(__name__)
class AnalyzeWritingSampleView(APIView):
def post(self, request, *args, **kwargs):
logger.debug(f"Request data: {request.data}")
serializer = PersonaSerializer(data=request.data)
if serializer.is_valid():
persona = serializer.save()
return Response(PersonaSerializer(persona).data, status=status.HTTP_201_CREATED)
else:
logger.error(f"Serializer validation failed: {serializer.errors}")
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
```
### **Ensure:**
- **Correct Endpoint:** Verify that the POST request is being made to the correct URL (`/api/personas/`).
- **Data Structure:** Ensure that the data sent from the frontend matches the expected serializer fields (`name`, `writing_sample`).
## 10. **Check Frontend Payload Structure**
Ensure that the frontend is sending the correct data structure expected by the serializer.
### **Review `UploadSample.tsx`:**
```typescript
import React, { useState } from 'react';
import axios from '../axiosConfig'; // Adjust the path if necessary
import { Box, Button, TextField, Typography, Alert, Stack } from '@mui/material';
const UploadSample: React.FC = () => {
const [name, setName] = useState('');
const [writingSample, setWritingSample] = useState('');
const [error, setError] = useState<string | null>(null);
const [success, setSuccess] = useState<string | null>(null);
const handleSubmit = async (event: React.FormEvent) => {
event.preventDefault();
const payload = {
name: name.trim(),
writing_sample: writingSample.trim(),
};
try {
console.log('Payload being sent:', payload);
const response = await axios.post('personas/', payload); // Updated endpoint
console.log('Response received:', response.data);
setSuccess(`Persona "${response.data.name}" created successfully!`);
setError(null);
setName('');
setWritingSample('');
} catch (error: any) {
console.error('Error uploading writing sample:', error);
console.log('Error response:', error.response);
if (error.response && error.response.data) {
setError(JSON.stringify(error.response.data));
} else {
setError('An error occurred while uploading the writing sample.');
}
setSuccess(null);
}
};
return (
<Box p={4} maxWidth="600px" mx="auto">
<Typography variant="h4" gutterBottom>
Upload Writing Sample
</Typography>
{error && (
<Alert severity="error" sx={{ mb: 2 }}>
{error}
</Alert>
)}
{success && (
<Alert severity="success" sx={{ mb: 2 }}>
{success}
</Alert>
)}
<form onSubmit={handleSubmit}>
<Stack spacing={3}>
<TextField
label="Persona Name"
variant="outlined"
fullWidth
value={name}
onChange={(e) => setName(e.target.value)}
required
inputProps={{ maxLength: 100 }}
/>
<TextField
label="Writing Sample"
variant="outlined"
fullWidth
multiline
rows={6}
value={writingSample}
onChange={(e) => setWritingSample(e.target.value)}
required
/>
<Button type="submit" variant="contained" color="primary" size="large">
Submit
</Button>
</Stack>
</form>
</Box>
);
};
export default UploadSample;
```
### **Ensure:**
- **Correct Endpoint:** The POST request should align with your Django `urls.py`. If your endpoint is `/api/analyze/`, ensure the frontend is posting to `analyze/`. From the logs, it seems you're posting to `personas/`, so confirm this aligns with your Django views.
- **Field Names:** The payload keys (`name`, `writing_sample`) should match the serializer's expected fields.
- **Endpoint Consistency:**
From the initial code you provided, the `AnalyzeWritingSampleView` is mapped to `'analyze/'`, not `'personas/'`. However, in your log, the POST request is made to `'personas/'`. This mismatch can lead to unexpected behavior.
### **Solution:**
Update the frontend POST request to match the correct endpoint.
#### **Modify `UploadSample.tsx`:**
If your intention is to create a new persona by analyzing a writing sample, the POST request should be directed to the `'analyze/'` endpoint.
```typescript
const response = await axios.post('analyze/', payload); // Change from 'personas/' to 'analyze/'
```
### **Explanation:**
- **Django URL Configuration:**
```python
# core/urls.py
urlpatterns = [
path('analyze/', AnalyzeWritingSampleView.as_view(), name='analyze-writing-sample'),
path('generate-content/', GenerateContentView.as_view(), name='generate-content'),
path('personas/', PersonaListView.as_view(), name='persona-list'),
path('personas/<int:persona_id>/', PersonaDetailView.as_view(), name='persona-detail'),
path('blog-posts/', BlogPostView.as_view(), name='blogpost-list'),
]
```
- **Serializer Handling:**
The `AnalyzeWritingSampleView` uses `PersonaSerializer` to create a new `Persona` instance by analyzing the writing sample.
- **Frontend Responsibility:**
Ensure the frontend aligns with the backend's URL structure. If the backend expects a POST request to `'analyze/'` to create a new persona, the frontend should do the same.
## 11. **Revised Frontend Code**
Here's the corrected version of `UploadSample.tsx` to align with your Django endpoints:
```typescript
import React, { useState } from 'react';
import axios from '../axiosConfig'; // Adjust the path if necessary
import { Box, Button, TextField, Typography, Alert, Stack } from '@mui/material';
const UploadSample: React.FC = () => {
const [name, setName] = useState('');
const [writingSample, setWritingSample] = useState('');
const [error, setError] = useState<string | null>(null);
const [success, setSuccess] = useState<string | null>(null);
const handleSubmit = async (event: React.FormEvent) => {
event.preventDefault();
const payload = {
name: name.trim(),
writing_sample: writingSample.trim(),
};
try {
console.log('Payload being sent:', payload);
const response = await axios.post('analyze/', payload); // Corrected endpoint
console.log('Response received:', response.data);
setSuccess(`Persona "${response.data.name}" created successfully!`);
setError(null);
setName('');
setWritingSample('');
} catch (error: any) {
console.error('Error uploading writing sample:', error);
console.log('Error response:', error.response);
if (error.response && error.response.data) {
setError(JSON.stringify(error.response.data));
} else {
setError('An error occurred while uploading the writing sample.');
}
setSuccess(null);
}
};
return (
<Box p={4} maxWidth="600px" mx="auto">
<Typography variant="h4" gutterBottom>
Upload Writing Sample
</Typography>
{error && (
<Alert severity="error" sx={{ mb: 2 }}>
{error}
</Alert>
)}
{success && (
<Alert severity="success" sx={{ mb: 2 }}>
{success}
</Alert>
)}
<form onSubmit={handleSubmit}>
<Stack spacing={3}>
<TextField
label="Persona Name"
variant="outlined"
fullWidth
value={name}
onChange={(e) => setName(e.target.value)}
required
inputProps={{ maxLength: 100 }}
/>
<TextField
label="Writing Sample"
variant="outlined"
fullWidth
multiline
rows={6}
value={writingSample}
onChange={(e) => setWritingSample(e.target.value)}
required
/>
<Button type="submit" variant="contained" color="primary" size="large">
Submit
</Button>
</Stack>
</form>
</Box>
);
};
export default UploadSample;
```
## 12. **Update Frontend Logs Interpretation**
After making the above changes, monitor your frontend and backend logs again to see if the issue persists.
### **Expected Changes:**
- **Successful POST Request:**
The POST request should now be directed to the correct endpoint (`/api/analyze/`), which maps to `AnalyzeWritingSampleView`. If everything is set up correctly, the server should process the request, analyze the writing sample using Anthropic's API, and return a `201 Created` response with the new `Persona` data.
- **Error Handling:**
If the issue persists, the enhanced server-side logging should provide more insights into why the analysis failed. Look for specific error messages in the server logs to guide further troubleshooting.
## 13. **Additional Recommendations**
1. **API Rate Limits and Quotas:**
Ensure that your Anthropic API usage is within the allowed limits to prevent request failures.
2. **Handle Partial Failures Gracefully:**
In cases where the analysis partially succeeds or returns unexpected data, implement fallback mechanisms or user-friendly error messages.
3. **Secure API Keys:**
Always keep your API keys secure. Avoid exposing them in client-side code or repositories. Use environment variables and secure storage mechanisms.
4. **Validate Input Data:**
Implement frontend and backend validations to ensure that the input data meets the expected formats and constraints before processing.
5. **Implement Retry Logic:**
For transient errors (e.g., network issues), consider implementing retry mechanisms to enhance reliability.
6. **Update Documentation:**
Reflect all changes in your project documentation to assist future development and maintenance.
## 14. **Summary**
By following the steps outlined above, you should be able to identify and resolve the `400 Bad Request` error you're experiencing. The key actions involve:
- Enhancing server-side logging to gain deeper insights into failures.
- Ensuring alignment between frontend requests and backend endpoints.
- Validating the correct configuration and usage of the Anthropic API.
- Testing components independently to isolate and address issues effectively.
If after implementing these steps the issue still persists, please provide the updated server logs, especially the detailed logs from the `analyze_writing_sample` function, so I can assist you further.
o1-mini
October 24, 2024 - 17:56:35
Django version 5.1.2, using settings 'backend.settings'
Starting development server at http://127.0.0.1:8000/
Quit the server with CONTROL-C.
Unauthorized: /api/token/
[24/Oct/2024 17:57:00] "POST /api/token/ HTTP/1.1" 401 63
[24/Oct/2024 17:57:26] "OPTIONS /api/token/ HTTP/1.1" 200 0
[24/Oct/2024 17:57:27] "POST /api/token/ HTTP/1.1" 200 483
HTTP Request failed: 400 Client Error: Bad Request for url: https://api.anthropic.com/v1/complete
Failed to analyze writing sample.
Bad Request: /api/personas/
[24/Oct/2024 17:57:39] "POST /api/personas/ HTTP/1.1" 400 58
The `400 Bad Request` error you're encountering indicates that the server couldn't process the request due to client-side issues. In your case, the response `{ "writing_sample": "Failed to analyze the writing sample." }` suggests that the `analyze_writing_sample` function returned `None`, leading to a validation error in the serializer.
To effectively troubleshoot and resolve this issue, let's systematically address potential causes and implement solutions.
## Table of Contents
1. [Understanding the Issue](#1-understanding-the-issue)
2. [Verify Frontend Endpoint Configuration](#2-verify-frontend-endpoint-configuration)
3. [Enhance Server-Side Logging](#3-enhance-server-side-logging)
4. [Test Anthropic API Independently](#4-test-anthropic-api-independently)
5. [Review and Correct `utils.py`](#5-review-and-correct-utilspy)
6. [Ensure Correct Serializer Configuration](#6-ensure-correct-serializer-configuration)
7. [Confirm Environment Variables](#7-confirm-environment-variables)
8. [Final Testing](#8-final-testing)
9. [Additional Recommendations](#9-additional-recommendations)
---
## 1. Understanding the Issue
From the logs you provided:
```
HTTP Request failed: 400 Client Error: Bad Request for url: https://api.anthropic.com/v1/complete
Failed to analyze writing sample.
Bad Request: /api/personas/
[24/Oct/2024 17:57:39] "POST /api/personas/ HTTP/1.1" 400 58
```
- **Primary Error:** The request to Anthropic's API (`https://api.anthropic.com/v1/complete`) returned a `400 Bad Request`.
- **Secondary Error:** The Django serializer returned a `400 Bad Request` because `analyze_writing_sample` failed.
This indicates that the issue likely lies in the communication between your Django backend and the Anthropic API.
