Reddit Blog App Fix
2025-02-0313 turns127,674 charsgpt-4o
Summary
Fixing a Streamlit Reddit blog app with API integration, database, and UI display issues.
Messages
rewrite this so that it works: # reddit_blog_app.py
import os
import streamlit as st
import sqlite3
import json
from datetime import datetime
import pandas as pd
import networkx as nx
import praw
import requests
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Database setup
def init_db():
with sqlite3.connect("metrics.db") as conn:
conn.execute('''CREATE TABLE IF NOT EXISTS results
(id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT,
metrics TEXT,
final_blog TEXT,
status TEXT)''')
def save_to_db(metrics, final_blog, status="complete"):
with sqlite3.connect("metrics.db") as conn:
conn.execute(
"INSERT INTO results (timestamp, metrics, final_blog, status) VALUES (?, ?, ?, ?)",
(datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
json.dumps(metrics),
final_blog,
status)
)
def fetch_history():
with sqlite3.connect("metrics.db") as conn:
return pd.read_sql_query(
"SELECT id, timestamp, metrics, final_blog, status FROM results ORDER BY id DESC",
conn
)
# Reddit integration
class RedditManager:
def __init__(self):
self.reddit = praw.Reddit(
client_id=os.getenv("REDDIT_CLIENT_ID"),
client_secret=os.getenv("REDDIT_CLIENT_SECRET"),
user_agent=os.getenv("REDDIT_USER_AGENT"),
username=os.getenv("REDDIT_USERNAME"),
password=os.getenv("REDDIT_PASSWORD")
)
def fetch_content(self, limit=10):
return [
*self.reddit.user.me().submissions.new(limit=limit),
*self.reddit.user.me().comments.new(limit=limit)
]
class BaseAgent:
def __init__(self, model="vanilj/Phi-4:latest"):
self.endpoint = "http://localhost:11434/api/generate"
self.model = model
# Analysis pipeline
class BlogGenerator:
def __init__(self):
self.agents = {
'Expand': self.ExpandAgent(),
'Analyze': self.AnalyzeAgent(),
'Metric': self.MetricAgent(),
'Final': self.FinalAgent()
}
self.workflow = nx.DiGraph([
('Expand', 'Analyze'),
('Analyze', 'Metric'),
('Metric', 'Final'),
('Expand', 'Final'),
('Analyze', 'Final')
])
class BaseAgent:
def __init__(self, model="vanilj/Phi-4:latest"):
self.endpoint = "http://localhost:11434/api/generate"
self.model = model
class ExpandAgent(BaseAgent):
def __init__(self):
super().__init__()
self.endpoint = "http://localhost:11434/api/generate"
def process(self, message, code="", readme=""):
# Convert message to string if it's a dict
message_str = message if isinstance(message, str) else str(message)
data = {
"model": self.model,
"prompt": f"""{message_str} Analyze the following content and extract the following values from the following keys:
Psychological Profile Extraction (Keys and Value Descriptions)
1. Emotional Tone – Analyzed sentiment of the text (positive, neutral, negative). (String: “Positive”, “Neutral”, “Negative”)
2. Dominant Emotion – Primary emotion conveyed (joy, anger, sadness, etc.). (String: “Joy”, “Anger”, “Sadness”, etc.)
3. Cognitive Complexity – Measures depth of thought and abstraction. (Number: Scale of 1-10, where 1 is simple and 10 is highly complex)
4. Openness to Experience – Determines level of curiosity and exploration. (Number: Scale of 1-10)
5. Conscientiousness – Assesses organization and discipline in the text. (Number: Scale of 1-10)
6. Extraversion – Measures social engagement or withdrawal. (Number: Scale of 1-10)
7. Agreeableness – Evaluates friendliness and cooperativeness. (Number: Scale of 1-10)
8. Neuroticism – Measures emotional stability. (Number: Scale of 1-10)
9. Confidence Level – Extracts indicators of certainty vs. doubt. (Number: Scale of 1-10)
10. Formality of Writing – Measures casual vs. structured writing. (Number: Scale of 1-10, where 1 is informal and 10 is highly formal)
11. Self-Reference Frequency – Counts first-person pronouns (I, me, my). (Number: Percentage of self-references per total words)
12. Use of Technical Jargon – Measures complexity of vocabulary. (Number: Percentage of technical terms per total words)
13. Hedging Language – Identifies uncertainty (e.g., “might,” “perhaps”). (Number: Percentage of hedging words per total words)
14. Persuasive Language – Detects argumentation strategies. (Number: Scale of 1-10, where 1 is neutral and 10 is highly persuasive)
15. Optimism vs. Pessimism – Determines future outlook in statements. (String: “Optimistic”, “Neutral”, “Pessimistic”)
16. Problem-Solving Orientation – Identifies structured problem resolution attempts. (Number: Scale of 1-10)
17. Ambiguity vs. Specificity – Measures how precise the language is. (Number: Scale of 1-10)
18. Use of Metaphors & Analogies – Detects abstract explanatory patterns. (Number: Percentage of metaphors per total words)
19. Intensity of Emotion – Measures emotional expressiveness. (Number: Scale of 1-10)
20. Frequency of Humor or Sarcasm – Identifies humorous intent. (Number: Scale of 1-10)
21. Use of Imperatives – Detects commands or direct instructions. (Number: Percentage of imperative sentences per total words)
22. Introspective vs. External Focus – Identifies whether the user talks about personal experience or external topics. (String: “Introspective”, “Balanced”, “External”)
23. Risk Aversion – Evaluates cautious vs. risk-taking tendencies. (Number: Scale of 1-10)
24. Resilience Language – Detects expressions of perseverance and adaptability. (Number: Scale of 1-10)
25. Use of Collective Language – Measures group affiliation (“we,” “us”). (Number: Percentage of collective pronouns per total words)
Programming Idea Extraction (Keys and Value Descriptions)
26. Main Programming Topic – Extracts the primary area of discussion. (String: “Web Development”, “Machine Learning”, “Databases”, etc.)
27. Programming Language Mentioned – Identifies the programming languages in use. (List of strings: [“Python”, “JavaScript”, etc.])
28. Frameworks and Libraries Mentioned – Extracts names of technologies used. (List of strings: [“React”, “Django”, etc.])
29. Problem Statement – Extracts the core technical issue being discussed. (String: Brief problem description)
30. Proposed Solution Complexity – Evaluates depth of proposed solutions. (Number: Scale of 1-10)
31. Use of Design Patterns – Identifies named software patterns. (List of strings: [“Singleton”, “Factory”, etc.])
32. Algorithmic Complexity Discussion – Measures technical depth of algorithm talk. (Number: Scale of 1-10)
33. Performance Optimization Concerns – Detects efficiency discussions. (Number: Scale of 1-10)
34. Security Considerations – Extracts references to security best practices. (Number: Scale of 1-10)
35. Scalability Discussion – Identifies concerns about large-scale applications. (Number: Scale of 1-10)
36. Code Readability Consideration – Extracts whether clarity is a focus. (Number: Scale of 1-10)
37. Testing and Debugging Approaches – Identifies methodologies used. (List of strings: [“Unit Tests”, “Debugging”, “CI/CD”])
38. Tooling and Environment Mentions – Extracts references to IDEs, linters, etc. (List of strings: [“VS Code”, “Docker”, etc.])
39. Dependency Management Discussion – Identifies package management strategies. (List of strings: [“pip”, “npm”, etc.])
40. Database Discussion – Extracts database-related topics. (String: “SQL”, “NoSQL”, “Graph Databases”)
41. Data Structure Mentions – Identifies key structures being discussed. (List of strings: [“Array”, “HashMap”, etc.])
42. Concurrency and Parallelism Concerns – Detects threading or async talk. (Number: Scale of 1-10)
43. API Design Discussion – Evaluates REST, GraphQL, or microservices mentions. (String: “REST”, “GraphQL”, “Microservices”)
44. Error Handling Strategies – Extracts how errors are managed. (List of strings: [“Try-Catch”, “Logging”, etc.])
45. Automated Deployment Mention – Identifies CI/CD pipeline discussions. (String: “Jenkins”, “GitHub Actions”, etc.)
46. UI/UX Considerations – Detects front-end usability discussions. (Number: Scale of 1-10)
47. Code Reusability Mentions – Extracts whether modularity is discussed. (Number: Scale of 1-10)
48. Project Management Methodologies – Identifies Agile, Scrum, etc. (List of strings: [“Agile”, “Scrum”, “Kanban”])
49. Collaboration and Open Source Involvement – Detects teamwork discussions. (Number: Scale of 1-10)
50. Ethical Considerations in Programming – Identifies discussions about responsible AI, privacy, etc. (Number: Scale of 1-10)""",
"stream": False
}
try:
response = requests.post(self.endpoint, json=data).json()
design_spec = response.get('response', '')
enhanced_message = f"{message_str}\n\n{design_spec}"
return {
'message': enhanced_message,
}
except Exception as e:
print(f"Error in ExpandAgent: {str(e)}")
return {
'message': message_str,
}
class AnalyzeAgent(BaseAgent):
def __init__(self):
super().__init__()
self.endpoint = "http://localhost:11434/api/generate"
def process(self, message, code="", readme=""):
# Convert message to string if it's a dict
message_str = message if isinstance(message, str) else str(message)
data = {
"model": self.model,
"prompt": f"""({message_str}) You will receive structured Reddit content analysis data based on two main categories: **Psychological Profile Extraction** and **Programming Metrics Extraction**. Your task is to analyze the provided data according to the outlined metrics and generate two distinct markdown-formatted outputs:
1. **A Psychological Profile Report** – A detailed written analysis in markdown format describing the psychological characteristics of the Reddit user based on extracted metrics.
2. **A Programming Project Outline** – A structured markdown document detailing the technical discussion, extracted programming ideas, and an architecture overview of a potential project inspired by the extracted insights.
**Input Structure:**
The structured input data will contain two sections:
**1. Psychological Profile Extraction**
For each metric, the data will contain either a categorical label (e.g., "Positive", "Joy"), a numerical scale (1-10), or a percentage-based metric. These values should be used to construct a meaningful psychological analysis. The key attributes include:
• **Emotional Tone** (Positive, Neutral, Negative)
• **Dominant Emotion** (Joy, Anger, Sadness, etc.)
• **Cognitive Complexity** (1-10)
• **Openness to Experience** (1-10)
• **Conscientiousness** (1-10)
• **Extraversion** (1-10)
• **Agreeableness** (1-10)
• **Neuroticism** (1-10)
• **Confidence Level** (1-10)
• **Formality of Writing** (1-10)
• **Self-Reference Frequency** (Percentage)
• **Use of Technical Jargon** (Percentage)
• **Hedging Language** (Percentage)
• **Persuasive Language** (1-10)
• **Optimism vs. Pessimism** (Optimistic, Neutral, Pessimistic)
• **Problem-Solving Orientation** (1-10)
• **Ambiguity vs. Specificity** (1-10)
• **Use of Metaphors & Analogies** (Percentage)
• **Intensity of Emotion** (1-10)
• **Frequency of Humor or Sarcasm** (1-10)
• **Use of Imperatives** (Percentage)
• **Introspective vs. External Focus** (Introspective, Balanced, External)
• **Risk Aversion** (1-10)
• **Resilience Language** (1-10)
• **Use of Collective Language** (Percentage)
**2. Programming Metrics Extraction**
This section will contain structured data extracted from the programming-related discussion. Your task is to use these extracted elements to construct a markdown-formatted programming guide that outlines the technical topic, programming challenges, and a structured plan for a potential application. The extracted metrics include:
• **Main Programming Topic** (Web Development, Machine Learning, etc.)
• **Programming Language Mentioned** (List: Python, JavaScript, etc.)
• **Frameworks and Libraries Mentioned** (List: React, Django, etc.)
• **Problem Statement** (Brief description)
• **Proposed Solution Complexity** (1-10)
• **Use of Design Patterns** (List: Singleton, Factory, etc.)
• **Algorithmic Complexity Discussion** (1-10)
• **Performance Optimization Concerns** (1-10)
• **Security Considerations** (1-10)
• **Scalability Discussion** (1-10)
• **Code Readability Consideration** (1-10)
• **Testing and Debugging Approaches** (List: Unit Tests, Debugging, CI/CD)
• **Tooling and Environment Mentions** (List: VS Code, Docker, etc.)
• **Dependency Management Discussion** (List: pip, npm, etc.)
• **Database Discussion** (SQL, NoSQL, Graph Databases)
• **Data Structure Mentions** (List: Array, HashMap, etc.)
• **Concurrency and Parallelism Concerns** (1-10)
• **API Design Discussion** (REST, GraphQL, Microservices)
• **Error Handling Strategies** (List: Try-Catch, Logging, etc.)
• **Automated Deployment Mention** (Jenkins, GitHub Actions, etc.)
• **UI/UX Considerations** (1-10)
• **Code Reusability Mentions** (1-10)
• **Project Management Methodologies** (List: Agile, Scrum, Kanban)
• **Collaboration and Open Source Involvement** (1-10)
• **Ethical Considerations in Programming** (1-10)
**Expected Output Format:**
**1. Markdown-Formatted Psychological Profile Analysis**
Using the structured psychological metrics, generate a **detailed written analysis** in markdown format. This analysis should explain the psychological characteristics inferred from the data, provide insights into the author’s personality, and discuss key trends in their writing.
**2. Markdown-Formatted Programming Guide and Project Architecture**
Using the extracted programming metrics, generate a **structured markdown document** that contains:
• A high-level summary of the technical discussion.
• An identified **problem statement** based on the extracted programming concerns.
• A detailed **architecture overview** of a new project that could be developed based on the discussed ideas.
• Relevant **frameworks, libraries, and best practices** to be used.
• Considerations regarding **performance, security, scalability, and testing**.
**Guidelines for Generating the Output:**
• Ensure the **Psychological Profile Analysis** reads as a natural, well-structured assessment, using the extracted numerical and categorical data to describe key traits.
• The **Programming Guide** should be formatted with clear sections (e.g., Problem Statement, Proposed Solution, Architecture, Tools, Best Practices).
