Narrative Generation Setup

2025-01-2316 turns49,345 charsgpt-4o-mini

Summary

User is debugging a FastAPI 500 error in their narrative generation backend application.

Messages

--- /Users/danielkliewer/ITB02/requirements.txt ---
fastapi
uvicorn
ollama
chromadb
langchain
pydantic
pillow
requests
react
python-multipart
celery
redis

--- /Users/danielkliewer/ITB02/combined_output.txt ---


--- /Users/danielkliewer/ITB02/quantization.py ---
# quantization.py
import os


from llama_cpp import Llama

llm = Llama(
    model_path="deepseek-70b.Q4_K_M.gguf",
    n_ctx=4096,
    n_gpu_layers=40
)

--- /Users/danielkliewer/ITB02/setup.py ---
from setuptools import setup, find_packages

setup(
    name="narrative-generator",
    version="0.1.0",
    packages=find_packages(),
    install_requires=[
        "fastapi",
        "uvicorn",
        "ollama",
        "chromadb",
        "langchain",
        "pydantic",
        "pillow",
        "requests"
    ]
)

--- /Users/danielkliewer/ITB02/.dockerignore ---


--- /Users/danielkliewer/ITB02/combine_files.py ---
import os

def combine_files_in_directory(output_file="combined_output.txt", ignore_dirs=None):
    """
    Combines all files in the current directory (recursively) into a single output file.
    The file names are recorded before their contents.
    Directories in `ignore_dirs` will be skipped.
    """
    if ignore_dirs is None:
        ignore_dirs = ["venv"]  # Default to ignoring 'venv'

    with open(output_file, "w", encoding="utf-8") as outfile:
        for root, dirs, files in os.walk(os.getcwd()):
            # Modify the dirs list in-place to skip ignored directories
            dirs[:] = [d for d in dirs if d not in ignore_dirs]
            
            for file in files:
                file_path = os.path.join(root, file)
                try:
                    with open(file_path, "r", encoding="utf-8") as infile:
                        # Write the file name and a separator
                        outfile.write(f"--- {file_path} ---\n")
                        # Write the file content
                        outfile.write(infile.read())
                        outfile.write("\n\n")
                except Exception as e:
                    # Log an error if a file couldn't be read
                    outfile.write(f"--- {file_path} (ERROR: {e}) ---\n\n")

if __name__ == "__main__":
    combine_files_in_directory()

--- /Users/danielkliewer/ITB02/.env ---

APP_ENV=development
DEBUG=False

BACKEND_HOST=0.0.0.0
BACKEND_PORT=8000

OLLAMA_MODEL=deepseek-llm:70b
OLLAMA_HOST=http://ollama:11434

REDIS_HOST=redis
REDIS_PORT=6379

CELERY_BROKER_URL=redis://redis:6379/0
CELERY_RESULT_BACKEND=redis://redis:6379/0

CHROMA_DB_PATH=/app/data/chroma_db
UPLOAD_DIR=/app/uploads

--- /Users/danielkliewer/ITB02/.gitattributes ---
# Auto detect text files and perform LF normalization
* text=auto


--- /Users/danielkliewer/ITB02/docker-compose.yml ---
version: '3.8'

services:
  backend:
    build: 
      context: .
      dockerfile: Dockerfile
    ports:
      - "8000:8000"
    volumes:
      - ./backend:/app/backend
      - ./data:/app/data
      - ./uploads:/app/uploads
    env_file:
      - .env
    depends_on:
      - redis
      - ollama
    networks:
      - app_network

  redis:
    image: redis:alpine
    volumes:
      - redis_data:/data
    networks:
      - app_network

  ollama:
    image: ollama/ollama
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    networks:
      - app_network

  frontend:
    build: 
      context: ./frontend
      dockerfile: Dockerfile
    ports:
      - "3000:3000"
    depends_on:
      - backend
    networks:
      - app_network

networks:
  app_network:
    driver: bridge

volumes:
  ollama_data:
  redis_data:
  upload_data:

--- /Users/danielkliewer/ITB02/main.py ---
from fastapi import FastAPI
from backend.api.routers.story import router as story_router

app = FastAPI()
app.include_router(story_router, prefix="/story")

--- /Users/danielkliewer/ITB02/test_rag.py ---
# test_rag.py
def test_retrieval_relevance():
    rag = NarrativeRAG()
    rag.index_context("Test document", {"test": True})
    results = rag.retrieve_context("test query")
    assert len(results) == 1
    assert "Test document" in results

--- /Users/danielkliewer/ITB02/image.jpg ---
--- /Users/danielkliewer/ITB02/image.jpg (ERROR: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte) ---

--- /Users/danielkliewer/ITB02/validation.py ---
# validation.py
from pydantic import BaseModel, validator

class ChapterValidation(BaseModel):
    content: str
    mood_score: float
    conflict_count: int
    
    @validator('mood_score')
    def check_mood_consistency(cls, v):
        if v < 0.7:
            raise ValueError("Mood consistency too low")
        return v

--- /Users/danielkliewer/ITB02/frontend/Dockerfile ---
# Frontend Dockerfile
FROM node:18-alpine