---
## 2. Verify Frontend Endpoint Configuration
### **Issue: Endpoint Mismatch**
From your initial code and the logs, there's a mismatch between the frontend and backend endpoints:
- **Backend URLs:**
```python
urlpatterns = [
path('analyze/', AnalyzeWritingSampleView.as_view(), name='analyze-writing-sample'),
path('generate-content/', GenerateContentView.as_view(), name='generate-content'),
path('personas/', PersonaListView.as_view(), name='persona-list'),
path('personas/<int:persona_id>/', PersonaDetailView.as_view(), name='persona-detail'),
path('blog-posts/', BlogPostView.as_view(), name='blogpost-list'),
]
```
- **Frontend Request:**
```
POST http://localhost:8000/api/personas/ 400 (Bad Request)
```
**Explanation:**
- The frontend is making a `POST` request to `/api/personas/`, which is mapped to `PersonaListView`. Typically, `PersonaListView` handles `GET` requests to list personas, not `POST` requests for creating personas with analyzed data.
- **Correct Endpoint:** To create a new persona by analyzing a writing sample, the frontend should `POST` to `/api/analyze/`, which is mapped to `AnalyzeWritingSampleView`.
### **Solution: Update Frontend to Use Correct Endpoint**
#### **Modify `UploadSample.tsx`**
Ensure that the frontend is making the `POST` request to the correct endpoint (`analyze/`) instead of `personas/`.
```typescript
// src/components/GhostWriter/UploadSample.tsx
import React, { useState } from 'react';
import axios from '../axiosConfig'; // Adjust the path if necessary
import { Box, Button, TextField, Typography, Alert, Stack } from '@mui/material';
const UploadSample: React.FC = () => {
const [name, setName] = useState('');
const [writingSample, setWritingSample] = useState('');
const [error, setError] = useState<string | null>(null);
const [success, setSuccess] = useState<string | null>(null);
const handleSubmit = async (event: React.FormEvent) => {
event.preventDefault();
const payload = {
name: name.trim(),
writing_sample: writingSample.trim(),
};
try {
console.log('Payload being sent:', payload);
const response = await axios.post('analyze/', payload); // Corrected endpoint
console.log('Response received:', response.data);
setSuccess(`Persona "${response.data.name}" created successfully!`);
setError(null);
setName('');
setWritingSample('');
} catch (error: any) {
console.error('Error uploading writing sample:', error);
console.log('Error response:', error.response);
if (error.response && error.response.data) {
setError(JSON.stringify(error.response.data));
} else {
setError('An error occurred while uploading the writing sample.');
}
setSuccess(null);
}
};
return (
<Box p={4} maxWidth="600px" mx="auto">
<Typography variant="h4" gutterBottom>
Upload Writing Sample
</Typography>
{error && (
<Alert severity="error" sx={{ mb: 2 }}>
{error}
</Alert>
)}
{success && (
<Alert severity="success" sx={{ mb: 2 }}>
{success}
</Alert>
)}
<form onSubmit={handleSubmit}>
<Stack spacing={3}>
<TextField
label="Persona Name"
variant="outlined"
fullWidth
value={name}
onChange={(e) => setName(e.target.value)}
required
inputProps={{ maxLength: 100 }}
/>
<TextField
label="Writing Sample"
variant="outlined"
fullWidth
multiline
rows={6}
value={writingSample}
onChange={(e) => setWritingSample(e.target.value)}
required
/>
<Button type="submit" variant="contained" color="primary" size="large">
Submit
</Button>
</Stack>
</form>
</Box>
);
};
export default UploadSample;
```
**Key Changes:**
- **Endpoint Correction:** Changed `axios.post('personas/', payload)` to `axios.post('analyze/', payload)` to align with the backend's `AnalyzeWritingSampleView`.
**Action Items:**
1. **Update Frontend Code:** Ensure all relevant frontend components make requests to the correct backend endpoints.
2. **Restart Frontend:** After making changes, restart your React development server to apply the updates.
---
## 3. Enhance Server-Side Logging
To gain deeper insights into why the `analyze_writing_sample` function is failing, enhance the logging within your `utils.py`.
### **Update `utils.py` with Detailed Logging**
```python
# core/utils.py
import logging
import requests
import json
import re
from decouple import config
logger = logging.getLogger(__name__)
# Update the API key and base URL for Anthropic
ANTHROPIC_API_KEY = config('ANTHROPIC_API_KEY')
ANTHROPIC_API_BASE = "https://api.anthropic.com/v1"
# Define the latest Anthropic model
ANTHROPIC_MODEL = "claude-2" # Ensure this is the latest model available
def analyze_writing_sample(writing_sample):
endpoint = f"{ANTHROPIC_API_BASE}/complete"
headers = {
"Content-Type": "application/json",
"X-API-Key": ANTHROPIC_API_KEY
}
prompt = f"""
Analyze the following writing sample and provide a detailed assessment of the author's characteristics. Use the template below and return the results in JSON format.
Template:
{{
"name": "[Author/Character Name]",
"vocabulary_complexity": [1-10],
"sentence_structure": "[simple/complex/varied]",
"paragraph_organization": "[structured/loose/stream-of-consciousness]",
"idiom_usage": [1-10],
"metaphor_frequency": [1-10],
"simile_frequency": [1-10],
"tone": "[formal/informal/academic/conversational/etc.]",
"punctuation_style": "[minimal/heavy/unconventional]",
"contraction_usage": [1-10],
"pronoun_preference": "[first-person/third-person/etc.]",
"passive_voice_frequency": [1-10],
"rhetorical_question_usage": [1-10],
"list_usage_tendency": [1-10],
"personal_anecdote_inclusion": [1-10],
"pop_culture_reference_frequency": [1-10],
"technical_jargon_usage": [1-10],
"parenthetical_aside_frequency": [1-10],
"humor_sarcasm_usage": [1-10],
"emotional_expressiveness": [1-10],
"emphatic_device_usage": [1-10],
"quotation_frequency": [1-10],
"analogy_usage": [1-10],
"sensory_detail_inclusion": [1-10],
"onomatopoeia_usage": [1-10],
"alliteration_frequency": [1-10],
"word_length_preference": "[short/long/varied]",
"foreign_phrase_usage": [1-10],
"rhetorical_device_usage": [1-10],
"statistical_data_usage": [1-10],
"personal_opinion_inclusion": [1-10],
"transition_usage": [1-10],
"reader_question_frequency": [1-10],
"imperative_sentence_usage": [1-10],
"dialogue_inclusion": [1-10],
"regional_dialect_usage": [1-10],
"hedging_language_frequency": [1-10],
"language_abstraction": "[concrete/abstract/mixed]",
"personal_belief_inclusion": [1-10],
"repetition_usage": [1-10],
"subordinate_clause_frequency": [1-10],
"verb_type_preference": "[active/stative/mixed]",
"sensory_imagery_usage": [1-10],
"symbolism_usage": [1-10],
"digression_frequency": [1-10],
"formality_level": [1-10],
"reflection_inclusion": [1-10],
"irony_usage": [1-10],
"neologism_frequency": [1-10],
"ellipsis_usage": [1-10],
"cultural_reference_inclusion": [1-10],
"stream_of_consciousness_usage": [1-10],
"openness_to_experience": [1-10],
"conscientiousness": [1-10],
"extraversion": [1-10],
"agreeableness": [1-10],
"emotional_stability": [1-10],
"dominant_motivations": "[achievement/affiliation/power/etc.]",
"core_values": "[integrity/freedom/knowledge/etc.]",
"decision_making_style": "[analytical/intuitive/spontaneous/etc.]",
"empathy_level": [1-10],
"self_confidence": [1-10],
"risk_taking_tendency": [1-10],
"idealism_vs_realism": "[idealistic/realistic/mixed]",
"conflict_resolution_style": "[assertive/collaborative/avoidant/etc.]",
"relationship_orientation": "[independent/communal/mixed]",
"emotional_response_tendency": "[calm/reactive/intense]",
"creativity_level": [1-10],
"age": "[age or age range]",
"gender": "[gender]",
"education_level": "[highest level of education]",
"professional_background": "[brief description]",
"cultural_background": "[brief description]",
"primary_language": "[language]",
"language_fluency": "[native/fluent/intermediate/beginner]",
"background": "[A brief paragraph describing the author's context, major influences, and any other relevant information not captured above]"
}}
Writing Sample:
{writing_sample}
"""
payload = {
"prompt": prompt,
"model": ANTHROPIC_MODEL,
"max_tokens_to_sample": 1500, # Adjust as needed
"temperature": 0.0, # Deterministic output
"stop_sequences": ["\n\n"]
}
logger.debug(f"Sending request to Anthropic API: {payload}")
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status() # Raises HTTPError for bad responses
# Log the API response for debugging
logger.debug(f"Anthropic API response: {response.text}")
assistant_message = response.json().get('completion', '').strip()
if not assistant_message:
logger.error("No 'completion' field found in the Anthropic API response.")
return None
logger.debug(f"Assistant message: {assistant_message}")
# Extract JSON from the assistant's message
json_str = re.search(r'\{.*\}', assistant_message, re.DOTALL)
if json_str:
try:
analyzed_data = json.loads(json_str.group())
logger.debug(f"Extracted JSON data: {analyzed_data}")
return analyzed_data
except json.JSONDecodeError as json_err:
logger.error(f"JSON decoding failed: {json_err}")
return None
else:
logger.error("No JSON object found in the response.")
return None
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error: {e}")
return None
def generate_content(persona_data, prompt):
endpoint = f"{ANTHROPIC_API_BASE}/complete"
headers = {
"Content-Type": "application/json",
"X-API-Key": ANTHROPIC_API_KEY
}
# Format the persona data into a readable string
characteristics = '\n'.join([
f"{key.replace('_', ' ').capitalize()}: {value}"
for key, value in persona_data.items()
if value is not None and key not in ['id', 'name']
])
decoding_prompt = f"""
You are to write a blog post in the style of {persona_data.get('name', 'Unknown Author')}, a writer with the following characteristics:
{characteristics}
Now, please write a blog post about the following topic:
"{prompt}"
Begin with a compelling title that reflects the content of the post.
"""
payload = {
"prompt": decoding_prompt,
"model": ANTHROPIC_MODEL,
"max_tokens_to_sample": 1500, # Adjust as needed
"temperature": 0.7, # Adjust for creativity
"stop_sequences": ["\n\n"]
}
logger.debug(f"Sending request to Anthropic API for content generation: {payload}")
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status()
assistant_message = response.json().get('completion', '').strip()
if not assistant_message:
logger.error("No 'completion' field found in the Anthropic API response.")
return ''
logger.debug(f"Assistant message: {assistant_message}")
return assistant_message
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return ''
except Exception as e:
logger.error(f"Unexpected error: {e}")
return ''
```
### **Key Enhancements:**
1. **Detailed Payload Logging:**
- Logs the entire payload being sent to Anthropic's API.
2. **Response Logging:**
- Logs the full response from Anthropic's API for inspection.
3. **Error Handling:**
- Checks if the `'completion'` field exists in the response.
- Logs specific errors related to JSON decoding or missing fields.
4. **Clear Separation of Concerns:**
- Maintains distinct logging for different stages of the request and response cycle.
### **Action Items:**
1. **Update `utils.py`:** Incorporate the above changes to enhance logging.
2. **Restart Django Server:** After updating, restart your Django development server to apply changes.
```bash
python manage.py runserver
```
---
## 4. Test Anthropic API Independently
Before integrating with your application, it's beneficial to test the Anthropic API independently to ensure that your API key and request structure are correct.