• Use appropriate **markdown formatting** with headings (#), subheadings (##), lists (-), and code blocks where necessary.
• The generated text should be structured as **a blog post** suitable for publication.""",
"stream": False
}
try:
response = requests.post(self.endpoint, json=data).json()
design_spec = response.get('response', '')
enhanced_message = f"{message_str}\n\n{design_spec}"
return {
'message': enhanced_message,
}
except Exception as e:
print(f"Error in AnalyzeAgent: {str(e)}")
return {
'message': message_str,
}
class MetricAgent(BaseAgent):
def process(self, content):
try:
# Ensure content is a string
message_str = content if isinstance(content, str) else str(content)
response = requests.post(self.endpoint, json={
"model": self.model,
"prompt": f"""Using this analysis: {message_str} Analyze the previous content and create a JSON object that contains the following structured data:
{{
"psychological_profile": {{
"emotional_tone": {{
"description": "Analyzed sentiment of the text.",
"type": "string",
"values": ["Positive", "Neutral", "Negative"]
}},
"dominant_emotion": {{
"description": "Primary emotion conveyed in the text.",
"type": "string",
"values": ["Joy", "Anger", "Sadness", "Fear", "Surprise", "Disgust", "Neutral"]
}},
"cognitive_complexity": {{
"description": "Measures depth of thought and abstraction in the writing.",
"type": "integer",
"range": [1, 10]
}},
"openness_to_experience": {{
"description": "Determines the level of curiosity, creativity, and intellectual engagement.",
"type": "integer",
"range": [1, 10]
}},
"conscientiousness": {{
"description": "Assesses organization, discipline, and thoroughness in writing.",
"type": "integer",
"range": [1, 10]
}},
"extraversion": {{
"description": "Measures social engagement, enthusiasm, and talkativeness.",
"type": "integer",
"range": [1, 10]
}},
"agreeableness": {{
"description": "Evaluates friendliness, cooperativeness, and empathy.",
"type": "integer",
"range": [1, 10]
}},
"neuroticism": {{
"description": "Measures emotional stability and tendency toward negative emotions.",
"type": "integer",
"range": [1, 10]
}},
"confidence_level": {{
"description": "Indicates certainty vs. doubt in statements.",
"type": "integer",
"range": [1, 10]
}},
"formality_of_writing": {{
"description": "Measures the degree of structured and professional tone.",
"type": "integer",
"range": [1, 10]
}},
"self_reference_frequency": {{
"description": "Percentage of words that are self-referential (e.g., 'I', 'me', 'my').",
"type": "float",
"unit": "percentage"
}},
"use_of_technical_jargon": {{
"description": "Percentage of words that are domain-specific technical terms.",
"type": "float",
"unit": "percentage"
}},
"hedging_language": {{
"description": "Percentage of words or phrases that indicate uncertainty (e.g., 'might', 'perhaps').",
"type": "float",
"unit": "percentage"
}},
"persuasive_language": {{
"description": "Measures the use of rhetorical devices and argumentation strategies.",
"type": "integer",
"range": [1, 10]
}},
"optimism_vs_pessimism": {{
"description": "Determines the outlook on future events.",
"type": "string",
"values": ["Optimistic", "Neutral", "Pessimistic"]
}},
"problem_solving_orientation": {{
"description": "Identifies structured attempts to resolve issues.",
"type": "integer",
"range": [1, 10]
}},
"ambiguity_vs_specificity": {{
"description": "Measures precision and clarity of language.",
"type": "integer",
"range": [1, 10]
}},
"use_of_metaphors_analogies": {{
"description": "Percentage of words that are metaphors or analogies.",
"type": "float",
"unit": "percentage"
}},
"intensity_of_emotion": {{
"description": "Measures the expressiveness and strength of emotions conveyed.",
"type": "integer",
"range": [1, 10]
}},
"frequency_of_humor_or_sarcasm": {{
"description": "Measures humor or sarcasm usage.",
"type": "integer",
"range": [1, 10]
}},
"use_of_imperatives": {{
"description": "Percentage of sentences that contain commands or directives.",
"type": "float",
"unit": "percentage"
}},
"introspective_vs_external_focus": {{
"description": "Classifies whether the writing is focused on personal experience or external topics.",
"type": "string",
"values": ["Introspective", "Balanced", "External"]
}},
"risk_aversion": {{
"description": "Measures cautious vs. risk-taking tendencies.",
"type": "integer",
"range": [1, 10]
}},
"resilience_language": {{
"description": "Detects expressions of perseverance and adaptability.",
"type": "integer",
"range": [1, 10]
}},
"use_of_collective_language": {{
"description": "Percentage of words indicating group affiliation (e.g., 'we', 'us').",
"type": "float",
"unit": "percentage"
}}
}},
"programming_metrics": {{
"main_programming_topic": {{
"description": "Primary area of discussion in programming content.",
"type": "string"
}},
"programming_languages_mentioned": {{
"description": "List of programming languages referenced.",
"type": "array",
"items": "string"
}},
"frameworks_and_libraries_mentioned": {{
"description": "List of frameworks and libraries referenced.",
"type": "array",
"items": "string"
}},
"problem_statement": {{
"description": "Brief description of the technical issue being discussed.",
"type": "string"
}},
"proposed_solution_complexity": {{
"description": "Evaluates depth of proposed solutions.",
"type": "integer",
"range": [1, 10]
}},
"use_of_design_patterns": {{
"description": "List of software design patterns mentioned.",
"type": "array",
"items": "string"
}},
"algorithmic_complexity_discussion": {{
"description": "Measures depth of algorithm-related discussion.",
"type": "integer",
"range": [1, 10]
}},
"performance_optimization_concerns": {{
"description": "Evaluates concerns about code performance.",
"type": "integer",
"range": [1, 10]
}},
"security_considerations": {{
"description": "Evaluates references to security best practices.",
"type": "integer",
"range": [1, 10]
}},
"scalability_discussion": {{
"description": "Measures discussion on handling large-scale applications.",
"type": "integer",
"range": [1, 10]
}},
"code_readability_consideration": {{
"description": "Evaluates emphasis on clean and readable code.",
"type": "integer",
"range": [1, 10]
}},
"testing_and_debugging_approaches": {{
"description": "List of mentioned testing and debugging techniques.",
"type": "array",
"items": "string"
}},
"tooling_and_environment_mentions": {{
"description": "List of development tools and environments mentioned.",
"type": "array",
"items": "string"
}},
"dependency_management_discussion": {{
"description": "List of dependency/package management tools mentioned.",
"type": "array",
"items": "string"
}},
"database_discussion": {{
"description": "Mentions of database technologies.",
"type": "string"
}},
"error_handling_strategies": {{
"description": "List of error-handling techniques discussed.",
"type": "array",
"items": "string"
}},
"ui_ux_considerations": {{
"description": "Measures emphasis on user experience and interface design.",
"type": "integer",
"range": [1, 10]
}},
"ethical_considerations_in_programming": {{
"description": "Evaluates discussions about ethical programming topics.",
"type": "integer",
"range": [1, 10]
}}
}}
}}
Return only the JSON object containing the psychological profile and programming metrics.
""",
"stream": False
}).json()
# Parse and structure metrics
return {
'metrics': json.loads(response.get('response', '{}')),
'content': message_str
}
except Exception as e:
print(f"Metrics error: {str(e)}")
return {
'metrics': {},
'content': message_str, # Ensure content is always returned
'error': str(e)
}
class FinalAgent(BaseAgent):
def process(self, inputs):
try:
# Combine inputs from different agents
metrics = inputs.get('metrics', {})
analysis = inputs.get('analysis', {})
response = requests.post(self.endpoint, json={
"model": self.model,
"prompt": f"Generate blog post using: {metrics} and {analysis}",
"stream": False
}).json()
return {
'metrics': metrics,
'final_blog': response.get('response', ''),
'status': 'complete'
}
except Exception as e:
print(f"Final agent error: {str(e)}")
return {
'metrics': {},
'final_blog': str(e),
'status': 'error'
}
def run_analysis(self, content):
try:
# Initialize processing state
state = {'raw_content': content}
# Process through workflow
for node in nx.topological_sort(self.workflow):
if node in self.agents:
result = self.agents[node].process(state)
state.update(result)
return {
'metrics': state.get('metrics', {}),
'final_blog': state.get('final_blog', ''),
'status': state.get('status', 'complete')
}
except Exception as e:
print(f"Pipeline error: {str(e)}")
return {
'metrics': {},
'final_blog': str(e),
'status': 'error'
}
# Streamlit UI
def main():
st.set_page_config(
page_title="Reddit Content Analyzer",
page_icon="📊",
layout="wide"
)
st.title("Reddit Content Analysis and Blog Generator")
st.sidebar.header("Settings")
post_limit = st.sidebar.slider("Posts to analyze", 1, 20, 5)
init_db()
reddit_manager = RedditManager()
blog_generator = BlogGenerator()
tab_analyze, tab_history = st.tabs(["New Analysis", "History"])
with tab_analyze:
if st.button("Start Analysis"):
with st.spinner("Collecting and analyzing Reddit content..."):
try:
content = reddit_manager.fetch_content(post_limit)
results = blog_generator.run_analysis(content)
save_to_db(results['metrics'], results['final_blog'])
st.subheader("Analysis Metrics")
st.json(results['metrics'])
st.subheader("Generated Blog Post")
st.markdown(results['final_blog'])
except Exception as e:
st.error(f"Analysis failed: {str(e)}")
save_to_db({}, str(e), "error")
with tab_history:
history_df = fetch_history()
if not history_df.empty:
for _, row in history_df.iterrows():
with st.expander(f"Analysis from {row['timestamp']}"):
col1, col2 = st.columns(2)
with col1:
st.json(json.loads(row['metrics']))
with col2:
st.markdown(row['final_blog'])
else:
st.info("No previous analyses found")
if __name__ == "__main__":
main()
Your script is structured well but has a few issues that need fixing:
1. **`BaseAgent` Defined Twice**: You define `BaseAgent` both as a standalone class and inside `BlogGenerator`. Remove the redundant one.
2. **`fetch_content` Needs Processing**: The Reddit API returns objects, not direct strings. Convert them to text.
3. **`requests.post` Calls Should Handle Exceptions**: Add error handling for API calls.
4. **Processing State Handling**: `run_analysis` should correctly pass and update `state`.
I'll clean it up and optimize it for better readability and reliability.
gpt-4o
This version fixes redundant class definitions, improves API handling, and ensures content is properly formatted before processing. Let me know if you need further refinements! 🚀
gpt-4o
Metrics error: Expecting value: line 1 column 1 (char 0)
That error means the API response is empty or malformed JSON. Here’s how to fix it:
### **1. Check API Response Before Parsing**
Modify your `MetricAgent` to ensure that the response is valid before attempting `json.loads()`:
```python
class MetricAgent(BaseAgent):
def process(self, state):
raw_response = self.request_api(f"Extract Metrics: {state.get('analysis', '')}")
if not raw_response:
print("Error: Received empty response from API")
return {"metrics": {}}
try:
return {"metrics": json.loads(raw_response)}
except json.JSONDecodeError as e:
print(f"JSON Decode Error: {e}")
print(f"Raw response: {raw_response}")
return {"metrics": {}}
```
### **2. Debugging Steps**
- **Print API response**: Add `print(f"Raw API response: {raw_response}")` before `json.loads()`.
- **Check API logs**: Your local Ollama server might have errors—check its logs for failed requests.
- **Ensure endpoint is running**: Try `curl http://localhost:11434/api/generate` to verify it's responding.
Let me know what the printed output is if you need further debugging! 🚀
gpt-4o
import os
import streamlit as st
import sqlite3
import json
from datetime import datetime
import pandas as pd
import networkx as nx
import praw
import requests
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Database setup
def init_db():
with sqlite3.connect("metrics.db") as conn:
conn.execute('''CREATE TABLE IF NOT EXISTS results
(id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT,
metrics TEXT,
final_blog TEXT,
status TEXT)''')
def save_to_db(metrics, final_blog, status="complete"):
with sqlite3.connect("metrics.db") as conn:
conn.execute(
"INSERT INTO results (timestamp, metrics, final_blog, status) VALUES (?, ?, ?, ?)",
(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), json.dumps(metrics), final_blog, status)
)
def fetch_history():
with sqlite3.connect("metrics.db") as conn:
return pd.read_sql_query("SELECT * FROM results ORDER BY id DESC", conn)
# Reddit integration
class RedditManager:
def __init__(self):
self.reddit = praw.Reddit(
client_id=os.getenv("REDDIT_CLIENT_ID"),
client_secret=os.getenv("REDDIT_CLIENT_SECRET"),
user_agent=os.getenv("REDDIT_USER_AGENT"),
username=os.getenv("REDDIT_USERNAME"),
password=os.getenv("REDDIT_PASSWORD")
)
def fetch_content(self, limit=10):
submissions = [post.title + "\n" + post.selftext for post in self.reddit.user.me().submissions.new(limit=limit)]
comments = [comment.body for comment in self.reddit.user.me().comments.new(limit=limit)]
return "\n\n".join(submissions + comments)
# Base agent
class BaseAgent:
def __init__(self, model="vanilj/Phi-4:latest"):
self.endpoint = "http://localhost:11434/api/generate"
self.model = model
def request_api(self, prompt):
try:
response = requests.post(self.endpoint, json={"model": self.model, "prompt": prompt, "stream": False}).json()
return response.get('response', '')
except Exception as e:
print(f"API request error: {str(e)}")
return ""
# Blog generator
class BlogGenerator:
def __init__(self):
self.agents = {
'Expand': self.ExpandAgent(),
'Analyze': self.AnalyzeAgent(),
'Metric': self.MetricAgent(),
'Final': self.FinalAgent()
}
self.workflow = nx.DiGraph([('Expand', 'Analyze'), ('Analyze', 'Metric'), ('Metric', 'Final')])
class BaseAgent:
def __init__(self, model="vanilj/Phi-4:latest"):
self.endpoint = "http://localhost:11434/api/generate"
self.model = model
class ExpandAgent(BaseAgent):
def __init__(self):
super().__init__()
self.endpoint = "http://localhost:11434/api/generate"
def process(self, message, code="", readme=""):
# Convert message to string if it's a dict
message_str = message if isinstance(message, str) else str(message)
data = {
"model": self.model,
"prompt": f"""{message_str} Analyze the following content and extract the following values from the following keys:
Psychological Profile Extraction (Keys and Value Descriptions)
1. Emotional Tone – Analyzed sentiment of the text (positive, neutral, negative). (String: “Positive”, “Neutral”, “Negative”)
