Reviving TruthGuard AI: From Zero History to Full CRUD with GitHub Copilot

Awaisranahmad — Fri, 22 May 2026 05:20:29 +0000

This is a submission for the GitHub Finish-Up-A-Thon Challenge.

🛡️ What I Built

TruthGuard AI is an enterprise-grade fake news detection and fact-checking tool that combines real-time web search with advanced LLM analysis. Users can paste any claim, news excerpt, or social media post, and the tool:

Searches the live web using Tavily's search API
Analyzes evidence using Groq's Llama 3.3-70B model
Provides a structured verdict with veracity score (0-100%)
Identifies logical fallacies and emotional manipulation
Offers a follow-up chat to interrogate the evidence

The application is built with Streamlit for the frontend, LangChain for LLM orchestration, Tavily for live web search, and Groq's Llama 3.3-70B for fast, high-quality inference.

This project started as a hackathon prototype where I had the core verification logic working, but it lacked persistence, testing, proper environment configuration, and a polished user experience. It was functional but incomplete — perfect for this challenge.

Demo

🔗 GitHub Repository: https://github.com/Awaisranahmad/AI-Based-Content-Verification-Tool

Before (Abandoned State):

After (Revived & Polished):

GitHub Copilot in Action:

📸 Before vs After — The Comeback Story

Where It Was (The "Before")

The original version of TruthGuard AI was a working prototype that proved the concept but had significant gaps:

Issue	Impact
❌ No history persistence	Results disappeared on page refresh
❌ No .env support	API keys only worked via Streamlit secrets; local development required manual editing
❌ No unit tests	Zero test coverage — fragile to API changes
❌ No delete or load functionality	History items stuck forever
❌ No "New Chat" option	No way to reset session
❌ Deprecated `st.experimental_rerun()`	Caused AttributeError in newer Streamlit versions

The code worked for me, but nobody else could use it reliably. There was no proper database setup, no environment configuration, and no way to revisit past verifications.

What Changed (The "After")

I transformed TruthGuard AI from a prototype into a production-ready application with GitHub Copilot writing most of the new infrastructure.

Feature	Before	After
History	❌ None	✅ SQLite database with timestamps
Load old claims	❌ Not possible	✅ One-click load with full report
Delete history	❌ Not possible	✅ Delete button per item
New chat	❌ No reset	✅ Reset all session state
Environment	❌ Hardcoded/st.secrets only	✅ `.env` + st.secrets fallback
Unit tests	❌ 0 tests	✅ 3 passing tests
API calls on load	❌ N/A	✅ Load uses saved report (no re-verification)
Deprecated code	❌ `experimental_rerun`	✅ `rerun`

New Files Added

During the revival, I added 6 new files to the repository:

history_manager.py — SQLite database operations (init_db, save_history, get_all_history, delete_history)
tests/test_history_manager.py — Unit tests with 3 passing tests
.env.example — Environment variable template
.gitignore — Python-appropriate ignores (.env, pycache, *.db)
Updated requirements.txt — Added pytest and python-dotenv
Updated app.py — Integrated history, .env support, and new UI buttons

Verdict Summary Extraction Logic

One of the more interesting additions was the extraction of structured data from the LLM's markdown output. The extract_verdict_summary() and extract_veracity_score() functions now cleanly parse the Groq Llama 3.3 response into database-friendly fields — something Copilot helped generate in a single prompt.

My Experience with GitHub Copilot

This was my first time using GitHub Copilot for a serious revival project, and it completely changed how I approach incomplete work. Here's exactly how Copilot helped me:

1. Copilot Built the Entire `history_manager.py` Module

My prompt:

"Create a Python module with SQLite database functions to store fact-check history. Columns: id, timestamp, claim, verdict_summary, veracity_score, full_report_text. Include functions: init_db(), save_history(), get_all_history(), delete_history()."

Copilot generated: All 6 database functions with proper error handling, SQL injection prevention (using parameterized queries), and connection management. I didn't write a single line of SQL manually.