### **Using cURL:**
```bash
curl -X POST https://api.anthropic.com/v1/complete \
-H "Content-Type: application/json" \
-H "X-API-Key: your_anthropic_api_key_here" \
-d '{
"prompt": "Analyze the following writing sample and provide a detailed assessment of the author\'s characteristics. Return the results in JSON format.\n\nWriting Sample:\nShe was lost in thought, her mind wandering through memories long forgotten.",
"model": "claude-2",
"max_tokens_to_sample": 500,
"temperature": 0.0,
"stop_sequences": ["\n\n"]
}'
```
### **Expected Response:**
Anthropic should return a JSON object containing the `completion` field with the analysis.
### **Troubleshooting:**
- **Invalid API Key:** Ensure your API key is correct and active.
- **Model Availability:** Verify that `claude-2` is available and correctly named.
- **Rate Limits:** Ensure you haven't exceeded any rate limits imposed by Anthropic.
- **Payload Structure:** Confirm that your payload adheres to Anthropic's API specifications.
---
## 5. Review and Correct `utils.py`
Based on the enhanced logging, you can now identify where the failure is occurring. Here's a step-by-step guide to ensure your `utils.py` is correctly configured.
### **Full `utils.py` for Reference**
```python
# core/utils.py
import logging
import requests
import json
import re
from decouple import config
logger = logging.getLogger(__name__)
# Update the API key and base URL for Anthropic
ANTHROPIC_API_KEY = config('ANTHROPIC_API_KEY')
ANTHROPIC_API_BASE = "https://api.anthropic.com/v1"
# Define the latest Anthropic model
ANTHROPIC_MODEL = "claude-2" # Ensure this is the latest model available
def analyze_writing_sample(writing_sample):
endpoint = f"{ANTHROPIC_API_BASE}/complete"
headers = {
"Content-Type": "application/json",
"X-API-Key": ANTHROPIC_API_KEY
}
prompt = f"""
Analyze the following writing sample and provide a detailed assessment of the author's characteristics. Use the template below and return the results in JSON format.
Template:
{{
"name": "[Author/Character Name]",
"vocabulary_complexity": [1-10],
"sentence_structure": "[simple/complex/varied]",
"paragraph_organization": "[structured/loose/stream-of-consciousness]",
"idiom_usage": [1-10],
"metaphor_frequency": [1-10],
"simile_frequency": [1-10],
"tone": "[formal/informal/academic/conversational/etc.]",
"punctuation_style": "[minimal/heavy/unconventional]",
"contraction_usage": [1-10],
"pronoun_preference": "[first-person/third-person/etc.]",
"passive_voice_frequency": [1-10],
"rhetorical_question_usage": [1-10],
"list_usage_tendency": [1-10],
"personal_anecdote_inclusion": [1-10],
"pop_culture_reference_frequency": [1-10],
"technical_jargon_usage": [1-10],
"parenthetical_aside_frequency": [1-10],
"humor_sarcasm_usage": [1-10],
"emotional_expressiveness": [1-10],
"emphatic_device_usage": [1-10],
"quotation_frequency": [1-10],
"analogy_usage": [1-10],
"sensory_detail_inclusion": [1-10],
"onomatopoeia_usage": [1-10],
"alliteration_frequency": [1-10],
"word_length_preference": "[short/long/varied]",
"foreign_phrase_usage": [1-10],
"rhetorical_device_usage": [1-10],
"statistical_data_usage": [1-10],
"personal_opinion_inclusion": [1-10],
"transition_usage": [1-10],
"reader_question_frequency": [1-10],
"imperative_sentence_usage": [1-10],
"dialogue_inclusion": [1-10],
"regional_dialect_usage": [1-10],
"hedging_language_frequency": [1-10],
"language_abstraction": "[concrete/abstract/mixed]",
"personal_belief_inclusion": [1-10],
"repetition_usage": [1-10],
"subordinate_clause_frequency": [1-10],
"verb_type_preference": "[active/stative/mixed]",
"sensory_imagery_usage": [1-10],
"symbolism_usage": [1-10],
"digression_frequency": [1-10],
"formality_level": [1-10],
"reflection_inclusion": [1-10],
"irony_usage": [1-10],
"neologism_frequency": [1-10],
"ellipsis_usage": [1-10],
"cultural_reference_inclusion": [1-10],
"stream_of_consciousness_usage": [1-10],
"openness_to_experience": [1-10],
"conscientiousness": [1-10],
"extraversion": [1-10],
"agreeableness": [1-10],
"emotional_stability": [1-10],
"dominant_motivations": "[achievement/affiliation/power/etc.]",
"core_values": "[integrity/freedom/knowledge/etc.]",
"decision_making_style": "[analytical/intuitive/spontaneous/etc.]",
"empathy_level": [1-10],
"self_confidence": [1-10],
"risk_taking_tendency": [1-10],
"idealism_vs_realism": "[idealistic/realistic/mixed]",
"conflict_resolution_style": "[assertive/collaborative/avoidant/etc.]",
"relationship_orientation": "[independent/communal/mixed]",
"emotional_response_tendency": "[calm/reactive/intense]",
"creativity_level": [1-10],
"age": "[age or age range]",
"gender": "[gender]",
"education_level": "[highest level of education]",
"professional_background": "[brief description]",
"cultural_background": "[brief description]",
"primary_language": "[language]",
"language_fluency": "[native/fluent/intermediate/beginner]",
"background": "[A brief paragraph describing the author's context, major influences, and any other relevant information not captured above]"
}}
Writing Sample:
{writing_sample}
"""
payload = {
"prompt": prompt,
"model": ANTHROPIC_MODEL,
"max_tokens_to_sample": 1500, # Adjust as needed
"temperature": 0.0, # Deterministic output
"stop_sequences": ["\n\n"]
}
logger.debug(f"Sending request to Anthropic API: {payload}")
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status() # Raises HTTPError for bad responses
# Log the API response for debugging
logger.debug(f"Anthropic API response: {response.text}")
assistant_message = response.json().get('completion', '').strip()
if not assistant_message:
logger.error("No 'completion' field found in the Anthropic API response.")
return None
logger.debug(f"Assistant message: {assistant_message}")
# Extract JSON from the assistant's message
json_str = re.search(r'\{.*\}', assistant_message, re.DOTALL)
if json_str:
try:
analyzed_data = json.loads(json_str.group())
logger.debug(f"Extracted JSON data: {analyzed_data}")
return analyzed_data
except json.JSONDecodeError as json_err:
logger.error(f"JSON decoding failed: {json_err}")
return None
else:
logger.error("No JSON object found in the response.")
return None
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error: {e}")
return None
def generate_content(persona_data, prompt):
endpoint = f"{ANTHROPIC_API_BASE}/complete"
headers = {
"Content-Type": "application/json",
"X-API-Key": ANTHROPIC_API_KEY
}
# Format the persona data into a readable string
characteristics = '\n'.join([
f"{key.replace('_', ' ').capitalize()}: {value}"
for key, value in persona_data.items()
if value is not None and key not in ['id', 'name']
])
decoding_prompt = f"""
You are to write a blog post in the style of {persona_data.get('name', 'Unknown Author')}, a writer with the following characteristics:
{characteristics}
Now, please write a blog post about the following topic:
"{prompt}"
Begin with a compelling title that reflects the content of the post.
"""
payload = {
"prompt": decoding_prompt,
"model": ANTHROPIC_MODEL,
"max_tokens_to_sample": 1500, # Adjust as needed
"temperature": 0.7, # Adjust for creativity
"stop_sequences": ["\n\n"]
}
logger.debug(f"Sending request to Anthropic API for content generation: {payload}")
try:
response = requests.post(endpoint, headers=headers, json=payload)
response.raise_for_status()
assistant_message = response.json().get('completion', '').strip()
if not assistant_message:
logger.error("No 'completion' field found in the Anthropic API response.")
return ''
logger.debug(f"Assistant message: {assistant_message}")
return assistant_message
except requests.exceptions.RequestException as e:
logger.error(f"HTTP Request failed: {e}")
return ''
except Exception as e:
logger.error(f"Unexpected error: {e}")
return ''
```
### **Checklist:**
1. **Endpoint Verification:**
- **URL:** `https://api.anthropic.com/v1/complete` is correct as per Anthropic's documentation.
- **Headers:** Ensure `X-API-Key` is used for authentication.
2. **Payload Structure:**
- **Prompt:** Clearly structured with the necessary instructions and template.
- **Model:** Ensure `claude-2` is the correct and latest model. If a newer model exists, update accordingly.
- **Parameters:**
- `max_tokens_to_sample`: Adjust based on the expected length.
- `temperature`: Set to `0.0` for deterministic output during analysis; `0.7` for content generation to allow creativity.
- `stop_sequences`: Ensure they align with how Anthropic expects to terminate responses.
3. **Error Handling:**
- **JSON Decoding:** Handles scenarios where JSON extraction fails.
- **Missing Fields:** Checks for the presence of the `'completion'` field.
4. **Logging:**
- **Before Request:** Logs the entire payload.
- **After Response:** Logs the full response and the extracted assistant message.
- **Error Logs:** Detailed error messages for easier debugging.
### **Action Items:**
1. **Ensure Correct Model:** Confirm that `claude-2` is available. If Anthropic has released a newer model (e.g., `claude-3`), update `ANTHROPIC_MODEL` accordingly.
2. **Validate Prompt Formatting:** Ensure the prompt is clear and adheres to Anthropic's guidelines.
3. **Restart Django Server:** After making changes, restart the server to apply updates.
```bash
python manage.py runserver
```
---
## 6. Ensure Correct Serializer Configuration
The serializer is responsible for validating and saving the data. Let's ensure it's correctly set up to handle the analyzed data.
### **Review `serializers.py`**
```python
# core/serializers.py
from rest_framework import serializers
from .models import Persona, BlogPost
from .utils import analyze_writing_sample
import logging
logger = logging.getLogger(__name__)
class PersonaSerializer(serializers.ModelSerializer):
writing_sample = serializers.CharField(write_only=True)
class Meta:
model = Persona
fields = ['id', 'name', 'writing_sample', 'data']
read_only_fields = ['id', 'data']
def create(self, validated_data):
writing_sample = validated_data.pop('writing_sample')
logger.debug(f"Writing sample received: {writing_sample[:100]}...") # Log first 100 characters
# Analyze the writing sample
analyzed_data = analyze_writing_sample(writing_sample)
logger.debug(f"Analyzed data: {analyzed_data}")
if not analyzed_data:
logger.error("analyze_writing_sample returned empty or invalid data.")
raise serializers.ValidationError({"writing_sample": "Failed to analyze the writing sample."})
# Store the analyzed data
validated_data['data'] = analyzed_data
# Save persona
return Persona.objects.create(**validated_data)
```
### **Checklist:**
1. **Fields Alignment:**
- **Frontend Payload:** Should include `name` and `writing_sample`.
- **Serializer Fields:** `name`, `writing_sample` (write-only), and `data` (read-only).
2. **Data Validation:**
- Ensures `analyzed_data` is not `None`.
- Raises a `ValidationError` if analysis fails.
3. **Logging:**
- Logs the received writing sample (first 100 characters).
- Logs the analyzed data or any errors encountered.
### **Action Items:**
1. **Confirm Model Fields:**
- Ensure the `Persona` model has `name`, `writing_sample`, and `data` fields.
```python
# core/models.py
from django.db import models
class Persona(models.Model):
name = models.CharField(max_length=100, blank=True, null=True)
data = models.JSONField(blank=True, null=True) # Stores analyzed writing sample data
def __str__(self):
return self.name
```
2. **Ensure Serializer is Invoked Correctly:**
- The `AnalyzeWritingSampleView` should use `PersonaSerializer` to create a new `Persona`.