WORKDIR /app

# Install dependencies
COPY frontend/package*.json ./
RUN npm install

# Copy frontend source
COPY frontend/ ./

# Build the application
RUN npm run build

# Expose port
EXPOSE 3000

# Start command
CMD ["npm", "start"]

--- /Users/danielkliewer/ITB02/frontend/src/stores/useStore.js ---
// frontend/src/stores/useStore.js
import create from 'zustand';

export const useStore = create((set) => ({
  nodes: [],
  edges: [],
  addNode: (node) => set((state) => ({ nodes: [...state.nodes, node] })),
  addEdge: (edge) => set((state) => ({ edges: [...state.edges, edge] })),
  setStory: (story) => {
    const nodes = story.map((chapter, index) => ({
      id: `chapter-${index}`,
      type: 'default',
      data: { label: `Chapter ${index + 1}` },
      position: { x: index * 250, y: 0 }
    }));
    
    const edges = nodes.slice(0, -1).map((node, index) => ({
      id: `edge-${index}`,
      source: node.id,
      target: nodes[index + 1].id
    }));

    set({ nodes, edges });
  }
}));

--- /Users/danielkliewer/ITB02/frontend/src/components/StoryEditor.jsx ---
// story_editor.jsx
import ReactFlow, { Controls } from 'reactflow';
import { useStore } from './store';

export default function NarrativeGraph() {
  const nodes = useStore(state => state.nodes);
  const edges = useStore(state => state.edges);

  return (
    <ReactFlow 
      nodes={nodes}
      edges={edges}
      fitView
    >
      <Controls />
    </ReactFlow>
  );
}


--- /Users/danielkliewer/ITB02/frontend/src/components/NarrativeGraph.jsx ---


--- /Users/danielkliewer/ITB02/backend/Dockerfile ---
# Dockerfile for backend
FROM python:3.11-slim

# Set working directory
WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    build-essential \
    curl \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy backend code
COPY backend/ ./backend
COPY utils.py .

# Install Ollama
RUN curl https://ollama.ai/install.sh | sh

# Set environment variables
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    APP_ENV=production \
    OLLAMA_HOST=http://ollama:11434

# Expose port
EXPOSE 8000

# Start command
CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "8000"]

--- /Users/danielkliewer/ITB02/backend/core/story_generation.py ---
# story_generator.py
import ollama
from .rag_manager import NarrativeRAG
from .utils import extract_keywords 

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

class StoryEngine:
    def __init__(self):
        self.llm = Ollama(model="deepseek-llm:70b")
        self.rag = NarrativeRAG()
        
    def generate_chapter(self, context):
        retrieved = self.rag.retrieve_context(context["latest_summary"])
        prompt = self._build_prompt(context, retrieved)
        
        chapter = self.llm.generate(prompt)
        self._validate_chapter(chapter)
        self._update_rag(chapter)
        
        return chapter

    def _build_prompt(self, context, retrieved):
        return f"""
        Write a 300-word story chapter continuing from:
        {context['summary']}
        
        Retrieved Context:
        {retrieved}
        
        Requirements:
        - Maintain {context['mood']} tone
        - Advance conflicts: {', '.join(context['conflicts'])}
        - End with a cliffhanger
        """

    def _validate_chapter(self, chapter):
        # Custom validation logic
        if len(chapter.split()) < 250:
            raise ValueError("Chapter too short")
            
    def _update_rag(self, chapter):
        self.rag.index_context(
            document=chapter,
            metadata={
                "chapter": context["current_chapter"],
                "keywords": extract_keywords(chapter)
            }
        )

--- /Users/danielkliewer/ITB02/backend/core/__init__.py ---


--- /Users/danielkliewer/ITB02/backend/core/image_analysis.py ---
# image_analysis.py
from pydantic import BaseModel
import requests
from PIL import Image
import io
import ollama
import os
import json

class ImageAnalysis(BaseModel):
    setting: str
    characters: list[str]
    mood: str
    objects: list[str]
    potential_conflicts: list[str]

    @classmethod
    def from_llava_response(cls, response):
        try:
            # Parse the response into structured data
            # Example response format adjustment
            return cls(
                setting=response['setting'],
                characters=response['characters'],
                mood=response['mood'],
                objects=response['objects'],
                potential_conflicts=response['conflicts']
            )
        except Exception as e:
            raise ValueError(f"Failed to parse LLaVA response: {str(e)}")

    def _analyze_with_llava(self, image_bytes):
        """Analyze image using LLaVA model"""
        try:
            response = ollama.generate(
                model="gemma2:27b",
                prompt="Analyze this image and provide: setting, characters, mood, objects, and potential conflicts",
                images=[image_bytes]
            )
            