2. Dominant Emotion – Primary emotion conveyed (joy, anger, sadness, etc.). (String: “Joy”, “Anger”, “Sadness”, etc.)
3. Cognitive Complexity – Measures depth of thought and abstraction. (Number: Scale of 1-10, where 1 is simple and 10 is highly complex)
4. Openness to Experience – Determines level of curiosity and exploration. (Number: Scale of 1-10)
5. Conscientiousness – Assesses organization and discipline in the text. (Number: Scale of 1-10)
6. Extraversion – Measures social engagement or withdrawal. (Number: Scale of 1-10)
7. Agreeableness – Evaluates friendliness and cooperativeness. (Number: Scale of 1-10)
8. Neuroticism – Measures emotional stability. (Number: Scale of 1-10)
9. Confidence Level – Extracts indicators of certainty vs. doubt. (Number: Scale of 1-10)
10. Formality of Writing – Measures casual vs. structured writing. (Number: Scale of 1-10, where 1 is informal and 10 is highly formal)
11. Self-Reference Frequency – Counts first-person pronouns (I, me, my). (Number: Percentage of self-references per total words)
12. Use of Technical Jargon – Measures complexity of vocabulary. (Number: Percentage of technical terms per total words)
13. Hedging Language – Identifies uncertainty (e.g., “might,” “perhaps”). (Number: Percentage of hedging words per total words)
14. Persuasive Language – Detects argumentation strategies. (Number: Scale of 1-10, where 1 is neutral and 10 is highly persuasive)
15. Optimism vs. Pessimism – Determines future outlook in statements. (String: “Optimistic”, “Neutral”, “Pessimistic”)
16. Problem-Solving Orientation – Identifies structured problem resolution attempts. (Number: Scale of 1-10)
17. Ambiguity vs. Specificity – Measures how precise the language is. (Number: Scale of 1-10)
18. Use of Metaphors & Analogies – Detects abstract explanatory patterns. (Number: Percentage of metaphors per total words)
19. Intensity of Emotion – Measures emotional expressiveness. (Number: Scale of 1-10)
20. Frequency of Humor or Sarcasm – Identifies humorous intent. (Number: Scale of 1-10)
21. Use of Imperatives – Detects commands or direct instructions. (Number: Percentage of imperative sentences per total words)
22. Introspective vs. External Focus – Identifies whether the user talks about personal experience or external topics. (String: “Introspective”, “Balanced”, “External”)
23. Risk Aversion – Evaluates cautious vs. risk-taking tendencies. (Number: Scale of 1-10)
24. Resilience Language – Detects expressions of perseverance and adaptability. (Number: Scale of 1-10)
25. Use of Collective Language – Measures group affiliation (“we,” “us”). (Number: Percentage of collective pronouns per total words)
Programming Idea Extraction (Keys and Value Descriptions)
26. Main Programming Topic – Extracts the primary area of discussion. (String: “Web Development”, “Machine Learning”, “Databases”, etc.)
27. Programming Language Mentioned – Identifies the programming languages in use. (List of strings: [“Python”, “JavaScript”, etc.])
28. Frameworks and Libraries Mentioned – Extracts names of technologies used. (List of strings: [“React”, “Django”, etc.])
29. Problem Statement – Extracts the core technical issue being discussed. (String: Brief problem description)
30. Proposed Solution Complexity – Evaluates depth of proposed solutions. (Number: Scale of 1-10)
31. Use of Design Patterns – Identifies named software patterns. (List of strings: [“Singleton”, “Factory”, etc.])
32. Algorithmic Complexity Discussion – Measures technical depth of algorithm talk. (Number: Scale of 1-10)
33. Performance Optimization Concerns – Detects efficiency discussions. (Number: Scale of 1-10)
34. Security Considerations – Extracts references to security best practices. (Number: Scale of 1-10)
35. Scalability Discussion – Identifies concerns about large-scale applications. (Number: Scale of 1-10)
36. Code Readability Consideration – Extracts whether clarity is a focus. (Number: Scale of 1-10)
37. Testing and Debugging Approaches – Identifies methodologies used. (List of strings: [“Unit Tests”, “Debugging”, “CI/CD”])
38. Tooling and Environment Mentions – Extracts references to IDEs, linters, etc. (List of strings: [“VS Code”, “Docker”, etc.])
39. Dependency Management Discussion – Identifies package management strategies. (List of strings: [“pip”, “npm”, etc.])
40. Database Discussion – Extracts database-related topics. (String: “SQL”, “NoSQL”, “Graph Databases”)
41. Data Structure Mentions – Identifies key structures being discussed. (List of strings: [“Array”, “HashMap”, etc.])
42. Concurrency and Parallelism Concerns – Detects threading or async talk. (Number: Scale of 1-10)
43. API Design Discussion – Evaluates REST, GraphQL, or microservices mentions. (String: “REST”, “GraphQL”, “Microservices”)
44. Error Handling Strategies – Extracts how errors are managed. (List of strings: [“Try-Catch”, “Logging”, etc.])
45. Automated Deployment Mention – Identifies CI/CD pipeline discussions. (String: “Jenkins”, “GitHub Actions”, etc.)
46. UI/UX Considerations – Detects front-end usability discussions. (Number: Scale of 1-10)
47. Code Reusability Mentions – Extracts whether modularity is discussed. (Number: Scale of 1-10)
48. Project Management Methodologies – Identifies Agile, Scrum, etc. (List of strings: [“Agile”, “Scrum”, “Kanban”])
49. Collaboration and Open Source Involvement – Detects teamwork discussions. (Number: Scale of 1-10)
50. Ethical Considerations in Programming – Identifies discussions about responsible AI, privacy, etc. (Number: Scale of 1-10)""",
"stream": False
}
try:
response = requests.post(self.endpoint, json=data).json()
design_spec = response.get('response', '')
enhanced_message = f"{message_str}\n\n{design_spec}"
return {
'message': enhanced_message,
}
except Exception as e:
print(f"Error in ExpandAgent: {str(e)}")
return {
'message': message_str,
}
class AnalyzeAgent(BaseAgent):
def __init__(self):
super().__init__()
self.endpoint = "http://localhost:11434/api/generate"
def process(self, message, code="", readme=""):
# Convert message to string if it's a dict
message_str = message if isinstance(message, str) else str(message)
data = {
"model": self.model,
"prompt": f"""({message_str}) You will receive structured Reddit content analysis data based on two main categories: **Psychological Profile Extraction** and **Programming Metrics Extraction**. Your task is to analyze the provided data according to the outlined metrics and generate two distinct markdown-formatted outputs:
1. **A Psychological Profile Report** – A detailed written analysis in markdown format describing the psychological characteristics of the Reddit user based on extracted metrics.
2. **A Programming Project Outline** – A structured markdown document detailing the technical discussion, extracted programming ideas, and an architecture overview of a potential project inspired by the extracted insights.
**Input Structure:**
The structured input data will contain two sections:
**1. Psychological Profile Extraction**
For each metric, the data will contain either a categorical label (e.g., "Positive", "Joy"), a numerical scale (1-10), or a percentage-based metric. These values should be used to construct a meaningful psychological analysis. The key attributes include:
• **Emotional Tone** (Positive, Neutral, Negative)
• **Dominant Emotion** (Joy, Anger, Sadness, etc.)
• **Cognitive Complexity** (1-10)
• **Openness to Experience** (1-10)
• **Conscientiousness** (1-10)
• **Extraversion** (1-10)
• **Agreeableness** (1-10)
• **Neuroticism** (1-10)
• **Confidence Level** (1-10)
• **Formality of Writing** (1-10)
• **Self-Reference Frequency** (Percentage)
• **Use of Technical Jargon** (Percentage)
• **Hedging Language** (Percentage)
• **Persuasive Language** (1-10)
• **Optimism vs. Pessimism** (Optimistic, Neutral, Pessimistic)
• **Problem-Solving Orientation** (1-10)
• **Ambiguity vs. Specificity** (1-10)
• **Use of Metaphors & Analogies** (Percentage)
• **Intensity of Emotion** (1-10)
• **Frequency of Humor or Sarcasm** (1-10)
• **Use of Imperatives** (Percentage)
• **Introspective vs. External Focus** (Introspective, Balanced, External)
• **Risk Aversion** (1-10)
• **Resilience Language** (1-10)
• **Use of Collective Language** (Percentage)
**2. Programming Metrics Extraction**
This section will contain structured data extracted from the programming-related discussion. Your task is to use these extracted elements to construct a markdown-formatted programming guide that outlines the technical topic, programming challenges, and a structured plan for a potential application. The extracted metrics include:
• **Main Programming Topic** (Web Development, Machine Learning, etc.)
• **Programming Language Mentioned** (List: Python, JavaScript, etc.)
• **Frameworks and Libraries Mentioned** (List: React, Django, etc.)
• **Problem Statement** (Brief description)
• **Proposed Solution Complexity** (1-10)
• **Use of Design Patterns** (List: Singleton, Factory, etc.)
• **Algorithmic Complexity Discussion** (1-10)
• **Performance Optimization Concerns** (1-10)
• **Security Considerations** (1-10)
• **Scalability Discussion** (1-10)
• **Code Readability Consideration** (1-10)
• **Testing and Debugging Approaches** (List: Unit Tests, Debugging, CI/CD)
• **Tooling and Environment Mentions** (List: VS Code, Docker, etc.)
• **Dependency Management Discussion** (List: pip, npm, etc.)
• **Database Discussion** (SQL, NoSQL, Graph Databases)
• **Data Structure Mentions** (List: Array, HashMap, etc.)
• **Concurrency and Parallelism Concerns** (1-10)
• **API Design Discussion** (REST, GraphQL, Microservices)
• **Error Handling Strategies** (List: Try-Catch, Logging, etc.)
• **Automated Deployment Mention** (Jenkins, GitHub Actions, etc.)
• **UI/UX Considerations** (1-10)
• **Code Reusability Mentions** (1-10)
• **Project Management Methodologies** (List: Agile, Scrum, Kanban)
• **Collaboration and Open Source Involvement** (1-10)
• **Ethical Considerations in Programming** (1-10)
**Expected Output Format:**
**1. Markdown-Formatted Psychological Profile Analysis**
Using the structured psychological metrics, generate a **detailed written analysis** in markdown format. This analysis should explain the psychological characteristics inferred from the data, provide insights into the author’s personality, and discuss key trends in their writing.
**2. Markdown-Formatted Programming Guide and Project Architecture**
Using the extracted programming metrics, generate a **structured markdown document** that contains:
• A high-level summary of the technical discussion.
• An identified **problem statement** based on the extracted programming concerns.
• A detailed **architecture overview** of a new project that could be developed based on the discussed ideas.
• Relevant **frameworks, libraries, and best practices** to be used.
• Considerations regarding **performance, security, scalability, and testing**.
**Guidelines for Generating the Output:**
• Ensure the **Psychological Profile Analysis** reads as a natural, well-structured assessment, using the extracted numerical and categorical data to describe key traits.
• The **Programming Guide** should be formatted with clear sections (e.g., Problem Statement, Proposed Solution, Architecture, Tools, Best Practices).
• Use appropriate **markdown formatting** with headings (#), subheadings (##), lists (-), and code blocks where necessary.