2. Copilot Added .env Support with Fallbacks

My prompt:

"Add code to load .env file using python-dotenv, then fallback to st.secrets and os.getenv for API keys."

Copilot generated: The complete environment loading chain, handling local development and Streamlit Cloud deployment seamlessly.

3. Copilot Wrote All 3 Unit Tests

My prompt:

"Write pytest unit tests for history_manager.py – test init_db, save_history, get_all_history, delete_history. Use temporary file for database."

Copilot generated: Complete test file with fixtures and assertions. All 3 tests passed on first run.

4. Copilot Added the "New Chat" and "Delete" Buttons

My prompt:

"In the sidebar history section, next to each history item, add a small delete button. Also add a 'New Chat' button that clears the session and reruns."

Copilot generated: The full Streamlit UI code with proper column layouts and session state management.

5. Copilot Fixed the Deprecated `experimental_rerun`

I ran into the AttributeError: module 'streamlit' has no attribute 'experimental_rerun' error. Instead of searching Stack Overflow, I asked Copilot:

"What replaces experimental_rerun in newer Streamlit versions?"

Copilot answered: st.rerun() and replaced all occurrences automatically.

Key Copilot Patterns I Learned

Pattern	How I Used It
Comment-driven prompts	Writing detailed comments before implementation guides Copilot to generate exactly what's needed
Function signature first	Typing `def save_history(` and letting Copilot infer the schema
Iterative refinement	Accepting suggestions, then asking "Add error handling" or "Use parameterized queries" to improve them
Test generation	Asking Copilot to write tests before implementing the function (test-driven development with AI)

The biggest surprise: Copilot didn't just autocomplete — it understood the project structure (Streamlit, SQLite, pytest) and generated context-aware code that integrated seamlessly with my existing logic.

🛠️ Technical Stack

Component	Technology
Frontend	Streamlit (Python)
LLM	Groq API (Llama 3.3-70B-Versatile)
Search	Tavily Search API
Orchestration	LangChain
Database	SQLite
Environment	python-dotenv
Testing	pytest
Dev Assistant	GitHub Copilot

🔍 Judging Criteria Reflection

Criterion	How TruthGuard AI Addresses It
Use of underlying technology	Combines Groq's LPU (sub-second inference), Tavily's real-time search, and LangChain for structured prompting
Usability & UX	Glassmorphism UI, progress indicators, chat-based follow-ups, one-click report downloads, persistent history
Originality & Creativity	LLM-powered fact-checking with structured verdicts (Veracity Score + Final Verdict + Bias detection) is not a basic chatbot wrapper — it's a purpose-built misinformation tool
Completion Arc	Clear "before" vs "after" transformation: from prototype to database-backed, tested, environment-aware application

📦 How to Run Locally


bash
# Clone the repository
git clone https://github.com/Awaisranahmad/AI-Based-Content-Verification-Tool.git
cd AI-Based-Content-Verification-Tool

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create .env file with your API keys
cp .env.example .env
# Edit .env with your GROQ_API_KEY and TAVILY_API_KEY

# Run the application
streamlit run app.py

DEV Community: Awaisranahmad

Reviving TruthGuard AI: From Zero History to Full CRUD with GitHub Copilot

🛡️ What I Built

Demo

📸 Before vs After — The Comeback Story

Where It Was (The "Before")

What Changed (The "After")

New Files Added

Verdict Summary Extraction Logic

My Experience with GitHub Copilot

1. Copilot Built the Entire history_manager.py Module

2. Copilot Added .env Support with Fallbacks

3. Copilot Wrote All 3 Unit Tests

4. Copilot Added the "New Chat" and "Delete" Buttons

5. Copilot Fixed the Deprecated experimental_rerun

Key Copilot Patterns I Learned

🛠️ Technical Stack

🔍 Judging Criteria Reflection

📦 How to Run Locally

1. Copilot Built the Entire `history_manager.py` Module

5. Copilot Fixed the Deprecated `experimental_rerun`