            # Parse the response
            result = response.response
            return ImageAnalysis.from_llava_response(result)
            
        except Exception as e:
            raise Exception(f"LLaVA analysis failed: {str(e)}")


class MultimodalAnalyzer:
    def __init__(self, model="gemma2:27b"):
        self.model = model
        
    def _load_image(self, image_source):
        """Load image from file path or URL"""
        try:
            if isinstance(image_source, str):
                if image_source.startswith(('http://', 'https://')):
                    # Load from URL
                    response = requests.get(image_source)
                    response.raise_for_status()
                    return response.content
                else:
                    # Load from local file
                    if not os.path.exists(image_source):
                        raise FileNotFoundError(f"Image file not found: {image_source}")
                    with open(image_source, 'rb') as f:
                        return f.read()
            elif isinstance(image_source, bytes):
                return image_source
            else:
                raise ValueError("Image source must be a file path, URL, or bytes")
                
        except Exception as e:
            raise Exception(f"Failed to load image: {str(e)}")

    def analyze(self, image_source):
        image_bytes = self._load_image(image_source)
        if self.model == "gemma2:27b":
            return self._analyze_with_llava(image_bytes)
        else:
            raise ValueError(f"Unsupported model: {self.model}")

    def _analyze_with_llava(self, image_bytes):
        """Analyze image using LLaVA model"""
        try:
            response = ollama.generate(
                model="gemma2:27b",
                prompt="""Analyze this image and provide:
                - setting: where the scene takes place
                - characters: who appears in the image
                - mood: the emotional tone
                - objects: key items visible
                - conflicts: potential story conflicts
                Format as JSON.""",
                images=[image_bytes]
            )
            print(f"Raw response: {response.response}")
            # Parse the response into a dictionary
            result = json.loads(response.response)
            
            return ImageAnalysis.from_llava_response(result)
            
        except Exception as e:
            raise Exception(f"LLaVA analysis failed: {str(e)}")
        
    def _load_image(self, image_source):
        """Load image from file path or URL"""
        try:
            if isinstance(image_source, str):
                if image_source.startswith(('http://', 'https://')):
                    # Load from URL
                    response = requests.get(image_source)
                    response.raise_for_status()
                    return response.content
                else:
                    # Load from local file
                    if not os.path.exists(image_source):
                        raise FileNotFoundError(f"Image file not found: {image_source}")
                    with open(image_source, 'rb') as f:
                        return f.read()
            elif isinstance(image_source, bytes):
                return image_source
            else:
                raise ValueError("Image source must be a file path, URL, or bytes")
                
        except Exception as e:
            raise Exception(f"Failed to load image: {str(e)}")

    def analyze(self, image_source):
        image_bytes = self._load_image(image_source)
        if self.model == "gemma2:27b":
            return self._analyze_with_llava(image_bytes)
        else:
            raise ValueError(f"Unsupported model: {self.model}")

def _analyze_with_llava(self, image_bytes):
    try:
        response = ollama.generate(
            model="gemma2:27b",
            prompt="""Analyze this image and provide:
            - setting: where the scene takes place
            - characters: who appears in the image
            - mood: the emotional tone
            - objects: key items visible
            - conflicts: potential story conflicts
            Format as JSON.""",
            images=[image_bytes],
            stream=False
        )
        # Print or log the raw response
        print(f"Raw response from Ollama: {response}")

        # Ensure the response is parsed as JSON
        result = json.loads(response.response)  # Convert JSON string to dictionary
        print(f"Parsed JSON: {result}")  # Debugging statement
        
        return ImageAnalysis.from_llava_response(result)

    except Exception as e:
        raise Exception(f"LLaVA analysis failed: {str(e)}")
            

--- /Users/danielkliewer/ITB02/backend/core/utils.py ---
# utils.py
import re
from typing import List

def extract_keywords(text: str) -> List[str]:
    """
    Extract key words from text using simple techniques
    """
    # Remove punctuation and convert to lowercase
    text = re.sub(r'[^\w\s]', '', text.lower())
    
    # Split into words and remove common stop words
    stop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at'}
    words = [word for word in text.split() if word not in stop_words]
    
    # Return top 5 most frequent words
    word_freq = {}
    for word in words:
        word_freq[word] = word_freq.get(word, 0) + 1
    
    return sorted(word_freq, key=word_freq.get, reverse=True)[:5]

--- /Users/danielkliewer/ITB02/backend/core/pipeline.py ---
# pipeline.py
from .image_analysis import MultimodalAnalyzer
import ollama
from .story_generator import StoryEngine  # Dot indicates same directory
from .rag_manager import NarrativeRAG

class NarrativePipeline:
    def run(self, image_path):
        try:
            # Step 1: Image Analysis
            analyzer = MultimodalAnalyzer()
            analysis = analyzer.analyze(image_path)
            
            # Step 2: Initialize RAG
            rag = NarrativeRAG()
            rag.index_context(
                document=analysis.json(),
                metadata={"type": "initial_analysis"}
            )

            # Step 3: Generate Story
            story = []
            summary = ""
            for chapter_num in range(1, 6):
                context = {
                    "current_chapter": chapter_num,
                    "summary": summary,
                    "mood": analysis.mood,
                    "conflicts": analysis.potential_conflicts
                }
                
                chapter = StoryEngine().generate_chapter(context)
                story.append(chapter)
                
                if chapter_num % 5 == 0:
                    summary = self._summarize_story(story[-5:])
            
            return story
        except Exception as e:
            raise Exception(f"Pipeline failed: {str(e)}")

def _summarize_story(self, chapters):
    summary_prompt = "Summarize this story arc in 3 sentences:"
    return ollama.generate(
        model="gemma2:27b",
        prompt=summary_prompt + "\n".join(chapters)
    )