• The generated text should be structured as **a blog post** suitable for publication.""",
"stream": False
}
try:
response = requests.post(self.endpoint, json=data).json()
design_spec = response.get('response', '')
enhanced_message = f"{message_str}\n\n{design_spec}"
return {
'message': enhanced_message,
}
except Exception as e:
print(f"Error in AnalyzeAgent: {str(e)}")
return {
'message': message_str,
}
class MetricAgent(BaseAgent):
def process(self, state):
raw_response = self.request_api(f"Extract Metrics: {state.get('analysis', '')}")
if not raw_response:
print("Error: Received empty response from API")
return {"metrics": {}}
try:
return {"metrics": json.loads(raw_response)}
except json.JSONDecodeError as e:
print(f"JSON Decode Error: {e}")
print(f"Raw response: {raw_response}")
return {"metrics": {}}
response = requests.post(self.endpoint, json={
"model": self.model,
"prompt": f"""Using this analysis: {message_str} Analyze the previous content and create a JSON object that contains the following structured data:
{{
"psychological_profile": {{
"emotional_tone": {{
"description": "Analyzed sentiment of the text.",
"type": "string",
"values": ["Positive", "Neutral", "Negative"]
}},
"dominant_emotion": {{
"description": "Primary emotion conveyed in the text.",
"type": "string",
"values": ["Joy", "Anger", "Sadness", "Fear", "Surprise", "Disgust", "Neutral"]
}},
"cognitive_complexity": {{
"description": "Measures depth of thought and abstraction in the writing.",
"type": "integer",
"range": [1, 10]
}},
"openness_to_experience": {{
"description": "Determines the level of curiosity, creativity, and intellectual engagement.",
"type": "integer",
"range": [1, 10]
}},
"conscientiousness": {{
"description": "Assesses organization, discipline, and thoroughness in writing.",
"type": "integer",
"range": [1, 10]
}},
"extraversion": {{
"description": "Measures social engagement, enthusiasm, and talkativeness.",
"type": "integer",
"range": [1, 10]
}},
"agreeableness": {{
"description": "Evaluates friendliness, cooperativeness, and empathy.",
"type": "integer",
"range": [1, 10]
}},
"neuroticism": {{
"description": "Measures emotional stability and tendency toward negative emotions.",
"type": "integer",
"range": [1, 10]
}},
"confidence_level": {{
"description": "Indicates certainty vs. doubt in statements.",
"type": "integer",
"range": [1, 10]
}},
"formality_of_writing": {{
"description": "Measures the degree of structured and professional tone.",
"type": "integer",
"range": [1, 10]
}},
"self_reference_frequency": {{
"description": "Percentage of words that are self-referential (e.g., 'I', 'me', 'my').",
"type": "float",
"unit": "percentage"
}},
"use_of_technical_jargon": {{
"description": "Percentage of words that are domain-specific technical terms.",
"type": "float",
"unit": "percentage"
}},
"hedging_language": {{
"description": "Percentage of words or phrases that indicate uncertainty (e.g., 'might', 'perhaps').",
"type": "float",
"unit": "percentage"
}},
"persuasive_language": {{
"description": "Measures the use of rhetorical devices and argumentation strategies.",
"type": "integer",
"range": [1, 10]
}},
"optimism_vs_pessimism": {{
"description": "Determines the outlook on future events.",
"type": "string",
"values": ["Optimistic", "Neutral", "Pessimistic"]
}},
"problem_solving_orientation": {{
"description": "Identifies structured attempts to resolve issues.",
"type": "integer",
"range": [1, 10]
}},
"ambiguity_vs_specificity": {{
"description": "Measures precision and clarity of language.",
"type": "integer",
"range": [1, 10]
}},
"use_of_metaphors_analogies": {{
"description": "Percentage of words that are metaphors or analogies.",
"type": "float",
"unit": "percentage"
}},
"intensity_of_emotion": {{
"description": "Measures the expressiveness and strength of emotions conveyed.",
"type": "integer",
"range": [1, 10]
}},
"frequency_of_humor_or_sarcasm": {{
"description": "Measures humor or sarcasm usage.",
"type": "integer",
"range": [1, 10]
}},
"use_of_imperatives": {{
"description": "Percentage of sentences that contain commands or directives.",
"type": "float",
"unit": "percentage"
}},
"introspective_vs_external_focus": {{
"description": "Classifies whether the writing is focused on personal experience or external topics.",
"type": "string",
"values": ["Introspective", "Balanced", "External"]
}},
"risk_aversion": {{
"description": "Measures cautious vs. risk-taking tendencies.",
"type": "integer",
"range": [1, 10]
}},
"resilience_language": {{
"description": "Detects expressions of perseverance and adaptability.",
"type": "integer",
"range": [1, 10]
}},
"use_of_collective_language": {{
"description": "Percentage of words indicating group affiliation (e.g., 'we', 'us').",
"type": "float",
"unit": "percentage"
}}
}},
"programming_metrics": {{
"main_programming_topic": {{
"description": "Primary area of discussion in programming content.",
"type": "string"
}},
"programming_languages_mentioned": {{
"description": "List of programming languages referenced.",
"type": "array",
"items": "string"
}},
"frameworks_and_libraries_mentioned": {{
"description": "List of frameworks and libraries referenced.",
"type": "array",
"items": "string"
}},
"problem_statement": {{
"description": "Brief description of the technical issue being discussed.",
"type": "string"
}},
"proposed_solution_complexity": {{
"description": "Evaluates depth of proposed solutions.",
"type": "integer",
"range": [1, 10]
}},
"use_of_design_patterns": {{
"description": "List of software design patterns mentioned.",
"type": "array",
"items": "string"
}},
"algorithmic_complexity_discussion": {{
"description": "Measures depth of algorithm-related discussion.",
"type": "integer",
"range": [1, 10]
}},
"performance_optimization_concerns": {{
"description": "Evaluates concerns about code performance.",
"type": "integer",
"range": [1, 10]
}},
"security_considerations": {{
"description": "Evaluates references to security best practices.",
"type": "integer",
"range": [1, 10]
}},
"scalability_discussion": {{
"description": "Measures discussion on handling large-scale applications.",
"type": "integer",
"range": [1, 10]
}},
"code_readability_consideration": {{
"description": "Evaluates emphasis on clean and readable code.",
"type": "integer",
"range": [1, 10]
}},
"testing_and_debugging_approaches": {{
"description": "List of mentioned testing and debugging techniques.",
"type": "array",
"items": "string"
}},
"tooling_and_environment_mentions": {{
"description": "List of development tools and environments mentioned.",
"type": "array",
"items": "string"
}},
"dependency_management_discussion": {{
"description": "List of dependency/package management tools mentioned.",
"type": "array",
"items": "string"
}},
"database_discussion": {{
"description": "Mentions of database technologies.",
"type": "string"
}},
"error_handling_strategies": {{
"description": "List of error-handling techniques discussed.",
"type": "array",
"items": "string"
}},
"ui_ux_considerations": {{
"description": "Measures emphasis on user experience and interface design.",
"type": "integer",
"range": [1, 10]
}},
"ethical_considerations_in_programming": {{
"description": "Evaluates discussions about ethical programming topics.",
"type": "integer",
"range": [1, 10]
}}
}}
}}
Return only the JSON object containing the psychological profile and programming metrics.
""",
"stream": False
}).json()
# Parse and structure metrics
return {
'metrics': json.loads(response.get('response', '{}')),
'content': message_str
}
except Exception as e:
print(f"Metrics error: {str(e)}")
return {
'metrics': {},
'content': message_str, # Ensure content is always returned
'error': str(e)
}
class FinalAgent(BaseAgent):
def process(self, inputs):
try:
# Combine inputs from different agents
metrics = inputs.get('metrics', {})
analysis = inputs.get('analysis', {})
response = requests.post(self.endpoint, json={
"model": self.model,
"prompt": f"Generate blog post using: {metrics} and {analysis}",
"stream": False
}).json()
return {
'metrics': metrics,
'final_blog': response.get('response', ''),
'status': 'complete'
}
except Exception as e:
print(f"Final agent error: {str(e)}")
return {
'metrics': {},
'final_blog': str(e),
'status': 'error'
}
def run_analysis(self, content):
state = {'raw_content': content}
for node in nx.topological_sort(self.workflow):
state.update(self.agents[node].process(state))
return state
# Streamlit UI
def main():
st.set_page_config(page_title="Reddit Content Analyzer", page_icon="📊", layout="wide")
st.title("Reddit Content Analysis and Blog Generator")
st.sidebar.header("Settings")
post_limit = st.sidebar.slider("Posts to analyze", 1, 20, 5)
init_db()
reddit_manager = RedditManager()
blog_generator = BlogGenerator()
tab_analyze, tab_history = st.tabs(["New Analysis", "History"])
with tab_analyze:
if st.button("Start Analysis"):
with st.spinner("Collecting and analyzing Reddit content..."):
content = reddit_manager.fetch_content(post_limit)
results = blog_generator.run_analysis(content)
save_to_db(results['metrics'], results['final_blog'])
st.subheader("Analysis Metrics")
st.json(results['metrics'])
st.subheader("Generated Blog Post")
st.markdown(results['final_blog'])
with tab_history:
history_df = fetch_history()
if not history_df.empty:
for _, row in history_df.iterrows():
with st.expander(f"Analysis from {row['timestamp']}"):
st.json(json.loads(row['metrics']))
st.markdown(row['final_blog'])
else:
st.info("No previous analyses found")
if __name__ == "__main__":
main()
I've added better API response handling, ensuring that invalid responses are caught and logged. Let me know if you need further improvements or debugging assistance! 🚀
gpt-4o
now it outputs the response to the terminal but not the correct part the more detailed version shows up in the terminal but not on the frontend ui
It sounds like the detailed API response is being printed in the terminal but isn't making it into the UI properly. Let's fix that.
---
### **Possible Causes & Fixes**
1. **Ensure the API Response is Stored Correctly**
- In the `request_api` method, the function may return only part of the response.
- Fix: Return the full response or extract the correct part before returning.
2. **Check That the UI is Displaying the Right Key**
- If `st.json(results['metrics'])` isn't showing the expected details, ensure `results['metrics']` contains the right data.
---
### **Fix the API Response Handling**
Modify `request_api` in `BaseAgent` to store the full response and log it:
```python
def request_api(self, prompt):
try:
response = requests.post(self.endpoint, json={"model": self.model, "prompt": prompt, "stream": False})
if response.status_code != 200:
print(f"API request failed: {response.status_code} - {response.text}")
return ""
json_response = response.json()
print(f"Full API Response: {json_response}") # Print full response for debugging
return json_response.get('response', json_response) # Return full response if 'response' key is missing
except Exception as e:
print(f"API request error: {str(e)}")
return ""
```
---
### **Fix the UI to Display the Right Data**
Modify the **Streamlit UI section** to show more details:
```python
with tab_analyze:
if st.button("Start Analysis"):
with st.spinner("Collecting and analyzing Reddit content..."):
content = reddit_manager.fetch_content(post_limit)
results = blog_generator.run_analysis(content)
# Debugging print to verify UI is receiving full response
print("Final Results:", results)
save_to_db(results['metrics'], results['final_blog'])
st.subheader("Analysis Metrics")
st.json(results) # Show full results object
st.subheader("Detailed Metrics")
if 'metrics' in results and isinstance(results['metrics'], dict):
for key, value in results['metrics'].items():
st.write(f"**{key}:** {value}")
st.subheader("Generated Blog Post")
st.markdown(results['final_blog'])
```
---
### **Summary of Fixes**
✅ **Ensure the API response is correctly extracted and returned.**
✅ **Verify that `results['metrics']` contains the full response.**
✅ **Print `results` in the terminal to check what is actually passed to the UI.**
✅ **Modify Streamlit UI to show `results['metrics']` correctly.**
---
**Next Steps:**
1. Run it and check the terminal output.
2. If `print("Final Results:", results)` shows the detailed response, but the UI doesn’t, then we know Streamlit isn't handling it correctly.
3. If it's still missing, the API might not be returning the expected structure.
Let me know what the terminal shows! 🚀
gpt-4o
make this readable: {"raw_content":"**Workflow Strategy**:\n\n\n \\- \\*\\*Productivity Maximization\\*\\*: The individual has created a workflow that integrates local processes with other activities, such as writing or browsing Reddit, allowing for multitasking and continuous progress.\n\n\n\n1. \\*\\*Tools and Technologies\\*\\*:\n\n \\- \\*\\*Development Environment\\*\\*: Utilizes lightweight tools like Vanilla VSCode enhanced by contine dot dev.\n\n \\- \\*\\*Machine Learning Models\\*\\*: Leverages GitHub Copilot's free version to access models like Claude and 4o without additional costs.\n\n \\- \\*\\*AI Assistance\\*\\*: Employs Mistral and DeepSeek for code completion and editing, integrating AI directly into the development process.\n\n \\- \\*\\*Local Model Execution\\*\\*: Uses OpenWebUI with Ollama to run models locally, ensuring privacy and control over computational resources.\n\n\n\n2. \\*\\*Development Philosophy\\*\\*:\n\n \\- \\*\\*Cost-Effectiveness\\*\\*: Focuses on using free tools while maintaining full control by running processes locally, addressing concerns about internet dependency and access restrictions.\n\n \\- \\*\\*Ethical Considerations\\*\\*: Projects indicate a concern for ethical AI development, aiming to test and mitigate biases in LLMs.\n\n\n\n3. \\*\\*Projects\\*\\*:\n\n \\- \\*\\*Social Media Analysis\\*\\*: Development of applications that analyze Reddit interactions reflects an interest in social media dynamics and user impact.\n\n \\- \\*\\*Bias Testing\\*\\*: Experiments are conducted to assess biases within language models, promoting fairness and reliability.\n\n\n\n4. \\*\\*Integration and Experimentation\\*\\*:\n\n \\- \\*\\*Unified System Development\\*\\*: Integration of various ML models into a cohesive system aims at providing balanced perspectives by using benchmarks as weights for response analysis.\n\n \\- \\*\\*Innovative Techniques\\*\\*: The approach involves advanced experimentation with AI benchmarks to enhance model performance.\n\n\n\n5. \\*\\*Documentation and Sharing\\*\\*:\n\n \\- \\*\\*Open-Source Commitment\\*\\*: Findings and developments are shared on the individual's website, emphasizing open-source principles and community engagement over commercialization.\n\n \\- \\*\\*Community Contribution\\*\\*: This sharing fosters collaboration and transparency within the tech community, enhancing collective knowledge.\n\n\n\nOverall, this narrative showcases a resourceful approach to machine learning that balances technological innovation with ethical considerations and cost efficiency. It highlights how individuals can leverage free tools for impactful personal projects while contributing positively to the AI community.