```python
# core/views.py
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status, generics
from .serializers import PersonaSerializer, BlogPostSerializer
from .models import Persona, BlogPost
from .utils import generate_content
import logging
logger = logging.getLogger(__name__)
class AnalyzeWritingSampleView(APIView):
def post(self, request, *args, **kwargs):
logger.debug(f"Request data: {request.data}")
serializer = PersonaSerializer(data=request.data)
if serializer.is_valid():
persona = serializer.save()
return Response(PersonaSerializer(persona).data, status=status.HTTP_201_CREATED)
else:
logger.error(f"Serializer validation failed: {serializer.errors}")
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
```
3. **Run Migrations (If Necessary):**
If you've made changes to the models, ensure migrations are applied.
```bash
python manage.py makemigrations
python manage.py migrate
```
---
## 7. Confirm Environment Variables
Ensure that the `ANTHROPIC_API_KEY` is correctly set and loaded into your Django application.
### **Steps:**
1. **Check `.env` File:**
Ensure your `.env` file (located in the project's root directory) contains the correct Anthropic API key.
```env
ANTHROPIC_API_KEY=your_anthropic_api_key_here
```
> **Security Tip:** Ensure that your `.env` file is **not** committed to version control. Add it to `.gitignore` if necessary.
2. **Verify `settings.py`:**
Ensure that Django is loading the environment variables correctly.
```python
# backend/settings.py
from decouple import config
ANTHROPIC_API_KEY = config('ANTHROPIC_API_KEY')
```
3. **Restart Django Server:**
After verifying, restart your Django server to apply changes.
```bash
python manage.py runserver
```
4. **Test Access to `ANTHROPIC_API_KEY`:**
You can temporarily add a test log to confirm the API key is loaded (ensure to remove it afterward to avoid exposing sensitive information).
```python
# core/utils.py
logger.debug(f"Anthropic API Key: {ANTHROPIC_API_KEY}")
```
**Note:** **Do not** log sensitive information like API keys in production environments.
---
## 8. Final Testing
After ensuring all configurations are correct, perform a thorough test to verify that everything works as expected.
### **Steps:**
1. **Start Django Server:**
```bash
python manage.py runserver
```
2. **Start React Development Server:**
```bash
npm start
# or
yarn start
```
3. **Use Frontend to Submit Writing Sample:**
- Navigate to the **Upload Sample** page in your React application.
- Enter a **Persona Name** and a **Writing Sample**.
- Submit the form.
4. **Monitor Logs:**
- **Frontend Console:** Check for any errors or successful messages.
- **Django Server Logs:** Observe the detailed logs added in `utils.py` to trace the request and response cycle with Anthropic's API.
5. **Verify Persona Creation:**
- If successful, the new persona should appear in the **Personas** list.
- If errors persist, refer to the detailed server logs to identify the root cause.
### **Expected Outcome:**
- **Successful Analysis:**
- The `analyze_writing_sample` function sends a correctly formatted request to Anthropic's API.
- Receives a valid JSON response with the analyzed data.
- The serializer saves the new `Persona` instance with the analyzed data.
- Frontend displays a success message and clears the form.
- **Failure Scenario:**
- If the request to Anthropic's API is malformed or the API key is incorrect, the server logs will indicate the exact issue.
- The frontend displays an error message with details from the server.
---
## 9. Additional Recommendations
### **1. Validate API Responses**
Ensure that the responses from Anthropic's API strictly adhere to the expected format. Any deviations can cause JSON parsing to fail.
### **2. Implement Retry Logic**
For transient errors (e.g., network issues), consider implementing retry mechanisms to enhance reliability.
```python
# Example using tenacity for retries
from tenacity import retry, stop_after_attempt, wait_fixed
@retry(stop=stop_after_attempt(3), wait=wait_fixed(2))
def analyze_writing_sample(writing_sample):
# Existing implementation
pass
```
### **3. Secure API Keys**
Ensure that API keys are never exposed in client-side code or version control. Use environment variables and secure storage practices.
### **4. Rate Limiting and Usage Monitoring**
Monitor your Anthropic API usage to avoid exceeding rate limits or incurring unexpected costs. Implement usage tracking and alerting as needed.
### **5. Error Feedback to Users**
Provide meaningful error messages to users without exposing sensitive information. For instance, instead of showing raw error responses, display user-friendly messages.
```python
# core/views.py
class AnalyzeWritingSampleView(APIView):
def post(self, request, *args, **kwargs):
logger.debug(f"Request data: {request.data}")
serializer = PersonaSerializer(data=request.data)
if serializer.is_valid():
try:
persona = serializer.save()
return Response(PersonaSerializer(persona).data, status=status.HTTP_201_CREATED)
except serializers.ValidationError as ve:
logger.error(f"Validation Error: {ve.detail}")
return Response({"error": "Failed to analyze the writing sample. Please try again."}, status=status.HTTP_400_BAD_REQUEST)
else:
logger.error(f"Serializer validation failed: {serializer.errors}")
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
```
### **6. Update Documentation**
Maintain up-to-date documentation reflecting the changes made during this migration. This assists future development and onboarding.
---
## Conclusion
By following the steps outlined above, you should be able to identify and resolve the `400 Bad Request` error you're experiencing. The key actions involve:
1. **Ensuring Frontend-Backend Endpoint Alignment:** The frontend should `POST` to `/api/analyze/` instead of `/api/personas/` to trigger the correct Django view for analyzing writing samples.
2. **Enhancing Logging:** Detailed logs in `utils.py` will help trace the exact point of failure when communicating with Anthropic's API.
3. **Validating API Configuration:** Confirm that your API key, model name, and request payload conform to Anthropic's specifications.
4. **Independent API Testing:** Use tools like cURL or Postman to test Anthropic's API outside of your application to ensure it's functioning as expected.
5. **Serializer and Model Configuration:** Ensure that your Django serializers and models are correctly set up to handle the analyzed data.
6. **Final Integration Testing:** After making all adjustments, perform comprehensive tests to ensure the entire workflow operates seamlessly.
If after implementing these steps the issue still persists, please provide the updated server logs, especially the detailed logs from the `analyze_writing_sample` function, so I can assist you further.
o1-mini
Payload being sent: {name: 'Karamazov 2', writing_sample: 'rom active punishment herself, as the sinner is to…is heart. Tears positively\nglittered in his eyes.'}
UploadSample.tsx:23
POST http://localhost:8000/api/analyze/ 404 (Not Found)
dispatchXhrRequest @ axios.js?v=5abf39a3:1680
xhr @ axios.js?v=5abf39a3:1560
dispatchRequest @ axios.js?v=5abf39a3:2035
Promise.then
_request @ axios.js?v=5abf39a3:2222
request @ axios.js?v=5abf39a3:2141
httpMethod @ axios.js?v=5abf39a3:2269
wrap @ axios.js?v=5abf39a3:8
handleSubmit @ UploadSample.tsx:23
callCallback2 @ chunk-NUMECXU6.js?v=5abf39a3:3674
invokeGuardedCallbackDev @ chunk-NUMECXU6.js?v=5abf39a3:3699
invokeGuardedCallback @ chunk-NUMECXU6.js?v=5abf39a3:3733
invokeGuardedCallbackAndCatchFirstError @ chunk-NUMECXU6.js?v=5abf39a3:3736
executeDispatch @ chunk-NUMECXU6.js?v=5abf39a3:7014
processDispatchQueueItemsInOrder @ chunk-NUMECXU6.js?v=5abf39a3:7034
processDispatchQueue @ chunk-NUMECXU6.js?v=5abf39a3:7043
dispatchEventsForPlugins @ chunk-NUMECXU6.js?v=5abf39a3:7051
(anonymous) @ chunk-NUMECXU6.js?v=5abf39a3:7174
batchedUpdates$1 @ chunk-NUMECXU6.js?v=5abf39a3:18913
batchedUpdates @ chunk-NUMECXU6.js?v=5abf39a3:3579
dispatchEventForPluginEventSystem @ chunk-NUMECXU6.js?v=5abf39a3:7173
dispatchEventWithEnableCapturePhaseSelectiveHydrationWithoutDiscreteEventReplay @ chunk-NUMECXU6.js?v=5abf39a3:5478
dispatchEvent @ chunk-NUMECXU6.js?v=5abf39a3:5472
dispatchDiscreteEvent @ chunk-NUMECXU6.js?v=5abf39a3:5449
Show 22 more frames
Show lessUnderstand this error
UploadSample.tsx:30 Error uploading writing sample: AxiosError {message: 'Request failed with status code 404', name: 'AxiosError', code: 'ERR_BAD_REQUEST', config: {…}, request: XMLHttpRequest, …}code: "ERR_BAD_REQUEST"config: {transitional: {…}, adapter: Array(3), transformRequest: Array(1), transformResponse: Array(1), timeout: 0, …}message: "Request failed with status code 404"name: "AxiosError"request: XMLHttpRequest {onreadystatechange: null, readyState: 4, timeout: 0, withCredentials: false, upload: XMLHttpRequestUpload, …}response: {data: '<!DOCTYPE html>\n<html lang="en">\n<head>\n <meta ht…d 404 page.\n </p>\n </footer>\n</body>\n</html>\n', status: 404, statusText: 'Not Found', headers: AxiosHeaders, config: {…}, …}status: 404stack: "AxiosError: Request failed with status code 404\n at settle (http://localhost:5173/node_modules/.vite/deps/axios.js?v=5abf39a3:1230:12)\n at XMLHttpRequest.onloadend (http://localhost:5173/node_modules/.vite/deps/axios.js?v=5abf39a3:1593:7)\n at Axios.request (http://localhost:5173/node_modules/.vite/deps/axios.js?v=5abf39a3:2145:41)\n at async handleSubmit (http://localhost:5173/src/components/GhostWriter/UploadSample.tsx:35:24)"[[Prototype]]: Error
handleSubmit @ UploadSample.tsx:30
await in handleSubmit
callCallback2 @ chunk-NUMECXU6.js?v=5abf39a3:3674
invokeGuardedCallbackDev @ chunk-NUMECXU6.js?v=5abf39a3:3699
invokeGuardedCallback @ chunk-NUMECXU6.js?v=5abf39a3:3733
invokeGuardedCallbackAndCatchFirstError @ chunk-NUMECXU6.js?v=5abf39a3:3736
executeDispatch @ chunk-NUMECXU6.js?v=5abf39a3:7014
processDispatchQueueItemsInOrder @ chunk-NUMECXU6.js?v=5abf39a3:7034
processDispatchQueue @ chunk-NUMECXU6.js?v=5abf39a3:7043
dispatchEventsForPlugins @ chunk-NUMECXU6.js?v=5abf39a3:7051
(anonymous) @ chunk-NUMECXU6.js?v=5abf39a3:7174
batchedUpdates$1 @ chunk-NUMECXU6.js?v=5abf39a3:18913
batchedUpdates @ chunk-NUMECXU6.js?v=5abf39a3:3579
dispatchEventForPluginEventSystem @ chunk-NUMECXU6.js?v=5abf39a3:7173
dispatchEventWithEnableCapturePhaseSelectiveHydrationWithoutDiscreteEventReplay @ chunk-NUMECXU6.js?v=5abf39a3:5478
dispatchEvent @ chunk-NUMECXU6.js?v=5abf39a3:5472
dispatchDiscreteEvent @ chunk-NUMECXU6.js?v=5abf39a3:5449
Show 15 more frames
Show lessUnderstand this error
UploadSample.tsx:31 Error response: {data: '<!DOCTYPE html>\n<html lang="en">\n<head>\n <meta ht…d 404 page.\n </p>\n </footer>\n</body>\n</html>\n', status: 404, statusText: 'Not Found', headers: AxiosHeaders, config: {…}, …}
The `404 Not Found` error you're encountering when making a `POST` request to `http://localhost:8000/api/analyze/` indicates that the Django backend does not recognize this endpoint. This typically means that the URL is not correctly mapped to a corresponding view in your Django application.