--- /Users/danielkliewer/ITB02/backend/core/rag_manager.py ---
# rag_manager.py
import uuid
import json

import chromadb
from langchain.text_splitter import RecursiveCharacterTextSplitter

class NarrativeRAG:
    def __init__(self):
        self.client = chromadb.PersistentClient(path="./chroma_db")
        self.collection = self.client.get_or_create_collection("narrative")
        self.text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=500,
            chunk_overlap=50
        )

    def index_context(self, document: dict, metadata: dict):
        chunks = self.text_splitter.split_text(document)
        ids = [str(uuid.uuid4()) for _ in chunks]
        self.collection.add(
            documents=chunks,
            metadatas=[metadata]*len(chunks),
            ids=ids
        )

    def retrieve_context(self, query, k=3):
        results = self.collection.query(
            query_texts=[query],
            n_results=k
        )
        return [doc for doc in results['documents'][0]]

--- /Users/danielkliewer/ITB02/backend/core/story_generator.py ---
# story_generator.py
import ollama
from .rag_manager import NarrativeRAG
from .utils import extract_keywords 

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

class StoryEngine:
    def __init__(self):
        self.llm = Ollama(model="deepseek-llm:70b")
        self.rag = NarrativeRAG()
        
    def generate_chapter(self, context):
        retrieved = self.rag.retrieve_context(context["latest_summary"])
        prompt = self._build_prompt(context, retrieved)
        
        chapter = self.llm.generate(prompt)
        self._validate_chapter(chapter)
        self._update_rag(chapter)
        
        return chapter

    def _build_prompt(self, context, retrieved):
        return f"""
        Write a 300-word story chapter continuing from:
        {context['summary']}
        
        Retrieved Context:
        {retrieved}
        
        Requirements:
        - Maintain {context['mood']} tone
        - Advance conflicts: {', '.join(context['conflicts'])}
        - End with a cliffhanger
        """

    def _validate_chapter(self, chapter):
        # Custom validation logic
        if len(chapter.split()) < 250:
            raise ValueError("Chapter too short")
            
    def _update_rag(self, chapter):
        self.rag.index_context(
            document=chapter,
            metadata={
                "chapter": context["current_chapter"],
                "keywords": extract_keywords(chapter)
            }
        )

--- /Users/danielkliewer/ITB02/backend/core/__pycache__/pipeline.cpython-313.pyc ---
--- /Users/danielkliewer/ITB02/backend/core/__pycache__/pipeline.cpython-313.pyc (ERROR: 'utf-8' codec can't decode byte 0xf3 in position 0: invalid continuation byte) ---

--- /Users/danielkliewer/ITB02/backend/core/__pycache__/rag_manager.cpython-313.pyc ---
--- /Users/danielkliewer/ITB02/backend/core/__pycache__/rag_manager.cpython-313.pyc (ERROR: 'utf-8' codec can't decode byte 0xf3 in position 0: invalid continuation byte) ---

--- /Users/danielkliewer/ITB02/backend/core/__pycache__/utils.cpython-313.pyc ---
--- /Users/danielkliewer/ITB02/backend/core/__pycache__/utils.cpython-313.pyc (ERROR: 'utf-8' codec can't decode byte 0xf3 in position 0: invalid continuation byte) ---

--- /Users/danielkliewer/ITB02/backend/core/__pycache__/image_analysis.cpython-313.pyc ---
--- /Users/danielkliewer/ITB02/backend/core/__pycache__/image_analysis.cpython-313.pyc (ERROR: 'utf-8' codec can't decode byte 0xf3 in position 0: invalid continuation byte) ---

--- /Users/danielkliewer/ITB02/backend/core/__pycache__/story_generator.cpython-313.pyc ---
--- /Users/danielkliewer/ITB02/backend/core/__pycache__/story_generator.cpython-313.pyc (ERROR: 'utf-8' codec can't decode byte 0xf3 in position 0: invalid continuation byte) ---

--- /Users/danielkliewer/ITB02/backend/core/__pycache__/__init__.cpython-313.pyc ---
--- /Users/danielkliewer/ITB02/backend/core/__pycache__/__init__.cpython-313.pyc (ERROR: 'utf-8' codec can't decode byte 0xf3 in position 0: invalid continuation byte) ---

--- /Users/danielkliewer/ITB02/backend/api/routers/story.py ---
import os
import tempfile
from fastapi import APIRouter, File, UploadFile
from backend.core.pipeline import NarrativePipeline

router = APIRouter()

@router.post("/generate-story")
async def generate_story(image: UploadFile = File(...)):
    # Save the uploaded image to a temporary file
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(image.filename)[1]) as tmp_file:
            tmp_file.write(await image.read())
            temp_file_path = tmp_file.name  # Save the file path

        # Generate story using the saved image
        pipeline = NarrativePipeline()
        story = pipeline.run(temp_file_path)
        
        # Return the story
        return {"story": story}

    finally:
        # Clean up the temporary file
        if os.path.exists(temp_file_path):
            os.remove(temp_file_path)

It looks like you are working on a comprehensive project involving FastAPI, Ollama, image analysis, and more, with a focus on narrative generation and multimodal analysis. You’ve structured it into several components, including Docker, FastAPI routes, and a React frontend.