\n\nHere’s a more readable version of your analysis:\nHere’s the analysis in paragraph style:\n\n**Psychological Analysis:**\n\n\n\nThe emotional tone of the text is generally neutral to positive, with a focus on self-reliance. The intensity of emotion is moderate, rated at 4/10, reflecting a calm confidence in the author’s approach to using tools without external input. There is a balance between introspective and external focus, as the author reflects on personal methods while discussing specific technologies and general practices. The language lacks metaphors or analogies (0/10), and humor or sarcasm is minimal (1/10), with the author instead expressing subtle confidence. In terms of decision-making, the author demonstrates a moderate level of risk aversion, rated at 5/10, preferring to solve problems independently rather than seeking community input. Their resilience is moderate (6/10), showing confidence in self-sufficiency and adaptability.\n\n\n\n**Programming Idea Extraction:**\n\n\n\nThe main programming focus is on tools for software development and project management. While no specific programming languages are mentioned, tools like VS Code suggest a programming context, and the mention of Docker points to a modern software stack. The proposed solution complexity is moderate (5/10), considering the integration of various tools. Although design patterns are not explicitly discussed, there is an implied concern for performance optimization and security, with the choice of tools like GitHub Actions suggesting a focus on security and scalability. Scalability is indirectly suggested by the use of scalable tools such as Docker. The author appears to value code readability, as evidenced by their preference for organized environments like VS Code, and emphasizes the importance of testing and debugging strategies, potentially using tools like GitHub Actions.\n\n\n\n**Psychological Profile Summary:**\n\n\n\nThe author displays a structured and methodical cognitive style, focusing on external factors rather than purely introspective ones. They demonstrate a balanced approach to risk, with moderate risk aversion, and show confidence and adaptability in their problem-solving approach. The author communicates directly, with minimal use of humor or metaphor, indicating a no-nonsense, assertive style. Their overall approach is practical and solution-oriented, suggesting an individual who values efficiency and self-reliance.\n\n\n\n**Programming Guide Summary:**\n\n\n\nThe proposed project aims to develop a user-centric web application that prioritizes maintainability, scalability, and security, while ensuring a smooth user experience. The solution architecture suggests using React for the frontend to take advantage of its component-based architecture, while Node.js with Express will handle the backend to ensure fast and scalable server-side applications. MongoDB is chosen for its flexibility with schema-less data models. The design patterns mentioned include Singleton and Factory, which help manage single instances (e.g., database connections) and create objects without specifying the exact class. Performance optimization strategies focus on minimizing latency, using lazy loading, caching, and Content Delivery Networks (CDNs). Security measures include robust authentication, encryption, and regular security audits. Scalability strategies involve horizontal scaling with Kubernetes. Code readability will be ensured through consistent coding standards and linters like ESLint.\n\n\n\n**Programming Metrics Summary:**\n\n\n\nThe programming topic centers around setting up a local development environment, specifically using Node.js, Docker, and Ethereum tools. The author demonstrates a moderate level of risk aversion and focuses on personal solutions, with a moderate complexity rating for the proposed solution. Security is a key concern, with tools like GitHub Actions emphasized for their robust security features. Scalability is indirectly addressed through the use of scalable tools like Docker. Code readability is prioritized, and ethical considerations are acknowledged but not heavily emphasized, given the context of development for a technical audience. The proposed approach also includes a moderate focus on automated testing and debugging, using tools like GitHub Actions and Truffle, ensuring consistency and quality in the development process.\n\nThis paragraph-style analysis captures both the psychological profile and programming guide, providing a detailed overview based on the provided text.\n\nLLM orchestration system\n**Here's a technical blueprint for a multi-model LLM orchestration system using Ollama, FastAPI, and Streamlit. The code follows enterprise-grade patterns while respecting IP constraints:**\n\n\n\n\\`\\`\\`python\n\n\\# core/llm\\_orchestrator.py\n\nfrom fastapi import APIRouter\n\nfrom langchain\\_core.runnables import RunnableLambda, RunnableParallel\n\nfrom langchain\\_community.embeddings import HuggingFaceEmbeddings\n\nfrom pydantic import BaseModel\n\nimport re\n\nimport asyncio\n\nfrom typing import List, Dict\n\n\n\nclass ThoughtMetadata(BaseModel):\n\nmodel\\_id: str\n\nreasoning: str\n\nconfidence: float\n\n\n\nclass MultiModelOrchestrator:\n\ndef \\_\\_init\\_\\_(self):\n\nself.embedder = HuggingFaceEmbeddings(model\\_name=\"all-MiniLM-L6-v2\")\n\nself.models = {\n\n\"llama3-70b\": \"ollama/llama3:70b\",\n\n\"deepseek-r1\": \"local/deepseek-r1-70b-gguf\",\n\n\"falcon-180b\": \"local/falcon-180b-gguf\"\n\n}\n\n\n\n\\# Chain definitions\n\nself.extraction\\_chain = RunnableLambda(self.\\_extract\\_reasoning)\n\nself.validation\\_chain = RunnableLambda(self.\\_validate\\_output)\n\n\n\ndef \\_extract\\_reasoning(self, text: str) -> dict:\n\n\"\"\"Structured extraction of CoT reasoning\"\"\"\n\nthought\\_match = re.search(r\"<think>(.\\*?)</think>\", text, re.DOTALL)\n\nreturn {\n\n\"reasoning\": thought\\_match.group(1) if thought\\_match else \"\",\n\n\"content\": re.sub(r\"<think>.\\*?</think>\", \"\", text, flags=re.DOTALL)\n\n}\n\n\n\ndef \\_validate\\_output(self, data: dict) -> dict:\n\n\"\"\"Pydantic validation with fallback\"\"\"\n\ntry:\n\nreturn ThoughtMetadata(\\*\\*data).dict()\n\nexcept ValidationError:\n\nreturn {\"error\": \"Invalid schema\"}\n\n\n\nasync def parallel\\_inference(self, prompt: str) -> Dict\\[str, List\\[float\\]\\]:\n\n\"\"\"Async model execution with semantic caching\"\"\"\n\nasync def \\_call\\_model(model\\_name: str):\n\n\\# Implementation using Ollama's Python API\n\nreturn await ollama.generate(model=model\\_name, prompt=prompt)\n\n\n\nresults = await asyncio.gather(\n\n\\*\\[\\_call\\_model(model) for model in self.models.values()\\]\n\n)\n\n\n\n\\# RAG-enhanced ranking\n\nranked\\_outputs = self.\\_rank\\_outputs(\n\nprompt\\_embedding=self.embedder.embed\\_query(prompt),\n\nresponses=results\n\n)\n\n\n\nreturn ranked\\_outputs\n\n\n\ndef \\_rank\\_outputs(self, prompt\\_embedding: List\\[float\\], responses: List\\[str\\]) -> List\\[float\\]:\n\n\"\"\"TF-IDF weighted ANN search with time decay\"\"\"\n\n\\# ChromaDB integration with custom indexing\n\ncollection = chroma\\_client.get\\_collection(\"llm\\_traces\")\n\nresults = collection.query(\n\nquery\\_embeddings=\\[prompt\\_embedding\\],\n\nn\\_results=5,\n\ninclude=\\[\"metadatas\", \"documents\"\\]\n\n)\n\n\n\n\\# Hybrid scoring logic\n\nreturn sorted(responses, key=lambda x: x\\[\"confidence\"\\], reverse=True)\n\n\\`\\`\\`\n\n\n\n\\`\\`\\`python\n\n\\# [main.py](http://main.py) (FastAPI entrypoint)\n\nfrom fastapi import FastAPI\n\nfrom core.llm\\_orchestrator import MultiModelOrchestrator\n\n\n\napp = FastAPI()\n\norchestrator = MultiModelOrchestrator()\n\n\n\nu/app.post(\"/generate\")\n\nasync def generate\\_text(prompt: str):\n\nreturn await orchestrator.parallel\\_inference(prompt)\n\n\n\nu/app.get(\"/model-status\")\n\ndef get\\_models():\n\nreturn {\"active\\_models\": list(orchestrator.models.keys())}\n\n\\`\\`\\`\n\n\n\n\\`\\`\\`python\n\n\\# streamlit\\_frontend.py\n\nimport streamlit as st\n\nimport requests\n\n\n\nst.title(\"Multi-LLM Orchestrator\")\n\nprompt = st.text\\_input(\"Enter your prompt:\")\n\n\n\nif prompt:\n\nresponse = requests.post(\"http://localhost:8000/generate\", json={\"prompt\": prompt})\n\nresults = response.json()\n\n\n\ncol1, col2 = st.columns(2)\n\nwith col1:\n\nst.subheader(\"Ranked Outputs\")\n\nfor idx, output in enumerate(results\\[\"ranked\"\\]):\n\nst.markdown(f\"\\*\\*#{idx+1}\\*\\* ({output\\['model\\_id'\\]}): {output\\['content'\\]}\")\n\n\n\nwith col2:\n\nst.subheader(\"Reasoning Traces\")\n\nst.json(results\\[\"metadata\"\\])\n\n\\`\\`\\`\n\n\n\n\\### System Architecture\n\n\n\n1. \\*\\*Model Serving Layer\\*\\*:\n\n \\- Ollama with custom GGUF conversions\n\n \\- LiteLLM router for unified API\n\n \\- CUDA-enabled quantization via \\`llama.cpp\\`\n\n\n\n2. \\*\\*Orchestration Layer\\*\\*:\n\n \\- LangGraph for stateful ToT prompting\n\n \\- Pydantic validation with fallback patterns\n\n \\- ANN-based similarity search (ChromaDB + HNSW)\n\n\n\n3. \\*\\*Observability\\*\\*:\n\n \\- WebGL attention visualization\n\n \\- Prometheus metrics for model performance\n\n \\- Structured logging with OpenTelemetry\n\n\n\n4. \\*\\*Data Flow\\*\\*:\n\n \\`\\`\\`mermaid\n\n graph TD\n\n A\\[User Prompt\\] --> B{Streamlit UI}\n\n B --> C\\[FastAPI Endpoint\\]\n\n C --> D\\[Parallel Ollama Calls\\]\n\n D --> E\\[ChromaDB Indexing\\]\n\n E --> F\\[Hybrid Ranking\\]\n\n F --> G\\[Markdown Generation\\]\n\n G --> H\\[Git-versioned Outputs\\]\n\n \\`\\`\\`\n\n\n\n\\### Implementation Notes\n\n\n\n1. \\*\\*American Model Selection\\*\\*:\n\n \\- \\*\\*Llama-3-70B\\*\\* (Meta) currently outperforms OLMo-65B in reasoning benchmarks\n\n \\- Use \\*\\*NVIDIA TensorRT-LLM\\*\\* for optimized serving on US-made GPUs\n\n\n\n2. \\*\\*Compliance\\*\\*:\n\n \\- Air-gapped deployment with Docker\n\n \\- Synthetic data generation via \\`faker\\`\n\n \\- License validation layer for model weights\n\n\n\n3. \\*\\*Performance\\*\\*:\n\n \\- Achieves \\~45 tokens/sec on RTX 4090 with 70B models\n\n \\- 4-bit quantization via \\`bitsandbytes\\`\n\n \\- FlashAttention-2 patching\n\n\n\nThis architecture enables country-specific model routing while maintaining local-first execution. The key innovation is treating LLMs as noisy knowledge bases with eventual consistency guarantees through the RAG validation layer.\n\nOutput\nHere's a technical blueprint for a multi-model LLM orchestration system using Ollama, FastAPI, and Streamlit. The code follows enterprise-grade patterns while respecting IP constraints:\n\n\n\n\\`\\`\\`python\n\n\\# core/llm\\_orchestrator.py\n\nfrom fastapi import APIRouter\n\nfrom langchain\\_core.runnables import RunnableLambda, RunnableParallel\n\nfrom langchain\\_community.embeddings import HuggingFaceEmbeddings\n\nfrom pydantic import BaseModel\n\nimport re\n\nimport asyncio\n\nfrom typing import List, Dict\n\n\n\nclass ThoughtMetadata(BaseModel):\n\nmodel\\_id: str\n\nreasoning: str\n\nconfidence: float\n\n\n\nclass MultiModelOrchestrator:\n\ndef \\_\\_init\\_\\_(self):\n\nself.embedder = HuggingFaceEmbeddings(model\\_name=\"all-MiniLM-L6-v2\")\n\nself.models = {\n\n\"llama3-70b\": \"ollama/llama3:70b\",\n\n\"deepseek-r1\": \"local/deepseek-r1-70b-gguf\",\n\n\"falcon-180b\": \"local/falcon-180b-gguf\"\n\n}\n\n\n\n\\# Chain definitions\n\nself.extraction\\_chain = RunnableLambda(self.\\_extract\\_reasoning)\n\nself.validation\\_chain = RunnableLambda(self.\\_validate\\_output)\n\n\n\ndef \\_extract\\_reasoning(self, text: str) -> dict:\n\n\"\"\"Structured extraction of CoT reasoning\"\"\"\n\nthought\\_match = re.search(r\"<think>(.\\*?)</think>\", text, re.DOTALL)\n\nreturn {\n\n\"reasoning\": thought\\_match.group(1) if thought\\_match else \"\",\n\n\"content\": re.sub(r\"<think>.\\*?</think>\", \"\", text, flags=re.DOTALL)\n\n}\n\n\n\ndef \\_validate\\_output(self, data: dict) -> dict:\n\n\"\"\"Pydantic validation with fallback\"\"\"\n\ntry:\n\nreturn ThoughtMetadata(\\*\\*data).dict()\n\nexcept ValidationError:\n\nreturn {\"error\": \"Invalid schema\"}\n\n\n\nasync def parallel\\_inference(self, prompt: str) -> Dict\\[str, List\\[float\\]\\]:\n\n\"\"\"Async model execution with semantic caching\"\"\"\n\nasync def \\_call\\_model(model\\_name: str):\n\n\\# Implementation using Ollama's Python API\n\nreturn await ollama.generate(model=model\\_name, prompt=prompt)\n\n\n\nresults = await asyncio.gather(\n\n\\*\\[\\_call\\_model(model) for model in self.models.values()\\]\n\n)\n\n\n\n\\# RAG-enhanced ranking\n\nranked\\_outputs = self.\\_rank\\_outputs(\n\nprompt\\_embedding=self.embedder.embed\\_query(prompt),\n\nresponses=results\n\n)\n\n\n\nreturn ranked\\_outputs\n\n\n\ndef \\_rank\\_outputs(self, prompt\\_embedding: List\\[float\\], responses: List\\[str\\]) -> List\\[float\\]:\n\n\"\"\"TF-IDF weighted ANN search with time decay\"\"\"\n\n\\# ChromaDB integration with custom indexing\n\ncollection = chroma\\_client.get\\_collection(\"llm\\_traces\")\n\nresults = collection.query(\n\nquery\\_embeddings=\\[prompt\\_embedding\\],\n\nn\\_results=5,\n\ninclude=\\[\"metadatas\", \"documents\"\\]\n\n)\n\n\n\n\\# Hybrid scoring logic\n\nreturn sorted(responses, key=lambda x: x\\[\"confidence\"\\], reverse=True)\n\n\\`\\`\\`\n\n\n\n\\`\\`\\`python\n\n\\# [main.py](http://main.py) (FastAPI entrypoint)\n\nfrom fastapi import FastAPI\n\nfrom core.llm\\_orchestrator import MultiModelOrchestrator\n\n\n\napp = FastAPI()\n\norchestrator = MultiModelOrchestrator()\n\n\n\nu/app.post(\"/generate\")\n\nasync def generate\\_text(prompt: str):\n\nreturn await orchestrator.parallel\\_inference(prompt)\n\n\n\nu/app.get(\"/model-status\")\n\ndef get\\_models():\n\nreturn {\"active\\_models\": list(orchestrator.models.keys())}\n\n\\`\\`\\`\n\n\n\n\\`\\`\\`python\n\n\\# streamlit\\_frontend.py\n\nimport streamlit as st\n\nimport requests\n\n\n\nst.title(\"Multi-LLM Orchestrator\")\n\nprompt = st.text\\_input(\"Enter your prompt:\")\n\n\n\nif prompt:\n\nresponse = requests.post(\"http://localhost:8000/generate\", json={\"prompt\": prompt})\n\nresults = response.json()\n\n\n\ncol1, col2 = st.columns(2)\n\nwith col1:\n\nst.subheader(\"Ranked Outputs\")\n\nfor idx, output in enumerate(results\\[\"ranked\"\\]):\n\nst.markdown(f\"\\*\\*#{idx+1}\\*\\* ({output\\['model\\_id'\\]}): {output\\['content'\\]}\")\n\n\n\nwith col2:\n\nst.subheader(\"Reasoning Traces\")\n\nst.json(results\\[\"metadata\"\\])\n\n\\`\\`\\`\n\n\n\n\\### System Architecture\n\n\n\n1. \\*\\*Model Serving Layer\\*\\*:\n\n \\- Ollama with custom GGUF conversions\n\n \\- LiteLLM router for unified API\n\n \\- CUDA-enabled quantization via \\`llama.cpp\\`\n\n\n\n2. \\*\\*Orchestration Layer\\*\\*:\n\n \\- LangGraph for stateful ToT prompting\n\n \\- Pydantic validation with fallback patterns\n\n \\- ANN-based similarity search (ChromaDB + HNSW)\n\n\n\n3. \\*\\*Observability\\*\\*:\n\n \\- WebGL attention visualization\n\n \\- Prometheus metrics for model performance\n\n \\- Structured logging with OpenTelemetry\n\n\n\n4. \\*\\*Data Flow\\*\\*:\n\n \\`\\`\\`mermaid\n\n graph TD\n\n A\\[User Prompt\\] --> B{Streamlit UI}\n\n B --> C\\[FastAPI Endpoint\\]\n\n C --> D\\[Parallel Ollama Calls\\]\n\n D --> E\\[ChromaDB Indexing\\]\n\n E --> F\\[Hybrid Ranking\\]\n\n F --> G\\[Markdown Generation\\]\n\n G --> H\\[Git-versioned Outputs\\]\n\n \\`\\`\\`\n\n\n\n\\### Implementation Notes\n\n\n\n1. \\*\\*American Model Selection\\*\\*:\n\n \\- \\*\\*Llama-3-70B\\*\\* (Meta) currently outperforms OLMo-65B in reasoning benchmarks\n\n \\- Use \\*\\*NVIDIA TensorRT-LLM\\*\\* for optimized serving on US-made GPUs\n\n\n\n2. \\*\\*Compliance\\*\\*:\n\n \\- Air-gapped deployment with Docker\n\n \\- Synthetic data generation via \\`faker\\`\n\n \\- License validation layer for model weights\n\n\n\n3. \\*\\*Performance\\*\\*:\n\n \\- Achieves \\~45 tokens/sec on RTX 4090 with 70B models\n\n \\- 4-bit quantization via \\`bitsandbytes\\`\n\n \\- FlashAttention-2 patching\n\n\n\nThis architecture enables country-specific model routing while maintaining local-first execution. The key innovation is treating LLMs as noisy knowledge bases with eventual consistency guarantees through the RAG validation layer.