@syed_hassan296

Our project is a Chrome Extension powered by AI that allows users to check whether a news article is real or fake in just one click. When the user visits a website, the extension extracts the main article text, sends it to a Python-based AI backend, which uses a pre-trained BERT model to detect whether the content is fake or not. Target Audience: Everyday internet users Journalists Students Researchers Fact-checkers Unique Features: One-click fake news detection Uses NLP and machine learning (AI) Lightweight Chrome Extension Hosted API with pre-trained HuggingFace model Quick, user-friendly results 🛠 Technologies Used: Area Technology Frontend HTML, CSS, JavaScript (Vanilla) Chrome Extension Chrome APIs (manifest.json) Backend (AI) Python, Flask, Transformers AI Model HuggingFace (bert-tiny-fake-news) Hosting Replit / Render (for Flask API) Code Repo GitHub How It Works (Workflow): User opens a news article in Chrome. Clicks the extension icon. Extension extracts main text from the page using content.js. Text is sent to Flask API (/predict endpoint). Flask API loads BERT model and predicts if the news is fake or real. Result is returned and displayed in the popup as: ✅ Real News or ❌ Fake News (plus a confidence score) 🧠 AI Model Details Model Name: mrm8488/bert-tiny-finetuned-fake-news Type: BERT NLP model How to Use: HuggingFace Transformers library Input: News article (string) Output: Label = Fake or Real + Confidence Score Set up Python + Flask environment Install dependencies: transformers, torch, Flask Load fake news model Create /predict API endpoint Test API with sample input (using Postman or curl) Host API on Replit or Render Create Chrome Extension files: popup.html, popup.js, manifest.json Extract current page text using content.js Send extracted text to your Flask API Display result in the extension popup