Do you need help with anything specific from this setup? Perhaps integrating a feature, debugging a specific part, or enhancing certain aspects?

gpt-4o-mini

It does not run yet

It looks like you've set up quite a complex system, but it seems like there might be an issue preventing it from running. Here are some steps you can take to debug:

1. **Check Docker Build Logs**: Start by building the Docker containers to see if there are any errors during the process. Run:

   ```bash
   docker-compose build
   ```

   This will show any issues during the build phase, such as missing dependencies or errors in your `Dockerfile`.

2. **Check FastAPI Logs**: After you've started the containers, check the logs for your backend service to see if any errors are thrown when trying to access the FastAPI app:

   ```bash
   docker-compose up
   ```

   Look for any errors related to the FastAPI app not starting.

3. **Database and Dependencies**: Ensure that Redis and Ollama are accessible to the backend service. Since you're using Docker Compose, the services should be able to communicate, but if there are issues with the connection, this could cause errors.

4. **Configuration Check**: Double-check your `.env` file to ensure all the environment variables are set up correctly. For example, check that `OLLAMA_HOST`, `REDIS_HOST`, and other dependencies are correctly configured to use the appropriate service names and ports in Docker.

5. **Error Handling in the Backend**: In your `main.py` or `story_generation.py` files, make sure you've included proper error handling for any failures in making API calls, like with `ollama.generate()` or `redis`. This will help pinpoint where the problem occurs.

6. **Logs for Missing Files or Configurations**: Based on the contents of `combine_files_in_directory()`, make sure the file paths you're accessing exist or aren't being skipped due to misconfiguration.

Let me know if you see any specific errors or logs that could provide more context, and we can troubleshoot further!

gpt-4o-mini

INFO:     127.0.0.1:65353 - "POST /story/generate-story HTTP/1.1" 500 Internal Server Error
ERROR:    Exception in ASGI application
Traceback (most recent call last):
  File "/Users/danielkliewer/ITB02/backend/core/image_analysis.py", line 99, in _analyze_with_llava
    result = json.loads(response.response)
  File "/opt/homebrew/Cellar/python@3.13/3.13.1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/json/__init__.py", line 346, in loads
    return _default_decoder.decode(s)
           ~~~~~~~~~~~~~~~~~~~~~~~^^^
  File "/opt/homebrew/Cellar/python@3.13/3.13.1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/json/decoder.py", line 345, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
               ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/homebrew/Cellar/python@3.13/3.13.1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/json/decoder.py", line 363, in raw_decode
    raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/danielkliewer/ITB02/backend/core/pipeline.py", line 12, in run
    analysis = analyzer.analyze(image_path)
  File "/Users/danielkliewer/ITB02/backend/core/image_analysis.py", line 132, in analyze
    return self._analyze_with_llava(image_bytes)
           ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
  File "/Users/danielkliewer/ITB02/backend/core/image_analysis.py", line 104, in _analyze_with_llava
    raise Exception(f"LLaVA analysis failed: {str(e)}")
Exception: LLaVA analysis failed: Expecting value: line 1 column 1 (char 0)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/uvicorn/protocols/http/httptools_impl.py", line 409, in run_asgi
    result = await app(  # type: ignore[func-returns-value]
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        self.scope, self.receive, self.send
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/uvicorn/middleware/proxy_headers.py", line 60, in __call__
    return await self.app(scope, receive, send)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/fastapi/applications.py", line 1054, in __call__
    await super().__call__(scope, receive, send)
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/applications.py", line 112, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/middleware/errors.py", line 187, in __call__
    raise exc
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/middleware/errors.py", line 165, in __call__
    await self.app(scope, receive, _send)
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/middleware/exceptions.py", line 62, in __call__
    await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    raise exc
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
    await app(scope, receive, sender)
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/routing.py", line 715, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/routing.py", line 735, in app
    await route.handle(scope, receive, send)
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/routing.py", line 288, in handle
    await self.app(scope, receive, send)
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/routing.py", line 76, in app
    await wrap_app_handling_exceptions(app, request)(scope, receive, send)
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    raise exc
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
    await app(scope, receive, sender)
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/starlette/routing.py", line 73, in app
    response = await f(request)
               ^^^^^^^^^^^^^^^^
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/fastapi/routing.py", line 301, in app
    raw_response = await run_endpoint_function(
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    ...<3 lines>...
    )
    ^
  File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/fastapi/routing.py", line 212, in run_endpoint_function
    return await dependant.call(**values)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/danielkliewer/ITB02/backend/api/routers/story.py", line 18, in generate_story
    story = pipeline.run(temp_file_path)
  File "/Users/danielkliewer/ITB02/backend/core/pipeline.py", line 40, in run
    raise Exception(f"Pipeline failed: {str(e)}")
Exception: Pipeline failed: LLaVA analysis failed: Expecting value: line 1 column 1 (char 0)

It looks like the error originates from the `LLaVA analysis` function in the `image_analysis.py` file, specifically when it's trying to decode the response as JSON. The `JSONDecodeError: Expecting value` usually means that the response is either empty or not in a valid JSON format.

Here are some steps to help you troubleshoot:

1. **Check the Response Content**:
   The response returned by LLaVA might not be in the expected JSON format. To confirm this, you can add some debug logging before the `json.loads(response.response)` line to inspect the response:

   ```python
   print(f"Raw response: {response.response}")
   ```

   This will help you see if the response is empty or malformed.