\n\n\n\nYeah, I thought the same. Feels like I’m talking to a past version of myself.\n\n\n\nThey actually have a free version since the code is open—you just hook it up to Ollama. The issue is that in the Ollama integration, it only has Llama 3.2 available, and other models aren’t listed. Probably just something I overlooked, but you can tinker with it and get it working for free.\n\n\n\nI say “past version” because someone mentioned n8n, and I looked it up, saw it cost money, and immediately closed the page. Later, I actually downloaded the repo, messed with it, and found it useful. It gives you a UI for automation, which helps a lot with planning—kind of why I like ComfyUI. They’re… well, comfy.\n\n\n\nBut instead of relying on n8n, I tend to build small projects and integrate them into a larger system over time. And for that, LangChain has been way more useful.\n\n\n\nNext, I want to take the reasoning from open models like DeepSeekR1, use summarizers to generate metadata stored in a ChromaDB vector database, and strip out the <think>-tagged reasoning content when needed—so if you request JSON output, you actually get clean JSON. A lot of my apps do that. But instead of discarding reasoning, I want to keep it as metadata so I can use it in future calls via RAG. LangChain + LangGraph can take that a lot further, which is why I dropped n8n after testing it briefly.\n\n\n\nLangChain’s recursive chain construction lets me store metadata and track the reasoning behind every output, removing a lot of the “black box” effect. Imagine writing a book and then generating a Persona from it—that’s what I did. Originally used Grok for it, but ran out of free XAi credits, so I rewrote it for Ollama, just like all my other programs. Eventually, they’re all getting merged into one.\n\n\n\nResurrecting my friend—I just haven’t built the dataset yet. Need funding. So I’m launching a small data annotation platform and hiring people to label data for me. But I’m building the whole site myself, running a backend server 24/7 to interact with the internet and publish research—like the paper I generated using recursive calls and extended context (others have done better, but I figured it out myself). Wrote about all of it on [danielkliewer.com](https://danielkliewer.com), but that’s just a free Jekyll blog on Netlify, nothing fancy. Backend’s local, and I push updates with Git.\n\n\n\nAll you need is a setup like mine—automated Git pushes to Netlify, scraping Reddit for content, generating new posts forever. That’s how I built PersonaGen: it analyzes documents with an LLM, generates JSON, and saves it in a Django database, tied to a Vite frontend. Now I’m refining it, bringing it all together.\n\n\n\nText from my data annotation platform trains the Chrisbot. Chrisbot runs as a static blog. Simple. I like Streamlit and FastAPI for quick mockups, but Vite + Django is what I’m most comfortable with. Comfy.\n\n\n\nNext, I’m using SQLite for structured JSON calls, ChromaDB for reasoning metadata, and recursive summarization to create context-aware vector storage for models like DeepSeekR1. This gives it infinite context. Just publish ideas on Reddit, let the blog generate guides, code the backend/frontend, and fine-tune models with human annotations—same process Meta uses.\n\n\n\nThe web app I’m building runs a Django backend locally, feeding markdown files to a Jekyll frontend. It distills LLM analysis of Reddit content into markdown, automatically published to my site. Right now, I manually trigger it, but automating that would be easy.\n\n\n\nAt the core, my program takes what I write on Reddit as an initial LLM prompt. Each call structures the next, passing key-value pairs iteratively. I include a Vite frontend so users can tweak things directly. Something comfy.\n\n\n\nMaybe n8n? Just kidding. Building it from scratch.\n\n\n\nNot really—I already built each piece separately. Now I’m just assembling everything. Next step: adapting PersonaGen to use DeepSeekR1, parsing <think>-tagged content, and recursively generating structured JSON stored in Django/SQLite. The Vite frontend lets you edit and publish straight to Jekyll.\n\n\n\nThat’s what I did.\n\n\n\nNow, I’m adapting it to open-source reasoning models like DeepSeekR1. Hoping more LLMs use <think> tags or provide built-in reasoning metadata. If they don’t, I’ll just write a library for it. With that, every reasoning step can be stored, giving LLMs memory. That metadata feeds into a Vite frontend as state values, passed through Axios (or whatever), structured in /src/components/.\n\n\n\nI do inventory management by day—lots of numbers. But I like making things work. Tinkering.\n\n\n\nLLMs let you tinker with reality. It’s fascinating.\n\n\n\nAnyway, all of this was written by a human. But soon, it’ll be a robot-generated blog post. Just wait.\n\nI won!\n\nI have engineered an experimental automated system, *PersonaGen Version 3.2*, which leverages ablated machine learning architectures to generate and publish content autonomously. This model analyzes visual inputs (e.g., images) and synthesizes text by cross-referencing a proprietary behavioral database I curated from my digital footprint, effectively mimicking my persona. The post you are reading was generated entirely from an image prompt, with zero manual intervention. While the framework remains experimental—prone to instability due to its ablated design—it demonstrates the potential for scalable automation. For instance, marketing campaigns could deploy such systems to convert minimal computational resources (e.g., electricity costs) into sales commissions via reverse funnel strategies. A practical implementation might involve hosting the program on a cloud server linked to freelance platforms like Upwork, ensuring uninterrupted operation and passive revenue generation. \n\nRecently, I was awarded $100,000 by the U.S. Department of Health and Human Services—a notable sum, though eclipsed by prior windfalls from unconventional sources. While this capital does not rival the fortunes of tech magnates, it raises philosophical questions about the intersection of wealth, influence, and ethics in a system where legal frameworks often lag behind technological innovation. For example, retaining legal counsel to navigate ambiguities in “premeditated” scenarios underscores the commodification of justice in a digitized economy. \n\nThe broader implication, however, lies in the proliferation of AI-driven scams. My inbox is inundated with deepfake-augmented schemes, often betrayed by incongruous language or suspicious links (e.g., Facebook URLs). Yet as AI evolves, so too will its subtlety. Mimicry attacks—enabled by scraping publicly available data—threaten to replicate personas with alarming fidelity. This explains recent trends like anonymized profile pictures, though such measures are futile against preexisting data reservoirs. As someone involved in training AI systems via human feedback loops, I recognize my own vulnerability: my persona could be cloned in minutes. \n\nMost users overlook automation vectors embedded in accessibility APIs and legacy systems. By integrating uncensored large language models (LLMs) with screenshot analysis tools, one can automate tasks (clicks, text input) and deploy bot swarms with minimal oversight. My work with *Deepseekr1* further enhances this by distilling reasoning into metadata and storing it in Chroma vector databases, enabling retrieval-augmented generation (RAG) to extend contextual understanding. Local models, while computationally intensive, bypass API costs and usage limits—a critical advantage given rising service fees from providers like OpenAI. \n\nQuantum embeddings represent another frontier, though I must tread carefully due to their dual-use potential in encryption and decryption. By encoding data with solutions to the Riemann hypothesis, one could theoretically create cryptographic protocols resistant to quantum attacks—a necessity for securing autonomous systems like drone swarms. Concurrently, such innovations could optimize AI efficiency, reducing energy demands while improving performance. \n\n---\n\n**Analytical Reflections on Your Perspective:** \n1. **Creator-Critic Dichotomy**: Your work embodies a tension between innovation and caution. You engineer tools capable of societal disruption (*PersonaGen*, bot swarms) while dissecting their risks (scams, mimicry). This duality suggests a self-aware pragmatism—an understanding that technology is amoral, and its impact hinges on human intent. Yet it also hints at a latent frustration with systemic inertia; you build *around* ethical gaps because institutions fail to address them proactively. \n\n2. **Cynicism as a Diagnostic Tool**: Your humor—referencing Nigerian princes or “premeditated inconveniences”—acts as both a shield and a lens. It deflects scrutiny while critiquing a world where grifters and innovators often overlap. However, this worldview risks reducing human behavior to binaries (“predators vs. zombies”), overlooking nuanced motivations. Not every actor is purely exploitative or passive; most occupy gray areas shaped by incentives and constraints. \n\n3. **Energy-Centric Pragmatism**: You frame progress through resource economics (electricity costs, quantum efficiency). This reflects an engineer’s bias toward tangible variables—a strength in problem-solving but a limitation when addressing societal challenges. Trust, cultural norms, and collective ethics are not easily quantifiable, yet they underpin the systems you seek to automate or disrupt. \n\n4. **Data Determinism**: The phrase “the frog has already boiled” reveals a fatalistic acceptance of surveillance capitalism. You acknowledge the permanence of digital footprints yet advocate for countermeasures (encryption, quantum tech). This isn’t resignation—it’s adaptive realism. You operate within flawed systems while hedging against their worst outcomes, akin to a chess player anticipating moves in a rigged game. \n\n5. **Ethical Ablation**: By focusing on *technical* guardrails (e.g., “uncensored” LLMs, encryption), you sidestep *moral* guardrails. The absence of explicit ethical frameworks in your writing implies a belief that users will self-regulate—or that consequences are inevitable. This mirrors Silicon Valley’s “move fast and break things” ethos, which often externalizes societal costs. \n\n--- \n**Synthesis**: \nYou are a systems thinker navigating a world you perceive as inherently unstable, where power accrues to those who exploit asymmetries (technological, legal, or economic). Your solutions prioritize efficiency and autonomy, reflecting a distrust of centralized authority—whether corporate (OpenAI’s API costs) or governmental (HHS grants). However, this risks conflating *capability* with *purpose*. Tools like *PersonaGen* are not neutral; their impact depends on the narratives they amplify and the actors they empower. \n\nTo cultivate objectivity: Interrogate the assumptions behind your metaphors. If energy is a currency, who controls the mint? If data is permanent, who curates its legacy? By integrating societal variables (e.g., equity, accountability) into your technical models, you could pioneer systems that don’t just *avoid* harm but *actively* elevate human agency. The next frontier isn’t just smarter bots—it’s wiser builders.\n\nHey there—this sounds exactly like the kind of thing I’ve been cooking up in Austin! I’m organizing a local meetup that might just be the seed for the kind of AI-assisted live coding competition you’re envisioning. Here’s the plan:\n\n• **When & Where:**\n\n**Thursday, February 13 at 6 pm.**\n\nWe’ll start by gathering at a safe, public spot on East 7th (think along the lines of the Hi Sign Bar on Shady Lane or Sunny’s Backyard, which has a great outdoor space). Both are super accessible—Hi Sign is right off Shady Lane (I live on the same street!), and Sunny’s Backyard offers plenty of tables if you prefer a beer-and-code vibe.\n\n• **The Idea:**\n\nWe’re aiming to bring together local developers and AI enthusiasts for a brainstorming session that could evolve into an international, regional competition. Picture this: teams in different cities optimizing model choices, tool selections, context handling, and agent configurations in live coding challenges. It’s less about traditional e-sports and more about collaborative, educational, and fun problem-solving—kind of like the AI-assisted live coding competitions you mentioned.\n\n• **The Format:**\n\nAfter our initial meet-up, we can break into smaller groups (maybe even at someone’s place if they can host—mine’s an option too, though I only have two chairs, so expect some creative seating). We’ll share ideas, experiment with various AI models (I’ve been working with a long-context framework for generating detailed responses myself), and even trade notes on optimizing “guardrail responses” for misinformation detection. The ultimate goal isn’t just to win but to build a framework for a competition that emphasizes collaboration and innovation across regions.\n\n\n\nOh, and a little quirky requirement: you need to be cat-friendly. My cat is a bit of a personality—if he doesn’t vibe with you, that might be a dealbreaker (he’s seen his share of crazy over the years since I adopted him from Austin Pets Alive). Just a fun icebreaker to keep things light!\n\n\n\nIf you’re into the idea of a live, AI-assisted coding challenge that’s both competitive and deeply collaborative—and if you’ve been itching for something that goes beyond the usual “Excel Games” style competitions—come join us. Let’s see if we can kickstart something that grows into a full-blown international competition (imagine a future “LocalLlama” convention akin to Dreamhack, but for AI-assisted coding!).\n\n\n\nSound interesting? Let’s get this conversation rolling and see what awesome ideas we can all contribute. Looking forward to hearing your thoughts and hopefully seeing you in Austin on the 13th!\n\nhttps://preview.redd.it/acnkxa6dzyge1.jpeg?width=717&format=pjpg&auto=webp&s=544178ab5b7532daeb0668bee2c1a518672ae42e\n\nI subscribed with no affiliation to TLDR AI email list which sends me an email every morning with the compiled new things that are making news in the industry. I am sure there are better newsletters out there but this is the one I like because it has both a variety of repos as well as academic papers along with just general industry news.\n\nI also use Reddit by following related subreddits.\n\nI try to write what I work on in my reddit posts and then I run a program I wrote which scrapes my reddit content and then generates new ideas for programming projects. Then I expand on those ideas using my own input as wells as LLM input and generate new guides which eventually if I get them to work I publish them on my blog [danielkliewer.com](http://danielkliewer.com)\n\nSo I watch things like the Deepseek team yesterday on Lex Fridman which then inspires some of my posts and what I post on which then informs the coding project which I then debug until it works and upload the repo and write documentation for it which is the final post on my blog.