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

This is a submission for the

Before (Abandoned State):

📸 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:

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.

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

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

🔍 Judging Criteria Reflection

📦 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