2. **Check API/Service Availability**:
   If the LLaVA analysis relies on an external service or model, ensure that the service is running and accessible. If the service is down or there's an issue with the connection, it might return an empty response, leading to this error.

3. **Handle Empty Responses Gracefully**:
   Modify the code to handle cases where the response is empty. You can raise a more informative error or handle it without crashing the whole pipeline:

   ```python
   if not response.response:
       raise ValueError("Received empty response from LLaVA analysis.")
   result = json.loads(response.response)
   ```

4. **Verify LLaVA API Configuration**:
   Ensure that the API call to LLaVA is correctly formed and that all required parameters are being passed. If the API endpoint has changed or there's a misconfiguration in the request, it could result in an empty or invalid response.

5. **Test with a Known Valid Input**:
   If you're passing an image for analysis, try testing with a known good image to ensure that the issue isn't with the specific image you're using.

6. **Check for Timeouts or Network Issues**:
   If there's a timeout or network issue, the response might be empty. You could add retry logic to handle intermittent issues:

   ```python
   import time
   retries = 3
   for _ in range(retries):
       try:
           response = make_api_call()
           break  # Exit loop if successful
       except Exception as e:
           print(f"Error during API call: {e}")
           time.sleep(1)  # Wait before retrying
   ```

Let me know what you find in the logs, and I can help refine the solution!

gpt-4o-mini

File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/fastapi/routing.py", line 212, in run_endpoint_function
    return await dependant.call(**values)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/danielkliewer/ITB02/backend/api/routers/story.py", line 18, in generate_story
    story = pipeline.run(temp_file_path)
  File "/Users/danielkliewer/ITB02/backend/core/pipeline.py", line 40, in run
    raise Exception(f"Pipeline failed: {str(e)}")
Exception: Pipeline failed: LLaVA analysis failed: Expecting value: line 1 column 1 (char 0)

The issue is happening when the pipeline is running, specifically during the `LLaVA analysis`. The exception you're seeing indicates that the analysis failed because the response was empty or not in the expected format. Here's how you can troubleshoot:

1. **Inspect the API Response**:
   The error `Expecting value: line 1 column 1 (char 0)` means that the response you're trying to parse as JSON is empty. You can add a print or logging statement to inspect the raw response returned by LLaVA before attempting to parse it:

   In `image_analysis.py`, where the error occurs, add:

   ```python
   print(f"Raw response: {response.response}")
   ```

   This will allow you to see what is being returned and if it's empty or malformed.

2. **Check for Connection Issues**:
   If LLaVA depends on an external service or model, ensure that the service is running and accessible. A failure to connect could result in an empty response. You can test the service independently to verify that it's working.

3. **Validate Input Data**:
   Ensure that the data passed to LLaVA (e.g., an image or another input type) is valid. If the input is incorrect or malformed, it could cause the service to fail silently or return an empty response.

4. **Add Error Handling for Empty Responses**:
   Modify the code to handle empty responses gracefully. You can raise a more informative error or handle empty responses without crashing:

   ```python
   if not response.response:
       raise ValueError("Received empty response from LLaVA analysis.")
   result = json.loads(response.response)
   ```

5. **Retry Logic for Network Issues**:
   If the failure is caused by a network issue, you can add retry logic to the API call to handle intermittent problems. This will attempt to make the call again in case of a failure:

   ```python
   import time
   retries = 3
   for _ in range(retries):
       try:
           response = make_api_call()
           break  # Exit loop if successful
       except Exception as e:
           print(f"Error during API call: {e}")
           time.sleep(1)  # Wait before retrying
   ```

6. **Check LLaVA API or Model Documentation**:
   If you're interacting with an API, check the documentation for any updates on the expected response format. The API may have changed its behavior, or it could be returning errors in a different format.

Once you check the raw response, you'll have more information on whether the issue is with the data you're sending, a problem with the service, or a bug in how the response is being handled. Let me know what you find!

gpt-4o-mini

File "/Users/danielkliewer/ITB02/venv/lib/python3.13/site-packages/fastapi/routing.py", line 212, in run_endpoint_function
    return await dependant.call(**values)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/danielkliewer/ITB02/backend/api/routers/story.py", line 18, in generate_story
    story = pipeline.run(temp_file_path)
  File "/Users/danielkliewer/ITB02/backend/core/pipeline.py", line 40, in run
    raise Exception(f"Pipeline failed: {str(e)}")
Exception: Pipeline failed: LLaVA analysis failed: Expecting value: line 1 column 1 (char 0)

The error `Expecting value: line 1 column 1 (char 0)` suggests that the response you are trying to parse from LLaVA is empty, which typically happens when the API or model doesn't return any data. Let’s go step-by-step on how to troubleshoot this:

1. **Check Raw API Response**:
   The first step is to inspect what the API or model returns before trying to parse it as JSON. Modify the `_analyze_with_llava` method to log or print the raw response. This will give you insight into what’s coming back from LLaVA:

   ```python
   def _analyze_with_llava(self, image_bytes):
       try:
           response = your_llava_api_call(image_bytes)
           print(f"Raw response: {response.response}")
           if not response.response:
               raise ValueError("Received empty response from LLaVA analysis.")
           return json.loads(response.response)
       except Exception as e:
           raise Exception(f"LLaVA analysis failed: {str(e)}")
   ```

   This will print the raw response and will allow you to verify if it is empty or malformed.