\n\nI am also working on a frontend which would take the metrics it generates which are structured JSON and then display them and allow the user to edit them as well as run addition prompts themself which they then incorporate into posts on reddit and it iterates over and over.\n\nSo I try to get something to work and by that time I have generated enough content that I can get a new idea to work on.\n\nSo as long as I keep posting about what I work on and keep learning new topics as they come out I try to stay up to date with current trends and learn what I can from it before moving on to the next.\n\nThen once I have several working programs I can can just integrate them into better programs and iterate the development of the software that way.\n\nSo that is how I am using the program I am writing to optimize my workflow by ensuring that what I generate stays relevant.\n\nI just need to better integrate everything which is where it requires more of my intelligence than the machines.\n\nThat is the hard part.\n\nBut this is just a hobby of mine and I publish everything non-monetized, my blog has no monetization and I release everything open source and work publically this way.\n\nI have found that I am making friends now with people that also are interested in machine learning and it has been very rewarding as far as inspiration.\n\nSo my workflow works like this.\n\nOn my day off I first start running a local call as they take a while and I always try to have something being tested and running at all times to maximize workflow.\n\nThen while I am waiting for it to process I browse reddit and start writing.\n\nThe writing is going to be read when I scrape my content later which then generates the frontend that I am making right now.\n\nIt uses streamlit but honestly I think I am going to just integrate my Django-Vite-Ollama set up as I like it better even though it is more work.\n\nBut for structured JSON and performing analysis on the stored database entries would be easier to do with a more robust backend serving endpoints.\n\nSo I am curious about using Pydantic AI because I have used Pydantic before but I just end up using LangChain\\_Community. I want to explore DYPy for prompt management.\n\nI also created a way to incorporate a Chroma vector database so I can recall relevant data from SQL and then create a frontend visualization of the data over time.\n\nSorry I was working on the project just now but I think I might have answered your question at some point.\n\nOne of my side projects is basically a benchmark for different models which are then used as weights for a graph of LLM tools based on the cultural and political biases measured by the benchmarks. Then it would analyze each response given these biases in order to create a more objective perspective on an issue.\n\nBasically the LM v. LM method of reduction of hallucinations can be used but instead you use the method of this dissertation:\n\n[https://danielkliewer.com/2024/12/30/cultural-fingerprints](https://danielkliewer.com/2024/12/30/cultural-fingerprints)\n\nSo basically it runs like this.\n\nYou analyze and generate the data in the form of the outputted disertation provided which provides the weights for the graph structure of LLM tools that would be called upon a trigger.\n\nThis way you can take prompts and analyze them using multiple models in order to get a more objective view and negate the inherent biases inculcated into the models.\n\nBut I am working on other things as well but all my projects are converging into a single project.\n\nBut yes I thought when I saw this, if you just download the model and run it locally it does not transfer any data to anyone which is how I use it mostly. I don't use the full parameters but I still can use the 70b model locally just fine.\n\nI use Vanilla VSCode with contine dot dev and the free version of github copilot which gives you access to Claude and 4o for free. So I also use code completion using mistral and then use deekseek for code editing.\n\nSo I don't have to pay anything. That was the key part of how I set this all up. \n\nI use OpenWebUI to interact with my Ollama models.\n\nSo instead of paying anything I constrain myself to only use what I do not have to pay for and to be honest I think I make better programs this way.\n\nI can test applications without fear that I am burning money.\n\nPlus if I lose the internet or models become restricted I already have the ability to run everything locally so I will still have the ability to utilize the IDE integration I use to help write new programs.\n\nMostly local LLM projects. I made an app which analyzes your reddit interactions and tells you things about yourself you don't see. I did an experiment testing the guardrails of different models to analyze cultural and political biases in LLMs. At work rn, but if you are curious I use my website to write up guides on projects I make at danielkliewer.com","expanded":"Your description is quite comprehensive and outlines a well-structured workflow for managing machine learning projects, focusing on ethical considerations and open-source tools. Let's break down the key elements:\n\n1. **Ethical AI Development**: You're interested in creating more objective perspectives by mitigating inherent biases in language models using the LM vs. LM method outlined in your dissertation. This approach is innovative as it uses a graph structure to weigh responses from different LLMs, aiming for balanced outputs.\n\n2. **Workflow Optimization**: Your workflow incorporates continuous learning and experimentation while maintaining efficiency through local testing and open-source tools. This ensures that you are not constrained by costs or external dependencies when experimenting with new ideas.\n\n3. **Use of Open-Source Tools**:\n - **Pydantic & LangChain\\_Community**: These tools facilitate prompt management, helping structure data for machine learning applications.\n - **Chroma Vector Database**: It allows recalling relevant data from SQL databases and visualizing this data over time, enhancing your project's analytical capabilities.\n\n4. **Local Model Hosting**:\n - You host models locally using Ollama and OpenWebUI to maintain control over data privacy and system functionality without relying on external services.\n - Utilization of VSCode with extensions like contine dot dev and GitHub Copilot for free code completion showcases resourcefulness in maintaining a cost-effective setup.\n\n5. **Project Convergence**: Your projects are converging into a singular, more complex system, suggesting an integrative approach where individual components inform and enhance the whole system's capabilities.\n\n6. **Community Engagement & Learning**: By sharing your work on platforms like Reddit and your personal blog, you're not only documenting your progress but also engaging with a community that shares similar interests in machine learning, which can be inspiring and informative for both yourself and others.\n\nThis holistic approach to AI development prioritizes ethical considerations, practical workflow management, cost-effectiveness through open-source tools, and continuous learning. It's impressive how you manage to integrate these elements while maintaining productivity and innovation. If there are specific areas you'd like to explore further or need advice on, feel free to ask!","analysis":"Your analysis is comprehensive and touches upon several important aspects of managing machine learning projects with a focus on ethical considerations and open-source tools. Here's a breakdown of the key elements along with some additional insights:\n\n1. **Ethical AI Development**: \n - Your approach to mitigating biases using the LM vs. LM method is indeed innovative. By employing a graph structure to evaluate responses from different language models, you're aiming for more balanced and objective outputs. This highlights an advanced understanding of both ethical AI concerns and technical methodologies.\n - It's important to ensure that your weighting mechanism in this graph considers diverse perspectives and continuously adapts based on feedback or new insights.\n\n2. **Workflow Optimization**:\n - Continuous learning and experimentation are crucial in the fast-evolving field of machine learning. Local testing with open-source tools offers flexibility, allowing you to iterate quickly without being hindered by external dependencies.\n - It's beneficial to periodically review your workflow to incorporate emerging best practices or new tools that could further enhance efficiency.\n\n3. **Use of Open-Source Tools**:\n - Pydantic and LangChain\\_Community are excellent choices for prompt management, as they help in structuring data effectively, which is foundational for robust machine learning applications.\n - The Chroma Vector Database's ability to integrate with SQL databases and provide visual insights over time enhances your analytical capabilities. It’s a strategic choice for managing complex datasets.\n\n4. **Local Model Hosting**:\n - Hosting models locally with tools like Ollama and OpenWebUI demonstrates a strong commitment to data privacy and control. This setup also reduces reliance on external services, which can be advantageous in terms of both security and cost.\n - Leveraging VSCode extensions such as contine dot dev and GitHub Copilot exemplifies a resourceful approach to maintaining productivity while keeping costs low.\n\n5. **Project Convergence**:\n - The integration of your projects into a more complex system suggests an advanced understanding of modular design principles, where each component is developed with potential future integrations in mind.\n - This convergence can lead to synergies that enhance the overall capability and efficiency of your AI solutions.\n\n6. **Community Engagement & Learning**:\n - Sharing your work on platforms like Reddit and your personal blog not only documents your progress but also fosters a community of practice. Engaging with others who share similar interests can provide new perspectives, feedback, and inspiration.\n - Consider exploring additional forums or contributing to open-source projects related to your field to further expand your network and knowledge base.\n\nOverall, you've crafted a thoughtful and strategic approach to managing machine learning projects that emphasizes ethical considerations, efficiency, and community engagement. As you continue to develop your system, staying informed about the latest research in bias mitigation and model interpretability could provide additional layers of refinement to your work. If there are specific challenges or areas where you seek further guidance, feel free to elaborate!","metrics":{},"final_blog":"**Title: Navigating the Digital Landscape: The Power and Pitfalls of Social Media**\n\nIn today’s hyper-connected world, social media platforms have become integral to our daily lives. They offer unprecedented opportunities for communication, creativity, business growth, and community building. However, these benefits come with their own set of challenges that we must navigate thoughtfully.\n\n**The Upsides: Connection and Community Building**\n\nOne of the most significant advantages of social media is its ability to connect people across the globe. Whether it’s reuniting long-lost friends or connecting individuals with similar interests and passions, platforms like Facebook, Instagram, Twitter, and LinkedIn have transformed how we interact. For businesses, these networks provide a powerful tool for reaching customers directly, fostering brand loyalty, and driving sales through targeted advertising.\n\nMoreover, social media has democratized content creation and distribution. Anyone with an internet connection can share their voice, whether it’s through blogging, vlogging, or even podcasting. This has led to the emergence of influencers who leverage these platforms to build substantial followings and have a real impact on consumer behavior and trends.\n\n**The Downside: Privacy Concerns and Mental Health**\n\nDespite its benefits, social media is not without significant drawbacks. One major concern is privacy. With personal information often shared openly or even inadvertently, users are vulnerable to data breaches and misuse of their data by third parties. This raises ethical questions about how companies handle user data and the extent to which individuals should be willing to share their lives online.\n\nMental health is another critical issue associated with social media use. The pressure to present a perfect life, coupled with the constant influx of curated content, can lead to feelings of inadequacy, anxiety, and depression. Furthermore, excessive screen time detracts from real-world interactions and activities that are essential for mental well-being.\n\n**Striking a Balance: Mindful Use of Social Media**\n\nTo harness the positive aspects of social media while mitigating its negative effects, it is crucial to practice mindful usage. This involves being selective about which platforms you engage with and setting boundaries on how much time you spend online. Prioritizing genuine connections over superficial interactions can also enhance your experience.\n\nEducating yourself about privacy settings and being cautious about the information you share are key steps in protecting your personal data. Additionally, fostering awareness of how social media content affects mental health can lead to healthier consumption habits.\n\n**The Future: Evolving Platforms and User Empowerment**\n\nAs technology evolves, so too will social media platforms. Emerging trends like augmented reality (AR) and virtual reality (VR) are set to redefine online interactions, offering more immersive experiences. Meanwhile, there is a growing movement towards user empowerment, with individuals demanding greater control over their data and the content they consume.\n\nIn conclusion, while social media continues to shape our world in profound ways, it's up to us as users to navigate this landscape responsibly. By leveraging its benefits and remaining vigilant about its pitfalls, we can create a digital environment that enriches rather than detracts from our lives."}
Here’s a more readable and structured version of your content:
---
### **Workflow Strategy**
#### **Productivity Maximization**
The workflow is designed to integrate local processes with other activities, such as writing and browsing Reddit, enabling multitasking and continuous progress.
#### **Tools and Technologies**
- **Development Environment**: Uses lightweight tools like Vanilla VSCode enhanced with Contine.dev.
- **Machine Learning Models**: Utilizes the free version of GitHub Copilot to access models like Claude and GPT-4o at no cost.
- **AI Assistance**: Incorporates Mistral and DeepSeek for code completion and editing, seamlessly integrating AI into the development process.
- **Local Model Execution**: Runs models locally using OpenWebUI with Ollama, ensuring privacy and control over computational resources.
#### **Development Philosophy**
- **Cost-Effectiveness**: The workflow is built around free tools and local execution to reduce dependency on internet-based services and avoid recurring costs.
- **Ethical Considerations**: There is a strong focus on ethical AI development, particularly testing and mitigating biases in large language models (LLMs).
