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

The author revived "TruthGuard AI," a fake news detection tool, from a prototype into a production-ready application using GitHub Copilot. Copilot assisted in adding critical features like SQLite database persistence, environment configuration, unit tests, and a polished Streamlit user interface, generating most of the new infrastructure code. The project, built with Streamlit, LangChain, Tavily, and Groq's Llama 3.3-70B, was submitted for the GitHub Finish-Up-A-Thon Challenge.

This is a submission for the GitHub Finish-Up-A-Thon Challenge https://dev.to/challenges/github-2026-05-21 . 🛡️ 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 https://github.com/Awaisranahmad/AI-Based-Content-Verification-Tool 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