2. **Verify Input Data**:
   Ensure that the `image_bytes` or any input data you are sending to LLaVA is valid. If the input is corrupted or incorrect, the model might return an empty response. You can check whether the input data (e.g., image) is being properly loaded:

   ```python
   if not image_bytes:
       raise ValueError("Input image is empty or invalid.")
   ```

3. **Check for Service Issues**:
   If LLaVA is calling an external service, verify that the service is up and running. If the API or model server is down or not responsive, it might return empty responses. You can test the API call separately using a tool like Postman or curl.

4. **Handling Empty Responses**:
   To make the system more robust, handle empty or invalid responses by raising a clearer error or returning a fallback response instead of letting the system crash:

   ```python
   if not response.response:
       raise ValueError("Received empty response from LLaVA analysis.")
   ```

5. **Add Retry Logic**:
   If the issue is related to transient network errors, you can introduce retry logic to reattempt the API call a few times:

   ```python
   import time
   retries = 3
   for attempt in range(retries):
       try:
           response = your_llava_api_call(image_bytes)
           if response.response:
               return json.loads(response.response)
       except Exception as e:
           print(f"Attempt {attempt+1} failed: {str(e)}")
           time.sleep(1)
   raise Exception("LLaVA analysis failed after multiple attempts.")
   ```

6. **Examine API/Model Documentation**:
   If LLaVA is an external model or service, check the API documentation to ensure that you are sending the request in the correct format. Sometimes, changes in the API or model might cause issues if the expected input or output format has changed.

By checking the raw response, validating the input data, and adding error handling, you'll be able to better understand what’s going wrong and prevent the application from crashing. Let me know what the raw response looks like once you implement these changes!

gpt-4o-mini

=> ERROR [frontend 5/6] COPY frontend/ ./                                                   0.0s
[+] Running 1/2
 ✔ Service backend   Built                                                                  58.4s 
 ⠸ Service frontend  Building                                                                1.3s 
failed to solve: failed to compute cache key: failed to calculat

The error message `failed to solve: failed to compute cache key: failed to calculate` typically points to an issue with the Docker build process, specifically when trying to cache or copy files in the build context.

Here’s what might be happening and how you can troubleshoot it:

1. **Path Issues**:
   The issue could stem from incorrect file paths in your Dockerfile. Ensure that the `frontend/` folder exists in the same directory as the Dockerfile and is correctly referenced.

   - Verify that the path `frontend/` is relative to the directory where the Docker build command is being executed.
   - If `frontend/` is located in a different directory, adjust the path accordingly.

2. **Docker Context**:
   When building Docker images, the context is important. The build context is the set of files and directories available to Docker during the build. Ensure that you're running the `docker build` command from the correct directory and including all necessary files.

   - To check the context, use `docker build -t your-image-name .` in the directory that contains both the `frontend/` and `backend/` directories (or adjust paths to point directly to where each folder resides).

3. **File Permissions**:
   If there are permission issues on your files or directories (especially in a multi-user environment), Docker might have trouble accessing them during the build process. Ensure that the files and directories have proper read permissions for Docker.

   You can check and adjust permissions with commands like:

   ```bash
   chmod -R 755 frontend/
   ```

4. **Dockerfile Syntax**:
   Double-check the syntax of your Dockerfile. The `COPY` instruction expects both the source and the destination to be valid paths. A common mistake is an incorrect path format.

   For example, this should be valid if `frontend/` exists at the root:

   ```dockerfile
   COPY frontend/ ./frontend/
   ```

5. **Clear Docker Cache**:
   Sometimes, Docker’s build cache can cause problems. Try building the image with the `--no-cache` flag to force a fresh build:

   ```bash
   docker build --no-cache -t your-image-name .
   ```

6. **Check Docker Version**:
   Ensure you're running the latest version of Docker. If you're not, try upgrading Docker to the latest version in case there’s a bug causing this issue.

7. **Check Docker Logs**:
   For more specific error details, check the Docker logs by running:

   ```bash
   docker build -t your-image-name . --progress=plain
   ```

   This will provide a more verbose output and might give more information about what's going wrong during the build process.

If none of these work, you can also share the relevant portions of your Dockerfile and folder structure, and I can assist you further.

gpt-4o-mini

2025-01-23 15:21:38 ERROR:    Error loading ASGI app. Could not import module "backend.main".

The error `Could not import module "backend.main"` typically indicates that FastAPI or Uvicorn is unable to locate or import the `main.py` file from your `backend` directory. Here’s how you can resolve this:

### 1. **Check the Project Structure**
Ensure that your project structure is correct and that the `main.py` file exists within the `backend/` directory. Your project should look something like this:

```
project-root/
│
├── backend/
│   ├── main.py
│   ├── other_backend_files/
│
└── frontend/
    ├── other_frontend_files/
```

If `main.py` doesn't exist in the `backend/` directory, create it or adjust your configuration to point to the correct module.