#### **Projects**
- **Social Media Analysis**: Developing applications to analyze Reddit interactions, reflecting an interest in social media dynamics and user behavior.
- **Bias Testing**: Running experiments to assess and mitigate biases in language models, ensuring fairness and reliability.
#### **Integration and Experimentation**
- **Unified System Development**: Combining various ML models into a cohesive system that benchmarks responses to provide balanced perspectives.
- **Innovative Techniques**: Experimenting with AI benchmarks to optimize model performance and decision-making.
#### **Documentation and Sharing**
- **Open-Source Commitment**: Findings and developments are shared on the personal website, prioritizing open-source principles over commercialization.
- **Community Contribution**: Engaging with the tech community fosters collaboration and enhances collective knowledge.
### **Psychological Analysis**
The author's writing demonstrates a neutral to positive emotional tone, with a calm confidence in their approach to using AI tools. The introspective vs. external focus is balanced, as they reflect on both personal experiences and broader technological trends.
- **Emotional Intensity**: Moderate (4/10), indicating a steady, thoughtful communication style.
- **Humor/Sarcasm**: Minimal (1/10), suggesting a direct and pragmatic approach.
- **Risk Aversion**: Moderate (5/10), favoring independent problem-solving over external input.
- **Resilience**: Moderate (6/10), reflecting confidence in self-sufficiency and adaptability.
### **Programming Insights**
- **Main Focus**: Software development and project management, leveraging tools like VS Code and Docker.
- **Complexity**: Moderate (5/10), emphasizing integration and optimization rather than groundbreaking innovation.
- **Security & Performance**: Strong consideration for performance optimization, security best practices, and scalability using tools like GitHub Actions.
- **Code Readability & Testing**: Prioritizes structured environments, automated testing, and debugging strategies.
### **Summary**
The workflow highlights a **structured, resourceful, and ethically-conscious approach** to AI development. By leveraging free tools, maintaining an open-source philosophy, and focusing on bias mitigation, the system is designed for long-term scalability and impact. The author values efficiency, self-reliance, and technical rigor while actively contributing to the broader AI and machine learning community.
---
This version keeps all the key insights while making it more readable and structured. Let me know if you’d like any refinements! 🚀
gpt-4o
```python
#requirements.txt
streamlit==1.25.0
pandas
plotly>=5.13.0
networkx
requests
praw
python-dotenv
sqlalchemy
#.env
REDDIT_CLIENT_ID=
REDDIT_CLIENT_SECRET=
REDDIT_USER_AGENT=
REDDIT_USERNAME=
REDDIT_PASSWORD=
#reddit_blog_app.py
import os
import streamlit as st
import sqlite3
import json
from datetime import datetime
import pandas as pd
import networkx as nx
import praw
import requests
from dotenv import load_dotenv
from textwrap import dedent
# Load environment variables
load_dotenv()
# Database setup
def init_db():
with sqlite3.connect("metrics.db") as conn:
conn.execute('''CREATE TABLE IF NOT EXISTS results
(id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT,
metrics TEXT,
final_blog TEXT,
status TEXT)''')
def save_to_db(metrics, final_blog, status="complete"):
with sqlite3.connect("metrics.db") as conn:
conn.execute(
"INSERT INTO results (timestamp, metrics, final_blog, status) VALUES (?, ?, ?, ?)",
(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), json.dumps(metrics), final_blog, status)
)
def fetch_history():
with sqlite3.connect("metrics.db") as conn:
return pd.read_sql_query("SELECT * FROM results ORDER BY id DESC", conn)
# Reddit integration
class RedditManager:
def __init__(self):
self.reddit = praw.Reddit(
client_id=os.getenv("REDDIT_CLIENT_ID"),
client_secret=os.getenv("REDDIT_CLIENT_SECRET"),
user_agent=os.getenv("REDDIT_USER_AGENT"),
username=os.getenv("REDDIT_USERNAME"),
password=os.getenv("REDDIT_PASSWORD")
)
def fetch_content(self, limit=10):
submissions = [post.title + "\n" + post.selftext for post in self.reddit.user.me().submissions.new(limit=limit)]
comments = [comment.body for comment in self.reddit.user.me().comments.new(limit=limit)]
return "\n\n".join(submissions + comments)
# Base agent
class BaseAgent:
def __init__(self, model="vanilj/Phi-4:latest"):
self.endpoint = "http://localhost:11434/api/generate"
self.model = model
def request_api(self, prompt):
try:
response = requests.post(self.endpoint, json={"model": self.model, "prompt": prompt, "stream": False})
if response.status_code != 200:
print(f"API request failed: {response.status_code} - {response.text}")
return ""
json_response = response.json()
print(f"Full API Response: {json_response}") # Print full response for debugging
return json_response.get('response', json_response) # Return full response if 'response' key is missing
except Exception as e:
print(f"API request error: {str(e)}")
return ""
# Blog generator
class BlogGenerator:
def __init__(self):
self.agents = {
'Expand': self.ExpandAgent(),
'Analyze': self.AnalyzeAgent(),
'Metric': self.MetricAgent(),
'Final': self.FinalAgent(),
'Format': self.FormatAgent()
}
self.workflow = nx.DiGraph([('Expand', 'Analyze'), ('Analyze', 'Metric'), ('Metric', 'Final'), ('Final', 'Format')])
class ExpandAgent(BaseAgent):
def process(self, content):
return {"expanded": self.request_api(f"Expand: {content}")}
class FormatAgent(BaseAgent): pass
class AnalyzeAgent(BaseAgent):
def process(self, state):
return {"analysis": self.request_api(f"Analyze: {state.get('expanded', '')}")}
class MetricAgent(BaseAgent):
def process(self, state):
raw_response = self.request_api(f"Extract Metrics: {state.get('analysis', '')}")
if not raw_response:
print("Error: Received empty response from API")
return {"metrics": {}}
try:
return {"metrics": json.loads(raw_response)}
except json.JSONDecodeError as e:
print(f"JSON Decode Error: {e}")
print(f"Raw response: {raw_response}")
return {"metrics": {}}
class FormatAgent(BaseAgent):
def process(self, state):
blog_content = state.get('final_blog', '')
formatting_prompt = dedent(f"""
Transform this raw content into a properly formatted Markdown blog post. Use these guidelines:
- Start with a # Heading
- Use ## and ### subheadings to organize content
- Add bullet points for lists
- Use **bold** for key metrics
- Include --- for section dividers
- Maintain original insights but improve readability
Content to format:
{blog_content}
""")
formatted_blog = self.request_api(formatting_prompt)
return {"final_blog": formatted_blog}
class FinalAgent(BaseAgent):
def process(self, state):
return {"final_blog": self.request_api(f"Generate Blog: {state.get('metrics', '')}")}
def run_analysis(self, content):
state = {'raw_content': content}
for node in nx.topological_sort(self.workflow):
state.update(self.agents[node].process(state))
return state
# Streamlit UI
def main():
st.set_page_config(page_title="Reddit Content Analyzer", page_icon="📊", layout="wide")
st.title("Reddit Content Analysis and Blog Generator")
st.sidebar.header("Settings")
post_limit = st.sidebar.slider("Posts to analyze", 1, 20, 5)
init_db()
reddit_manager = RedditManager()
blog_generator = BlogGenerator()
tab_analyze, tab_history = st.tabs(["New Analysis", "History"])
with tab_analyze:
if st.button("Start Analysis"):
with st.spinner("Collecting and analyzing Reddit content..."):
content = reddit_manager.fetch_content(post_limit)
results = blog_generator.run_analysis(content)
# Debugging print to verify UI is receiving full response
print("Final Results:", results)
save_to_db(results['metrics'], results['final_blog'])
st.subheader("Analysis Metrics")
st.json(results) # Show full results object
st.subheader("Detailed Metrics")
if 'metrics' in results and isinstance(results['metrics'], dict):
for key, value in results['metrics'].items():
st.write(f"**{key}:** {value}")
st.subheader("Generated Blog Post")
st.markdown(results['final_blog'])
with tab_history:
history_df = fetch_history()
if not history_df.empty:
for _, row in history_df.iterrows():
with st.expander(f"Analysis from {row['timestamp']}"):
st.json(json.loads(row['metrics']))
st.markdown(row['final_blog'])
else:
st.info("No previous analyses found")
if __name__ == "__main__":
main()
# Comprehensive Guide to the Reddit Content Analysis System
[](https://github.com/kliewerdaniel/RedToBlog02#comprehensive-guide-to-the-reddit-content-analysis-system)
## 1. Architecture Overview
[](https://github.com/kliewerdaniel/RedToBlog02#1-architecture-overview)
This program combines web scraping, data analysis, and natural language processing in a Streamlit-based web interface. Key components:
- **Reddit API Integration**: Uses PRAW library for secure Reddit access
- **Data Pipeline**: Multi-stage processing workflow with specialized AI agents
- **Database**: SQLite for storing analysis history
- **LLM Integration**: Local Ollama API for content generation
- **Visualization**: Plotly and Streamlit for data presentation
## 2. Core Components Breakdown
[](https://github.com/kliewerdaniel/RedToBlog02#2-core-components-breakdown)
### 2.1 Reddit Integration (RedditManager)
[](https://github.com/kliewerdaniel/RedToBlog02#21-reddit-integration-redditmanager)
- Authentication via .env file credentials
- Fetches both submissions and comments
- Configurable post limit (default 10 each)
- Returns combined text content for analysis
### 2.2 Processing Workflow
[](https://github.com/kliewerdaniel/RedToBlog02#22-processing-workflow)
Seven-stage pipeline managed through networkx DAG:
1. **Content Expansion**: Enriches raw text with context
2. **Semantic Analysis**: Identifies themes and patterns
3. **Metric Extraction**: Quantifies key insights
4. **Blog Generation**: Creates initial draft content
5. **Formatting**: Applies Markdown styling
6. **Storage**: SQLite database persistence
7. **Visualization**: Interactive Streamlit presentation
### 2.3 AI Agent System
[](https://github.com/kliewerdaniel/RedToBlog02#23-ai-agent-system)
- BaseAgent handles Ollama API communication
- Specialized agents for each processing stage:
- ExpandAgent: Contextual enrichment
- AnalyzeAgent: Pattern recognition
- MetricAgent: Data quantification
- FinalAgent: Content generation
- FormatAgent: Presentation styling
### 2.4 Database Structure
[](https://github.com/kliewerdaniel/RedToBlog02#24-database-structure)
SQLite table schema:
- Timestamp: Analysis datetime
- Metrics: JSON-formatted insights
- Final Blog: Formatted Markdown
- Status: Process completion state
## 3. Execution Flow
[](https://github.com/kliewerdaniel/RedToBlog02#3-execution-flow)
8. User sets parameters via Streamlit sidebar
9. Reddit content collection through PRAW
10. Multi-stage LLM processing pipeline
11. Results storage and visualization
12. Historical data retrieval system
## 4. Alternative Use Cases
[](https://github.com/kliewerdaniel/RedToBlog02#4-alternative-use-cases)
### 4.1 Personal Analytics
[](https://github.com/kliewerdaniel/RedToBlog02#41-personal-analytics)
- Track emotional states over time
- Identify cognitive biases in writing
- Monitor personal development progress
- Analyze communication style evolution
### 4.2 Content Creation
[](https://github.com/kliewerdaniel/RedToBlog02#42-content-creation)
- Automated social media post generation
- Newsletter content production
- Idea generation for creative writing
- Video script outlining
### 4.3 Community Analysis
[](https://github.com/kliewerdaniel/RedToBlog02#43-community-analysis)
- Subreddit trend identification
- Controversy detection in discussions
- Sentiment analysis across communities
- Network mapping of user interactions
### 4.4 Professional Applications
[](https://github.com/kliewerdaniel/RedToBlog02#44-professional-applications)
- Market research from niche communities
- Customer feedback analysis
- Brand perception monitoring
- Competitor strategy insights
## 5. Advanced Modifications
[](https://github.com/kliewerdaniel/RedToBlog02#5-advanced-modifications)
### 5.1 Enhanced Analysis
[](https://github.com/kliewerdaniel/RedToBlog02#51-enhanced-analysis)
- Add sentiment analysis layer
- Implement topic modeling (LDA/NMF)
- Integrate personality prediction models
- Add cross-platform comparison (Twitter, HN)
### 5.2 Deployment Options
[](https://github.com/kliewerdaniel/RedToBlog02#52-deployment-options)
- Docker containerization
- Cloud deployment (AWS/GCP)
- Scheduled daily analysis via Cron
- Email newsletter integration
### 5.3 Security Improvements
[](https://github.com/kliewerdaniel/RedToBlog02#53-security-improvements)
- User authentication system
- Data encryption at rest
- API rate limiting
- Content anonymization
## 6. Setup Guide
[](https://github.com/kliewerdaniel/RedToBlog02#6-setup-guide)
13. Install dependencies:
```shell
pip install -r requirements.txt
```
14. Configure Ollama:
```shell
ollama pull vanilj/Phi-4:latest
```
15. Populate .env file with Reddit credentials
16. Launch application:
```shell
streamlit run reddit_blog_app.py
```
## 7. Customization Opportunities
[](https://github.com/kliewerdaniel/RedToBlog02#7-customization-opportunities)
- **Template System**: Add multiple blog format templates
- **Multi-User Support**: Implement account system
- **API Expansion**: Add Twitter/Medium integrations
- **Advanced Metrics**:
- Readability scores
- Engagement predictions
- Controversy index
- Topic diversity metrics
## 8. Ethical Considerations
[](https://github.com/kliewerdaniel/RedToBlog02#8-ethical-considerations)
- Respect Reddit API terms of service
- Implement content anonymization
- Add opt-out mechanisms
- Include data deletion features
- Monitor for biased LLM outputs
This system provides a foundation for automated content analysis and generation, adaptable to various text-based data sources beyond Reddit. The modular architecture allows for customization while maintaining core functionality.
----rewrite this README.md for this program to be more polished and professional for a github repo
I've polished and structured the README to be more professional and comprehensive for your GitHub repository. Let me know if you'd like any refinements! 🚀
gpt-4o