Let's systematically troubleshoot and resolve this issue.
---
## Table of Contents
1. [Understanding the 404 Error](#1-understanding-the-404-error)
2. [Verify Backend URL Configuration](#2-verify-backend-url-configuration)
3. [Ensure `AnalyzeWritingSampleView` is Defined Correctly](#3-ensure-analyzewritingsampleview-is-defined-correctly)
4. [Check URL Patterns and Imports](#4-check-url-patterns-and-imports)
5. [Restart the Django Development Server](#5-restart-the-django-development-server)
6. [Test the Endpoint Independently](#6-test-the-endpoint-independently)
7. [Review Frontend Configuration](#7-review-frontend-configuration)
8. [Additional Debugging Steps](#8-additional-debugging-steps)
9. [Summary](#9-summary)
---
## 1. Understanding the 404 Error
A `404 Not Found` error signifies that the server cannot find the requested resource. In your case, the frontend is attempting to `POST` data to `/api/analyze/`, but Django doesn't have a URL pattern that matches this endpoint.
---
## 2. Verify Backend URL Configuration
### **Check `core/urls.py`**
Ensure that the `core` app's `urls.py` includes the `analyze/` endpoint mapped to `AnalyzeWritingSampleView`.
```python
# core/urls.py
from django.urls import path
from .views import (
AnalyzeWritingSampleView,
GenerateContentView,
PersonaListView,
PersonaDetailView,
BlogPostView
)
urlpatterns = [
path('analyze/', AnalyzeWritingSampleView.as_view(), name='analyze-writing-sample'),
path('generate-content/', GenerateContentView.as_view(), name='generate-content'),
path('personas/', PersonaListView.as_view(), name='persona-list'),
path('personas/<int:persona_id>/', PersonaDetailView.as_view(), name='persona-detail'),
path('blog-posts/', BlogPostView.as_view(), name='blogpost-list'),
]
```
### **Check `backend/urls.py`**
Confirm that the `core` app's URLs are included under the `api/` prefix.
```python
# backend/urls.py
from django.contrib import admin
from django.urls import path, include
urlpatterns = [
path('admin/', admin.site.urls),
path('api/', include('core.urls')), # Ensure this line exists
]
```
**Action Items:**
1. **Open `core/urls.py`:** Verify that the `analyze/` endpoint is correctly defined.
2. **Open `backend/urls.py`:** Ensure that the `core.urls` are included under the `api/` path.
---
## 3. Ensure `AnalyzeWritingSampleView` is Defined Correctly
### **Check `views.py`**
Ensure that `AnalyzeWritingSampleView` is properly defined in `core/views.py`.
```python
# core/views.py
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from .serializers import PersonaSerializer
from .utils import generate_content
import logging
logger = logging.getLogger(__name__)
class AnalyzeWritingSampleView(APIView):
def post(self, request, *args, **kwargs):
logger.debug(f"Request data: {request.data}")
serializer = PersonaSerializer(data=request.data)
if serializer.is_valid():
persona = serializer.save()
return Response(PersonaSerializer(persona).data, status=status.HTTP_201_CREATED)
else:
logger.error(f"Serializer validation failed: {serializer.errors}")
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
```
**Action Items:**
1. **Open `core/views.py`:** Confirm that `AnalyzeWritingSampleView` is defined as shown above.
2. **Ensure Imports Are Correct:** Verify that all necessary imports (`APIView`, `Response`, `status`, `PersonaSerializer`, `generate_content`, `logging`) are present and correctly spelled.
---
## 4. Check URL Patterns and Imports
### **Ensure Correct Imports in `core/urls.py`**
Make sure that `AnalyzeWritingSampleView` is imported correctly in `core/urls.py`.
```python
# core/urls.py
from django.urls import path
from .views import (
AnalyzeWritingSampleView,
GenerateContentView,
PersonaListView,
PersonaDetailView,
BlogPostView
)
urlpatterns = [
path('analyze/', AnalyzeWritingSampleView.as_view(), name='analyze-writing-sample'),
path('generate-content/', GenerateContentView.as_view(), name='generate-content'),
path('personas/', PersonaListView.as_view(), name='persona-list'),
path('personas/<int:persona_id>/', PersonaDetailView.as_view(), name='persona-detail'),
path('blog-posts/', BlogPostView.as_view(), name='blogpost-list'),
]
```
**Common Issues:**
- **Typographical Errors:** Ensure there are no typos in the view names.
- **Incorrect Imports:** Verify that `AnalyzeWritingSampleView` is correctly imported from `views.py`.
**Action Items:**
1. **Open `core/urls.py`:** Check the import statements for accuracy.
2. **Verify View Names:** Ensure that `AnalyzeWritingSampleView` matches exactly with its definition in `views.py`.
---
## 5. Restart the Django Development Server
After making changes to your Django project (especially URL configurations), it's essential to restart the development server to apply the updates.
**Action Steps:**
1. **Stop the Server:** If it's running, stop it using `CTRL + C` in the terminal.
2. **Start the Server Again:**
```bash
python manage.py runserver
```
3. **Monitor Startup Logs:** Look for any errors during startup that might indicate issues with URL patterns or view definitions.
---
## 6. Test the Endpoint Independently
Before testing through the frontend, verify that the backend endpoint `/api/analyze/` is accessible and functioning correctly.
### **Using cURL**
Execute the following command in your terminal:
```bash
curl -X POST http://localhost:8000/api/analyze/ \
-H "Content-Type: application/json" \
-d '{
"name": "Karamazov 2",
"writing_sample": "rom active punishment herself, as the sinner is to…is heart. Tears positively glittered in his eyes."
}'
```
### **Expected Response:**
- **Success (`201 Created`):** JSON data of the created `Persona`.
- **Failure (`400 Bad Request`):** Error messages indicating what went wrong.
### **Troubleshooting:**
- **404 Error:** Indicates the endpoint is still not recognized. Revisit steps 2-4.
- **400 Error:** Suggests issues with the request payload or processing logic. Check backend logs for details.
---
## 7. Review Frontend Configuration
Ensure that the frontend is correctly configured to send requests to the right endpoint.
### **Check `UploadSample.tsx`**
From your logs, it appears you're attempting to `POST` to `/api/analyze/`, which is correct. However, ensure that the frontend is correctly configured.
```typescript
// src/components/GhostWriter/UploadSample.tsx
import React, { useState } from 'react';
import axios from '../axiosConfig'; // Ensure this path is correct
import { Box, Button, TextField, Typography, Alert, Stack } from '@mui/material';
const UploadSample: React.FC = () => {
const [name, setName] = useState('');
const [writingSample, setWritingSample] = useState('');
const [error, setError] = useState<string | null>(null);
const [success, setSuccess] = useState<string | null>(null);
const handleSubmit = async (event: React.FormEvent) => {
event.preventDefault();
const payload = {
name: name.trim(),
writing_sample: writingSample.trim(),
};
try {
console.log('Payload being sent:', payload);
const response = await axios.post('analyze/', payload); // Correct endpoint
console.log('Response received:', response.data);
setSuccess(`Persona "${response.data.name}" created successfully!`);
setError(null);
setName('');
setWritingSample('');
} catch (error: any) {
console.error('Error uploading writing sample:', error);
console.log('Error response:', error.response);
if (error.response && error.response.data) {
setError(JSON.stringify(error.response.data));
} else {
setError('An error occurred while uploading the writing sample.');
}
setSuccess(null);
}
};
return (
<Box p={4} maxWidth="600px" mx="auto">
<Typography variant="h4" gutterBottom>
Upload Writing Sample
</Typography>
{error && (
<Alert severity="error" sx={{ mb: 2 }}>
{error}
</Alert>
)}
{success && (
<Alert severity="success" sx={{ mb: 2 }}>
{success}
</Alert>
)}
<form onSubmit={handleSubmit}>
<Stack spacing={3}>
<TextField
label="Persona Name"
variant="outlined"
fullWidth
value={name}
onChange={(e) => setName(e.target.value)}
required
inputProps={{ maxLength: 100 }}
/>
<TextField
label="Writing Sample"
variant="outlined"
fullWidth
multiline
rows={6}
value={writingSample}
onChange={(e) => setWritingSample(e.target.value)}
required
/>
<Button type="submit" variant="contained" color="primary" size="large">
Submit
</Button>
</Stack>
</form>
</Box>
);
};
export default UploadSample;
```
### **Checklist:**
1. **Correct Endpoint:** Ensure that `axios.post('analyze/', payload)` points to the correct relative URL. Given that your Axios instance has a `baseURL` of `http://localhost:8000/api/`, this will correctly target `http://localhost:8000/api/analyze/`.
2. **Axios Configuration:**
```javascript
// src/axiosConfig.js
import axios from 'axios';
const instance = axios.create({
baseURL: 'http://localhost:8000/api/', // Ensure this matches your backend
});
export default instance;
```
3. **Path Accuracy:** Confirm that the path `'analyze/'` is correctly spelled and matches the backend URL pattern.
---
## 8. Additional Debugging Steps
If the issue persists after following the above steps, consider the following additional debugging measures.
### **a. List All URL Patterns**
Use Django's `show_urls` management command to list all available URL patterns and verify that `/api/analyze/` exists.
**Option 1: Using `django-extensions`**
1. **Install `django-extensions`:**
```bash
pip install django-extensions
```
2. **Add to `INSTALLED_APPS` in `backend/settings.py`:**
```python
INSTALLED_APPS = [
# ... other apps ...
'django_extensions',
]
```
3. **Run `show_urls`:**
```bash
python manage.py show_urls
```
4. **Look for `/api/analyze/`:** Ensure it is listed.
**Option 2: Manual Inspection**
Manually inspect `core/urls.py` and `backend/urls.py` to confirm that the `analyze/` endpoint is correctly mapped.
### **b. Create a Test View**
To verify that the URL mapping works, create a simple test view.
**Steps:**
1. **Add Test View in `core/views.py`:**
```python
from rest_framework.views import APIView
from rest_framework.response import Response
class TestAnalyzeView(APIView):
def get(self, request):
return Response({"message": "Analyze endpoint is working."})
```
2. **Map the Test View in `core/urls.py`:**
```python
urlpatterns = [
path('analyze/', AnalyzeWritingSampleView.as_view(), name='analyze-writing-sample'),
path('test-analyze/', TestAnalyzeView.as_view(), name='test-analyze'), # New test endpoint
# ... other paths ...
]
```
3. **Restart Django Server:**
```bash
python manage.py runserver
```
4. **Access Test Endpoint:**
Navigate to `http://localhost:8000/api/test-analyze/` in your browser or use cURL:
```bash
curl http://localhost:8000/api/test-analyze/
```
**Expected Response:**
```json
{
"message": "Analyze endpoint is working."
}
```
5. **Interpret Results:**
- **Success:** Indicates that the URL routing is functioning correctly.
- **Failure:** Suggests issues with URL configuration or view definitions.
6. **Remove Test View After Verification:** Once confirmed, remove the test view to maintain code cleanliness.