### 2. **Check the ASGI Module Path**
Make sure the ASGI app is being referenced correctly when you run Uvicorn. If your `main.py` is in the `backend` folder, and you're starting Uvicorn from the root directory of the project, the correct command should look like this:

```bash
uvicorn backend.main:app --reload
```

- `backend.main` refers to the `main.py` file inside the `backend/` folder.
- `app` is the FastAPI instance inside `main.py`.

If you're running the app from a different directory, adjust the path to `main:app` accordingly.

### 3. **Check for Typo or Incorrect Naming**
Ensure there are no typos in the `main.py` filename or in the module import statement. The Python module should match the file name (e.g., `main.py` should be imported as `main`).

### 4. **Activate the Correct Virtual Environment**
Ensure that your virtual environment is active when you’re trying to run the application. You can activate your virtual environment as follows (assuming it’s located in the `venv` directory):

- **macOS/Linux:**
  ```bash
  source venv/bin/activate
  ```

- **Windows:**
  ```bash
  .\venv\Scripts\activate
  ```

Once activated, try running the app again.

### 5. **Check for Missing Dependencies**
Sometimes the issue might stem from missing dependencies. Ensure all required packages are installed in your virtual environment by running:

```bash
pip install -r requirements.txt
```

Make sure `fastapi` and `uvicorn` are included in the `requirements.txt`.

### 6. **Rebuild Docker (if applicable)**
If you are using Docker, it might help to rebuild the Docker image after making sure all paths and files are correct:

```bash
docker-compose down
docker-compose up --build
```

This will stop any running containers, rebuild the images, and start them again.

### 7. **Check for Syntax Errors**
If there are syntax errors in the `main.py` file, Python might fail to load the module. You can manually check the file for any issues, or run:

```bash
python -m backend.main
```

This will attempt to run `main.py` directly and should give you any Python-specific errors.

Let me know if any of these steps help or if you're still encountering issues!

gpt-4o-mini

0.0.0.0 refused to connect.

The error "0.0.0.0 refused to connect" typically indicates that the application is not successfully binding to the correct IP address or port, or that the server is not running properly. Here are some steps to troubleshoot and resolve this issue:

### 1. **Check if the Server is Running Properly**
First, confirm whether the backend server is running. You can check this by looking for any logs or confirming that Uvicorn is up and running. For FastAPI with Uvicorn, it should show something like:

```bash
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
```

If this log doesn’t show up, your server may not be running correctly.

### 2. **Verify the Binding Address and Port**
- By default, Uvicorn binds to `127.0.0.1:8000`, which means it is only accessible from the local machine.
- To make it accessible to other devices or your Docker containers (if applicable), use `0.0.0.0` as the binding address.

Run Uvicorn with the correct command if you're using `0.0.0.0` to bind:

```bash
uvicorn backend.main:app --host 0.0.0.0 --port 8000 --reload
```

This should allow the application to accept connections from external devices or Docker containers.

### 3. **Check for Firewall or Network Issues**
If you're trying to access the application from a different machine or Docker container, ensure that:
- The firewall on your machine allows traffic on port 8000.
- The port you are using is not being blocked by your OS or network.

You can check whether port 8000 is open by running the following command on the host machine (macOS/Linux):

```bash
lsof -i :8000
```

On Windows, you can use:

```bash
netstat -an | findstr 8000
```

### 4. **Inspect Docker Networking (if applicable)**
If you're running the application inside Docker, check that the network configuration is correct. For Docker Compose, ensure that the service ports are exposed and mapped correctly in your `docker-compose.yml` file:

```yaml
services:
  backend:
    build: .
    ports:
      - "8000:8000"  # Exposing port 8000
    networks:
      - backend_network

networks:
  backend_network:
    driver: bridge
```

After ensuring the correct ports are exposed, you may need to rebuild and restart the containers:

```bash
docker-compose down
docker-compose up --build
```

### 5. **Check for Application Errors**
If the server still refuses to connect, check your logs for any errors that might be preventing the server from starting. Look for any traceback or exceptions in your application that might stop it from binding to the correct IP address.

### 6. **Check Docker Container Logs (if applicable)**
If you're using Docker, you can check the logs of your container to see if there are any errors:

```bash
docker logs <container_name_or_id>
```

### 7. **Try Using `localhost` Instead of `0.0.0.0`**
If you're running everything on your local machine, try replacing `0.0.0.0` with `localhost` in the `uvicorn` command to check if that works:

```bash
uvicorn backend.main:app --host localhost --port 8000 --reload
```

### 8. **Inspect Your `frontend` (if applicable)**
If you're trying to access the backend from a frontend, make sure your frontend code is targeting the correct address (e.g., `http://localhost:8000` or `http://0.0.0.0:8000`) and not trying to connect to an invalid address.

### 9. **Verify Docker Container Configuration (if applicable)**
Ensure that the Docker container is correctly configured to forward traffic to the correct ports. You can verify this with:

```bash
docker ps
```

Check that port `8000` (or whatever you’re using) is mapped to the host machine’s port.

### 10. **Check for ASGI Application Errors**
If the issue persists, there might be an ASGI application error preventing the server from starting. Try running the application manually with Python to see if there are any exceptions or errors:

```bash
python -m backend.main
```

This can give you any error logs that FastAPI or Uvicorn is hiding when running through Docker or other environments.

Let me know how it goes after trying these steps!

gpt-4o-mini