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metadata 
title: TruthCheck
emoji: 🌍
colorFrom: blue
colorTo: indigo
sdk: docker
app_file: app.py
pinned: false

🕵️‍♂️ TruthCheck – AI-Powered Fake News Detector

TruthCheck is an end-to-end Machine Learning web application designed to counter misinformation and verify the credibility of news claims. Users can input any news headline or statement, and the application cross-references it with real-time web search insights using NLP models to grade its authenticity.

🚀 Live Demo

You can try out the live web application here: 👉 TruthCheck Live Space

💻 GitHub Repository

Access the source code here: 👉 GitHub Repository

🛠️ Key Features

  • Real-Time Fact-Checking: Integrates with Google Search results to fetch the most recent news articles and official statements related to the user's query.
  • Semantic Similarity Scoring: Uses state-of-the-art NLP transformers to check how closely the web search results match or contradict the user's input claim.
  • Confidence Grading: Provides a calculated truth score/percentage to help users quickly assess if the news is Verified, Misleading, or Unverified.
  • Clean & Intuitive UI: Built with HTML/CSS and Flask templates for a smooth user experience.

🧰 Tech Stack & Architecture

  • Backend Framework: Python, Flask
  • AI/NLP Models: * Sentence-Transformers (CrossEncoder / Embeddings) for semantic similarity scoring.
    • Transformers (T5-Tokenizer & Models) for textual analysis.
  • External APIs: SerpAPI (Google Search Engine Engine)
  • Deployment & DevOps: Docker, Hugging Face Spaces Secrets, Git for Version Control

⚙️ How It Works (Under the Hood)

  1. User Input: The user submits a claim or news headline through the web interface.
  2. Web Scraping & API Search: The system uses SerpAPI to query Google Search and pulls the top 5 relevant news sources/articles.
  3. Semantic Processing: The application processes the text of these articles and uses a SentenceTransformer model to calculate the similarity between the user's claim and the trusted news sources.
  4. Final Verdict: Based on the similarity score threshold, the app displays whether the claim is backed by credible sources or if it lacks evidence.

🔒 Security Practices Followed

  • Environment Separation: Sensitive credentials like SERPAPI_KEY are strictly managed via a local .env file (hidden using .gitignore) and securely stored within Hugging Face Cloud Secrets. Hardcoded keys have been completely eliminated to maintain repository safety.