### **c. Inspect Django Server Logs**
With detailed logging in place, examine the Django server logs when a request is made to `/api/analyze/`.
**Steps:**
1. **Make a `POST` Request from Frontend or cURL.**
2. **Check Server Logs:** Look for debug messages from `AnalyzeWritingSampleView` and `utils.py`.
**Example Log Entries:**
```plaintext
DEBUG:core.views:Request data: {'name': 'Karamazov 2', 'writing_sample': 'rom active punishment herself...'}
DEBUG:core.utils:Sending request to Anthropic API: {...}
DEBUG:core.utils:Anthropic API response: {...}
DEBUG:core.utils:Assistant message: {...}
DEBUG:core.utils:Extracted JSON data: {...}
```
**Interpretation:**
- **Presence of Logs:** Indicates that the request reached the view and is being processed.
- **Absence of Logs:** Suggests that the request didn't reach the view, possibly due to URL misconfiguration.
### **d. Verify Anthropic API Key and Permissions**
Ensure that your Anthropic API key is valid and has the necessary permissions.
**Steps:**
1. **Check API Key in `.env`:** Ensure it's correctly set.
```env
ANTHROPIC_API_KEY=your_valid_anthropic_api_key
```
2. **Test API Key Independently:** Use cURL or Postman to send a request to Anthropic's API.
```bash
curl -X POST https://api.anthropic.com/v1/complete \
-H "Content-Type: application/json" \
-H "X-API-Key: your_anthropic_api_key_here" \
-d '{
"prompt": "Test prompt",
"model": "claude-2",
"max_tokens_to_sample": 100,
"temperature": 0.0,
"stop_sequences": ["\n\n"]
}'
```
3. **Verify Response:**
- **Success:** Receive a valid JSON response with a `completion` field.
- **Failure:** Check for errors related to the API key or request structure.
### **e. Review Serializer and Model Definitions**
Ensure that your `Persona` model and `PersonaSerializer` are correctly defined to handle the incoming data.
**Model (`core/models.py`):**
```python
from django.db import models
class Persona(models.Model):
name = models.CharField(max_length=100, blank=True, null=True)
data = models.JSONField(blank=True, null=True) # Stores analyzed writing sample data
def __str__(self):
return self.name
```
**Serializer (`core/serializers.py`):**
```python
from rest_framework import serializers
from .models import Persona
from .utils import analyze_writing_sample
import logging
logger = logging.getLogger(__name__)
class PersonaSerializer(serializers.ModelSerializer):
writing_sample = serializers.CharField(write_only=True)
class Meta:
model = Persona
fields = ['id', 'name', 'writing_sample', 'data']
read_only_fields = ['id', 'data']
def create(self, validated_data):
writing_sample = validated_data.pop('writing_sample')
logger.debug(f"Writing sample received: {writing_sample[:100]}...") # Log first 100 characters
# Analyze the writing sample
analyzed_data = analyze_writing_sample(writing_sample)
logger.debug(f"Analyzed data: {analyzed_data}")
if not analyzed_data:
logger.error("analyze_writing_sample returned empty or invalid data.")
raise serializers.ValidationError({"writing_sample": "Failed to analyze the writing sample."})
# Store the analyzed data
validated_data['data'] = analyzed_data
# Save persona
return Persona.objects.create(**validated_data)
```
**Checklist:**
1. **Model Fields:** Ensure that the `Persona` model has `name` and `data` fields, with `data` being a `JSONField`.
2. **Serializer Fields:** Confirm that `PersonaSerializer` includes `name`, `writing_sample`, and `data`, with `writing_sample` as `write_only`.
3. **Serializer Validation:** The `create` method should handle the analysis and raise appropriate errors if analysis fails.
---
## 9. Summary
To resolve the `404 Not Found` error when posting to `/api/analyze/`, follow these steps:
1. **Backend URL Configuration:**
- Ensure that `core/urls.py` includes the `analyze/` endpoint mapped to `AnalyzeWritingSampleView`.
- Confirm that `backend/urls.py` includes `core.urls` under the `api/` path.
2. **View Definition:**
- Verify that `AnalyzeWritingSampleView` is correctly defined in `core/views.py` and properly imported in `core/urls.py`.
3. **Restart Servers:**
- Restart the Django development server to apply any changes.
- Restart the React development server if necessary.
4. **Test the Endpoint:**
- Use cURL or Postman to send a `POST` request to `/api/analyze/` and verify the response.
- Ensure that the `AnalyzeWritingSampleView` processes the request and interacts with Anthropic's API correctly.
5. **Review Frontend Configuration:**
- Ensure that the frontend is making the `POST` request to the correct endpoint (`analyze/`) relative to the Axios `baseURL`.
6. **Enhance Logging:**
- Utilize detailed logging in `utils.py` and `views.py` to trace the flow of data and identify where failures occur.
7. **Check Environment Variables:**
- Confirm that `ANTHROPIC_API_KEY` is correctly set in the `.env` file and loaded into Django.
8. **Validate Serializer and Model:**
- Ensure that the `Persona` model and `PersonaSerializer` are correctly set up to handle incoming data and store analyzed results.
By meticulously following these steps, you should be able to identify and rectify the cause of the `404 Not Found` error, ensuring that your application can successfully analyze writing samples and create personas.
If after following these steps the issue persists, please provide the updated backend logs, especially any error messages that appear when a `POST` request is made to `/api/analyze/`. This additional information will help in diagnosing the problem more precisely.
---
**Example Verification:**
1. **Backend Logs After Correcting URLs:**
```plaintext
DEBUG:core.views:Request data: {'name': 'Karamazov 2', 'writing_sample': 'rom active punishment herself...'}
DEBUG:core.utils:Sending request to Anthropic API: {...}
DEBUG:core.utils:Anthropic API response: {...}
DEBUG:core.utils:Assistant message: {...}
DEBUG:core.utils:Extracted JSON data: {...}
```
2. **Successful cURL Response:**
```json
{
"id": 1,
"name": "Karamazov 2",
"data": {
"vocabulary_complexity": 8,
"sentence_structure": "complex",
// ... other analyzed fields ...
}
}
```
3. **Frontend Success Message:**
```
Persona "Karamazov 2" created successfully!
```
By ensuring each component is correctly configured and communicating as intended, your application should function seamlessly.
o1-mini
"<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n <meta http-equiv=\"content-type\" content=\"text/html; charset=utf-8\">\n <title>Page not found at /api/analyze/</title>\n <meta name=\"robots\" content=\"NONE,NOARCHIVE\">\n <style>\n html * { padding:0; margin:0; }\n body * { padding:10px 20px; }\n body * * { padding:0; }\n body { font-family: sans-serif; background:#eee; color:#000; }\n body > :where(header, main, footer) { border-bottom:1px solid #ddd; }\n h1 { font-weight:normal; margin-bottom:.4em; }\n h1 small { font-size:60%; color:#666; font-weight:normal; }\n table { border:none; border-collapse: collapse; width:100%; }\n td, th { vertical-align:top; padding:2px 3px; }\n th { width:12em; text-align:right; color:#666; padding-right:.5em; }\n #info { background:#f6f6f6; }\n #info ol { margin: 0.5em 4em; }\n #info ol li { font-family: monospace; }\n #summary { background: #ffc; }\n #explanation { background:#eee; border-bottom: 0px none; }\n pre.exception_value { font-family: sans-serif; color: #575757; font-size: 1.5em; margin: 10px 0 10px 0; }\n </style>\n</head>\n<body>\n <header id=\"summary\">\n <h1>Page not found <small>(404)</small></h1>\n \n <table class=\"meta\">\n <tr>\n <th scope=\"row\">Request Method:</th>\n <td>POST</td>\n </tr>\n <tr>\n <th scope=\"row\">Request URL:</th>\n <td>http://localhost:8000/api/analyze/</td>\n </tr>\n \n </table>\n </header>\n\n <main id=\"info\">\n \n <p>\n Using the URLconf defined in <code>backend.urls</code>,\n Django tried these URL patterns, in this order:\n </p>\n <ol>\n \n <li>\n \n <code>\n admin/\n \n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n token/\n [name='token_obtain_pair']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n token/refresh/\n [name='token_refresh']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n ^personas/$\n [name='persona-list']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n ^personas\\.(?P<format>[a-z0-9]+)/?$\n [name='persona-list']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n ^personas/(?P<pk>[^/.]+)/$\n [name='persona-detail']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n ^personas/(?P<pk>[^/.]+)\\.(?P<format>[a-z0-9]+)/?$\n [name='persona-detail']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n ^personas/(?P<pk>[^/.]+)/generate_content/$\n [name='persona-generate-content']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n ^personas/(?P<pk>[^/.]+)/generate_content\\.(?P<format>[a-z0-9]+)/?$\n [name='persona-generate-content']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n ^content/$\n [name='content-list']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n ^content\\.(?P<format>[a-z0-9]+)/?$\n [name='content-list']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n ^content/(?P<pk>[^/.]+)/$\n [name='content-detail']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n ^content/(?P<pk>[^/.]+)\\.(?P<format>[a-z0-9]+)/?$\n [name='content-detail']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n \n [name='api-root']\n </code>\n \n </li>\n \n <li>\n \n <code>\n api/\n \n </code>\n \n <code>\n \n \n </code>\n \n <code>\n <drf_format_suffix:format>\n [name='api-root']\n </code>\n \n </li>\n \n </ol>\n <p>\n \n The current path, <code>api/analyze/</code>,\n \n didn’t match any of these.\n </p>\n \n </main>\n\n <footer id=\"explanation\">\n <p>\n You’re seeing this error because you have <code>DEBUG = True</code> in\n your Django settings file. Change that to <code>False</code>, and Django\n will display a standard 404 page.\n </p>\n </footer>\n</body>\n</html>\n"
The `404 Not Found` error you're encountering when making a `POST` request to `http://localhost:8000/api/analyze/` indicates that Django does not recognize the `/api/analyze/` endpoint. This typically means that the URL pattern for the `analyze/` endpoint is either missing or incorrectly configured in your Django project's URL configurations.
Let's systematically address this issue to ensure that your frontend can successfully communicate with the backend.
---
## Table of Contents
1. [Verify Backend URL Configuration](#1-verify-backend-url-configuration)
2. [Ensure `AnalyzeWritingSampleView` is Defined Correctly](#2-ensure-analyzewritingsampleview-is-defined-correctly)
3. [Check URL Patterns and Imports](#3-check-url-patterns-and-imports)
4. [Restart the Django Development Server](#4-restart-the-django-development-server)
5. [Test the Endpoint Independently](#5-test-the-endpoint-independently)
6. [Review Frontend Configuration](#6-review-frontend-configuration)
7. [Additional Debugging Steps](#7-additional-debugging-steps)
8. [Summary](#8-summary)
---
## 1. Verify Backend URL Configuration
**Objective:** Ensure that the `analyze/` endpoint is correctly mapped in your Django project's URL configurations.
### **a. Check `core/urls.py`**
1. **Locate `core/urls.py`:** This file should define the URL patterns specific to your `core` app.
2. **Ensure `analyze/` Path is Defined:**
Your `core/urls.py` should include a path for `analyze/` mapped to the `AnalyzeWritingSampleView`. Here's how it should look:
```python
# core/urls.py
from django.urls import path
from .views import (
AnalyzeWritingSampleView,
GenerateContentView,
PersonaListView,
PersonaDetailView,
BlogPostView
)
urlpatterns = [
path('analyze/', AnalyzeWritingSampleView.as_view(), name='analyze-writing-sample'),
path('generate-content/', GenerateContentView.as_view(), name='generate-content'),
path('personas/', PersonaListView.as_view(), name='persona-list'),
path('personas/<int:persona_id>/', PersonaDetailView.as_view(), name='persona-detail'),
path('blog-posts/', BlogPostView.as_view(), name='blogpost-list'),
]
```
**Action Items:**
- **Open `core/urls.py`:** Ensure the `analyze/` path is present and correctly mapped.
- **Correct Any Missing Paths:** If the `analyze/` path is missing, add it as shown above.
### **b. Check `backend/urls.py`**
1. **Locate `backend/urls.py`:** This file includes the URL configurations for the entire Django project.
2. **Ensure `core.urls` are Included Under `api/`:**
Your `backend/urls.py` should include the `core` app's URLs under the `api/` prefix. Here's an example:
```python
# backend/urls.py
from django.contrib import admin
from django.urls import path, include
urlpatterns = [
path('admin/', admin.site.urls),
path('api/', include('core.urls')), # Ensure this line exists
]
```
**Action Items:**
- **Open `backend/urls.py`:** Verify that the `core.urls` are included under the `api/` path.
- **Add the Include Statement if Missing:** If the line `path('api/', include('core.urls')),` is missing, add it.
---
## 2. Ensure `AnalyzeWritingSampleView` is Defined Correctly
**Objective:** Confirm that the `AnalyzeWritingSampleView` is properly defined in `core/views.py` and correctly imported in `core/urls.py`.
### **a. Check `core/views.py`**
1. **Locate `core/views.py`:**
2. **Ensure `AnalyzeWritingSampleView` is Defined:**
Here's how the view should be defined:
```python
# core/views.py
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from .serializers import PersonaSerializer
from .utils import generate_content
import logging
logger = logging.getLogger(__name__)
class AnalyzeWritingSampleView(APIView):
def post(self, request, *args, **kwargs):
logger.debug(f"Request data: {request.data}")
serializer = PersonaSerializer(data=request.data)
if serializer.is_valid():
persona = serializer.save()
return Response(PersonaSerializer(persona).data, status=status.HTTP_201_CREATED)
else:
logger.error(f"Serializer validation failed: {serializer.errors}")
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
```
**Action Items:**
- **Open `core/views.py`:** Ensure that `AnalyzeWritingSampleView` is defined as shown.
- **Verify Imports:** Confirm that all necessary modules (`APIView`, `Response`, `status`, `PersonaSerializer`, `generate_content`, `logging`) are correctly imported.
- **Check for Typographical Errors:** Ensure there are no typos in the view name or method definitions.
---
## 3. Check URL Patterns and Imports
**Objective:** Confirm that URL patterns in `core/urls.py` are correctly imported and mapped to their respective views.
### **a. Verify Imports in `core/urls.py`**
1. **Open `core/urls.py`:**
2. **Ensure Correct Imports:**
```python
# core/urls.py
from django.urls import path
from .views import (
AnalyzeWritingSampleView,
GenerateContentView,
PersonaListView,
PersonaDetailView,
BlogPostView
)
urlpatterns = [
path('analyze/', AnalyzeWritingSampleView.as_view(), name='analyze-writing-sample'),
path('generate-content/', GenerateContentView.as_view(), name='generate-content'),
path('personas/', PersonaListView.as_view(), name='persona-list'),
path('personas/<int:persona_id>/', PersonaDetailView.as_view(), name='persona-detail'),
path('blog-posts/', BlogPostView.as_view(), name='blogpost-list'),
]
```
**Action Items:**
- **Ensure All Views are Imported:** Confirm that `AnalyzeWritingSampleView` and other views are correctly imported from `core/views.py`.
- **Check for Misspellings:** Ensure that the view names match exactly between `views.py` and `urls.py`.
---
## 4. Restart the Django Development Server
**Objective:** Apply any changes made to the URL configurations by restarting the Django development server.
### **Steps:**
1. **Stop the Server:**
If the server is running, stop it by pressing `CTRL + C` in the terminal where it's running.
2. **Start the Server Again:**
```bash
python manage.py runserver
```
3. **Monitor Startup Logs:**
Ensure there are no errors related to URL configurations or view definitions during startup.
---
## 5. Test the Endpoint Independently
**Objective:** Verify that the `/api/analyze/` endpoint is accessible and functioning correctly without involving the frontend.
### **a. Using cURL**
1. **Open a Terminal:**
2. **Execute the Following Command:**
```bash
curl -X POST http://localhost:8000/api/analyze/ \
-H "Content-Type: application/json" \
-d '{
"name": "Karamazov 2",
"writing_sample": "rom active punishment herself, as the sinner is to…is heart. Tears positively glittered in his eyes."
}'
```
3. **Interpret the Response:**
- **Success (`201 Created`):** You should receive a JSON response with the created `Persona` data.
- **Failure (`400 Bad Request` or `404 Not Found`):** Indicates issues with the request payload or endpoint configuration.
### **b. Using Postman**
1. **Open Postman.**
2. **Create a New `POST` Request:**
- **URL:** `http://localhost:8000/api/analyze/`
- **Headers:**
- `Content-Type: application/json`
- **Body:**
```json
{
"name": "Karamazov 2",
"writing_sample": "rom active punishment herself, as the sinner is to…is heart. Tears positively glittered in his eyes."
}
```
3. **Send the Request and Review the Response:**
- **Success (`201 Created`):** JSON data of the created `Persona`.
- **Failure (`400 Bad Request` or `404 Not Found`):** Error messages indicating what went wrong.
---
## 6. Review Frontend Configuration
**Objective:** Ensure that the frontend is correctly configured to send requests to the `/api/analyze/` endpoint.
### **a. Check Axios Configuration**
1. **Locate `axiosConfig.js` or `axiosConfig.ts`:**
Ensure that the `baseURL` is set correctly.
```javascript
// src/axiosConfig.js or src/axiosConfig.ts
import axios from 'axios';
const instance = axios.create({
baseURL: 'http://localhost:8000/api/', // Ensure this matches your backend
});
export default instance;
```
### **b. Verify `UploadSample.tsx`**
1. **Open `UploadSample.tsx`:**
2. **Ensure Correct Endpoint is Being Used:**
The `POST` request should be directed to `analyze/` relative to the `baseURL`.
```typescript
// src/components/GhostWriter/UploadSample.tsx
import React, { useState } from 'react';
import axios from '../axiosConfig'; // Ensure this path is correct
import { Box, Button, TextField, Typography, Alert, Stack } from '@mui/material';
const UploadSample: React.FC = () => {
const [name, setName] = useState('');
const [writingSample, setWritingSample] = useState('');
const [error, setError] = useState<string | null>(null);
const [success, setSuccess] = useState<string | null>(null);
const handleSubmit = async (event: React.FormEvent) => {
event.preventDefault();
const payload = {
name: name.trim(),
writing_sample: writingSample.trim(),
};
try {
console.log('Payload being sent:', payload);
const response = await axios.post('analyze/', payload); // Correct endpoint
console.log('Response received:', response.data);
setSuccess(`Persona "${response.data.name}" created successfully!`);
setError(null);
setName('');
setWritingSample('');
} catch (error: any) {
console.error('Error uploading writing sample:', error);
console.log('Error response:', error.response);
if (error.response && error.response.data) {
setError(JSON.stringify(error.response.data));
} else {
setError('An error occurred while uploading the writing sample.');
}
setSuccess(null);
}
};
return (
<Box p={4} maxWidth="600px" mx="auto">
<Typography variant="h4" gutterBottom>
Upload Writing Sample
</Typography>
{error && (
<Alert severity="error" sx={{ mb: 2 }}>
{error}
</Alert>
)}
{success && (
<Alert severity="success" sx={{ mb: 2 }}>
{success}
</Alert>
)}
<form onSubmit={handleSubmit}>
<Stack spacing={3}>
<TextField
label="Persona Name"
variant="outlined"
fullWidth
value={name}
onChange={(e) => setName(e.target.value)}
required
inputProps={{ maxLength: 100 }}
/>
<TextField
label="Writing Sample"
variant="outlined"
fullWidth
multiline
rows={6}
value={writingSample}
onChange={(e) => setWritingSample(e.target.value)}
required
/>
<Button type="submit" variant="contained" color="primary" size="large">
Submit
</Button>
</Stack>
</form>
</Box>
);
};
export default UploadSample;
```
**Key Points:**
- **Endpoint Accuracy:** Ensure that `axios.post('analyze/', payload)` aligns with the backend's URL configuration.
- **Correct Path:** Given that the `baseURL` is `http://localhost:8000/api/`, the full endpoint becomes `http://localhost:8000/api/analyze/`.
### **c. Verify React Development Server**
1. **Ensure React Server is Running:**
```bash
npm start
# or
yarn start
```
2. **Monitor Browser Console and Network Tab:**
- **Console:** Look for any errors or logs that can provide insights.
- **Network:** Check the `POST` request to `http://localhost:8000/api/analyze/` and its response.
---
## 7. Additional Debugging Steps
If after following the above steps the issue persists, consider the following additional debugging measures.
### **a. List All URL Patterns**
**Objective:** Confirm that the `/api/analyze/` endpoint exists in Django's URL patterns.
1. **Use Django Extensions' `show_urls` Command:**
- **Install `django-extensions`:**
```bash
pip install django-extensions
```
- **Add to `INSTALLED_APPS` in `backend/settings.py`:**
```python
INSTALLED_APPS = [
# ... other apps ...
'django_extensions',
]
```
- **Run the `show_urls` Command:**
```bash
python manage.py show_urls
```
- **Review the Output:** Look for the `/api/analyze/` endpoint and ensure it is mapped correctly.
**Note:** If you don't want to install `django-extensions`, you can manually inspect `core/urls.py` and `backend/urls.py` as previously described.
### **b. Create a Test View**
**Objective:** Verify that URL routing is functioning correctly by creating a simple test view.
1. **Define a Test View in `core/views.py`:**
```python
# core/views.py
from rest_framework.views import APIView
from rest_framework.response import Response
class TestAnalyzeView(APIView):
def get(self, request):
return Response({"message": "Analyze endpoint is working."})
```
2. **Map the Test View in `core/urls.py`:**
```python
# core/urls.py
from django.urls import path
from .views import (
AnalyzeWritingSampleView,
GenerateContentView,
PersonaListView,
PersonaDetailView,
BlogPostView,
TestAnalyzeView # Import the test view
)
urlpatterns = [
path('analyze/', AnalyzeWritingSampleView.as_view(), name='analyze-writing-sample'),
path('test-analyze/', TestAnalyzeView.as_view(), name='test-analyze'), # New test endpoint
path('generate-content/', GenerateContentView.as_view(), name='generate-content'),
path('personas/', PersonaListView.as_view(), name='persona-list'),
path('personas/<int:persona_id>/', PersonaDetailView.as_view(), name='persona-detail'),
path('blog-posts/', BlogPostView.as_view(), name='blogpost-list'),
]
```
3. **Restart the Django Server:**
```bash
python manage.py runserver
```
4. **Access the Test Endpoint:**
- **Browser:** Navigate to `http://localhost:8000/api/test-analyze/`.
- **Expected Response:**
```json
{
"message": "Analyze endpoint is working."
}
```
5. **Interpret the Results:**
- **Success:** Indicates that URL routing is functioning correctly.
- **Failure:** Suggests issues with URL configurations or view definitions.
6. **Remove the Test View After Verification:** To maintain code cleanliness, remove the `TestAnalyzeView` and its URL mapping once verification is complete.
### **c. Inspect Django Server Logs**
**Objective:** Gain insights into what happens when a request is made to `/api/analyze/`.
1. **Monitor Server Logs During a `POST` Request:**
After making a `POST` request to `/api/analyze/` (either via frontend, cURL, or Postman), observe the server logs for any error messages or debug statements.
2. **Look for Specific Log Entries:**
- **Request Data:** Should show the data being received.
- **Anthropic API Interaction:** Logs detailing the request sent to Anthropic and the response received.
- **JSON Parsing:** Logs indicating whether JSON extraction and parsing were successful.
**Example Log Entries:**
```plaintext
DEBUG:core.views:Request data: {'name': 'Karamazov 2', 'writing_sample': 'rom active punishment herself...'}
DEBUG:core.utils:Sending request to Anthropic API: {...}
DEBUG:core.utils:Anthropic API response: {...}
DEBUG:core.utils:Assistant message: {...}
DEBUG:core.utils:Extracted JSON data: {...}
```
3. **Identify Any Errors:**
- **Missing Fields:** Errors related to missing or malformed fields in the request.
- **Anthropic API Failures:** Issues with the request sent to Anthropic's API.
- **JSON Decoding Issues:** Problems parsing the JSON response.
### **d. Verify Anthropic API Key and Permissions**
**Objective:** Ensure that your Anthropic API key is valid and has the necessary permissions.
1. **Check API Key in `.env`:**
Ensure your `.env` file contains the correct API key:
```env
ANTHROPIC_API_KEY=your_valid_anthropic_api_key_here
```
2. **Test API Key Independently:**
Use cURL or Postman to send a request to Anthropic's API.
**Using cURL:**
```bash
curl -X POST https://api.anthropic.com/v1/complete \
-H "Content-Type: application/json" \
-H "X-API-Key: your_anthropic_api_key_here" \
-d '{
"prompt": "Test prompt",
"model": "claude-2",
"max_tokens_to_sample": 100,
"temperature": 0.0,
"stop_sequences": ["\n\n"]
}'
```
**Expected Response:**
A JSON object containing the `completion` field with the AI's response.
3. **Troubleshoot API Key Issues:**
- **Invalid API Key:** Ensure the API key is correct and active.
- **Insufficient Permissions:** Verify that the API key has access to the required endpoints.
- **Rate Limits:** Ensure you're not exceeding Anthropic's rate limits.
### **e. Review Serializer and Model Definitions**
**Objective:** Ensure that your Django models and serializers are correctly set up to handle incoming data and store analyzed results.
1. **Check `core/models.py`:**
```python
# core/models.py
from django.db import models
class Persona(models.Model):
name = models.CharField(max_length=100, blank=True, null=True)
data = models.JSONField(blank=True, null=True) # Stores analyzed writing sample data
def __str__(self):
return self.name
```
**Action Items:**
- **Ensure Fields are Correct:** `name` as `CharField` and `data` as `JSONField`.
- **Apply Migrations:** If you've made changes to models, run migrations.
```bash
python manage.py makemigrations
python manage.py migrate
```
2. **Check `core/serializers.py`:**
```python
# core/serializers.py
from rest_framework import serializers
from .models import Persona
from .utils import analyze_writing_sample
import logging
logger = logging.getLogger(__name__)
class PersonaSerializer(serializers.ModelSerializer):
writing_sample = serializers.CharField(write_only=True)
class Meta:
model = Persona
fields = ['id', 'name', 'writing_sample', 'data']
read_only_fields = ['id', 'data']
def create(self, validated_data):
writing_sample = validated_data.pop('writing_sample')
logger.debug(f"Writing sample received: {writing_sample[:100]}...") # Log first 100 characters
# Analyze the writing sample
analyzed_data = analyze_writing_sample(writing_sample)
logger.debug(f"Analyzed data: {analyzed_data}")
if not analyzed_data:
logger.error("analyze_writing_sample returned empty or invalid data.")
raise serializers.ValidationError({"writing_sample": "Failed to analyze the writing sample."})
# Store the analyzed data
validated_data['data'] = analyzed_data
# Save persona
return Persona.objects.create(**validated_data)
```
**Action Items:**
- **Ensure Correct Fields:** The serializer should handle `name`, `writing_sample` (write-only), and `data` (read-only).
- **Handle Validation Errors:** The `create` method should raise appropriate errors if analysis fails.
---
## 8. Final Testing
After ensuring all configurations are correct, perform a comprehensive test to verify that the `/api/analyze/` endpoint is functioning as expected.
### **a. Start the Django Server**
```bash
python manage.py runserver
```
### **b. Start the React Development Server**
```bash
npm start
# or
yarn start
```
### **c. Use Frontend to Submit a Writing Sample**
1. **Navigate to the Upload Sample Page:**
Access the page in your React application where you can upload a writing sample.
2. **Fill Out the Form:**
- **Persona Name:** Enter a name (e.g., "Karamazov 2").
- **Writing Sample:** Enter a writing sample.
3. **Submit the Form:**
Click the "Submit" button.
### **d. Monitor Logs and Responses**
1. **Frontend Console:**
- **Success:** You should see a success message indicating that the persona was created.
- **Failure:** Any errors should be displayed as alerts.
2. **Django Server Logs:**
- **Successful Request:**
```plaintext
DEBUG:core.views:Request data: {'name': 'Karamazov 2', 'writing_sample': 'rom active punishment herself...'}
DEBUG:core.utils:Sending request to Anthropic API: {...}
DEBUG:core.utils:Anthropic API response: {...}
DEBUG:core.utils:Assistant message: {...}
DEBUG:core.utils:Extracted JSON data: {...}
```
- **Failed Request:**
```plaintext
ERROR:core.utils:JSON decoding failed: Expecting value: line 1 column 1 (char 0)
ERROR:core.serializers:analyze_writing_sample returned empty or invalid data.
ERROR:core.views:Serializer validation failed: {'writing_sample': ['Failed to analyze the writing sample.']}
```
3. **Verify Persona Creation:**
- **Frontend:** A success alert should indicate the creation.
- **Backend:** The new `Persona` should be saved in the database with the analyzed data.
### **e. Test Independently Using cURL**
1. **Send a `POST` Request:**
```bash
curl -X POST http://localhost:8000/api/analyze/ \
-H "Content-Type: application/json" \
-d '{
"name": "Karamazov 2",
"writing_sample": "rom active punishment herself, as the sinner is to…is heart. Tears positively glittered in his eyes."
}'
```
2. **Expected Response:**
- **Success (`201 Created`):** JSON data of the created `Persona`.
- **Failure (`400 Bad Request`):** Error messages indicating what went wrong.
---
## 9. Summary
To resolve the `404 Not Found` error when posting to `/api/analyze/`, follow these steps:
1. **Backend URL Configuration:**
- **Ensure `core/urls.py` includes the `analyze/` endpoint.**
- **Confirm that `backend/urls.py` includes `core.urls` under the `api/` path.**
2. **View Definition:**
- **Verify that `AnalyzeWritingSampleView` is correctly defined in `core/views.py`.**
- **Ensure all necessary imports are present and correctly spelled.**
3. **Restart Servers:**
- **Restart the Django development server to apply URL configuration changes.**
- **Restart the React development server if necessary.**
4. **Test the Endpoint:**
- **Use cURL or Postman to send a `POST` request to `/api/analyze/` and verify the response.**
- **Ensure that the `AnalyzeWritingSampleView` processes the request and interacts with Anthropic's API correctly.**
5. **Review Frontend Configuration:**
- **Ensure that the frontend is making the `POST` request to the correct endpoint (`analyze/`) relative to the Axios `baseURL`.**
6. **Enhance Logging:**
- **Utilize detailed logging in `utils.py` and `views.py` to trace the flow of data and identify where failures occur.**
7. **Check Environment Variables:**
- **Confirm that `ANTHROPIC_API_KEY` is correctly set in the `.env` file and loaded into Django.**
8. **Validate Serializer and Model:**
- **Ensure that the `Persona` model and `PersonaSerializer` are correctly set up to handle incoming data and store analyzed results.**
By meticulously following these steps, you should be able to identify and rectify the cause of the `404 Not Found` error, ensuring that your application can successfully analyze writing samples and create personas.
---
**If the Issue Persists:**
Please provide the updated server logs after making these changes, especially any debug or error messages related to the `/api/analyze/` endpoint. This additional information will help in diagnosing the problem more precisely.
---
**Example Correct Setup:**
1. **`core/urls.py`:**
```python
# core/urls.py
from django.urls import path
from .views import (
AnalyzeWritingSampleView,
GenerateContentView,
PersonaListView,
PersonaDetailView,
BlogPostView
)
urlpatterns = [
path('analyze/', AnalyzeWritingSampleView.as_view(), name='analyze-writing-sample'),
path('generate-content/', GenerateContentView.as_view(), name='generate-content'),
path('personas/', PersonaListView.as_view(), name='persona-list'),
path('personas/<int:persona_id>/', PersonaDetailView.as_view(), name='persona-detail'),
path('blog-posts/', BlogPostView.as_view(), name='blogpost-list'),
]
```
2. **`backend/urls.py`:**
```python
# backend/urls.py
from django.contrib import admin
from django.urls import path, include
urlpatterns = [
path('admin/', admin.site.urls),
path('api/', include('core.urls')), # Ensure this line exists
]
```
3. **`core/views.py`:**
```python
# core/views.py
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from .serializers import PersonaSerializer
from .utils import generate_content
import logging
logger = logging.getLogger(__name__)
class AnalyzeWritingSampleView(APIView):
def post(self, request, *args, **kwargs):
logger.debug(f"Request data: {request.data}")
serializer = PersonaSerializer(data=request.data)
if serializer.is_valid():
persona = serializer.save()
return Response(PersonaSerializer(persona).data, status=status.HTTP_201_CREATED)
else:
logger.error(f"Serializer validation failed: {serializer.errors}")
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
```
4. **`core/serializers.py`:**
```python
# core/serializers.py
from rest_framework import serializers
from .models import Persona
from .utils import analyze_writing_sample
import logging
logger = logging.getLogger(__name__)
class PersonaSerializer(serializers.ModelSerializer):
writing_sample = serializers.CharField(write_only=True)
class Meta:
model = Persona
fields = ['id', 'name', 'writing_sample', 'data']
read_only_fields = ['id', 'data']
def create(self, validated_data):
writing_sample = validated_data.pop('writing_sample')
logger.debug(f"Writing sample received: {writing_sample[:100]}...") # Log first 100 characters
# Analyze the writing sample
analyzed_data = analyze_writing_sample(writing_sample)
logger.debug(f"Analyzed data: {analyzed_data}")
if not analyzed_data:
logger.error("analyze_writing_sample returned empty or invalid data.")
raise serializers.ValidationError({"writing_sample": "Failed to analyze the writing sample."})
# Store the analyzed data
validated_data['data'] = analyzed_data
# Save persona
return Persona.objects.create(**validated_data)
```
5. **`core/utils.py`:**
Ensure that `analyze_writing_sample` is correctly implemented and communicates with Anthropic's API as per earlier instructions.
---
By ensuring that all these components are correctly configured and interconnected, your application should successfully handle `POST` requests to `/api/analyze/`, analyze the writing samples using the Anthropic API, and create new personas accordingly.
If you continue to experience issues, please share the updated server logs and any additional error messages you encounter for further assistance.
o1-mini