🚀 Fake News Detection using ML + Generative AI

🧠 Detect fake vs. real news headlines and explain the reasoning using OpenAI GPT-4o-mini.

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🏗️ Project Overview

This upgraded version of the classic Fake News Detection project adds an LLM-powered explainability layer.
Traditional ML models classify news articles, while GPT-4o-mini provides a human-readable justification of why an article is likely fake or real.

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⚙️ Tech Stack

Layer	Technology	Purpose
🧩 Machine Learning	Scikit-Learn	Core fake/real classification
🧠 Generative AI	OpenAI GPT-4o-mini	Natural-language explanations
💻 Frontend	HTML + CSS	Simple, responsive web interface
🌐 Backend	Flask	Web app for serving predictions
💾 Storage	joblib, pandas	Model + dataset handling

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🧩 Features

• 📰 Classifies news as FAKE or REAL using Passive Aggressive Classifier.
• 🔍 Generates explanations via GPT-4o-mini for every prediction.
• 🌐 Flask-based web app with an easy-to-use text input box.
• 📈 Confusion matrix & accuracy summary available on /metrics.
• 🧱 Modular folder structure for quick extension or retraining.

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📁 Folder Structure

Fake_News_Detection/
│
├── app.py                     # Flask entry point
├── requirements.txt            # Dependencies
├── model/
│   ├── fake_news_model.pkl     # Trained PAC model
│   └── tfidf_vectorizer.pkl    # TF-IDF vectorizer
│
├── data/
│   └── news.csv                # Dataset (Kaggle-style)
│
├── utils/
│   ├── gpt_explainer.py        # GPT-4o-mini text explanations
│   ├── model_loader.py         # Load + predict helpers
│   └── preprocess.py           # Text preprocessing utils
│
├── templates/
│   └── index.html              # Web interface
│
├── static/
│   └── style.css               # Styling
│
└── README.md

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🧰 Setup Instructions

# 1️⃣ Clone the repository
git clone https://github.com/ramarav/Fake_News_Detection.git
cd Fake_News_Detection

# 2️⃣ Create a virtual environment
python -m venv venv
source venv/bin/activate  # on Windows use venv\Scripts\activate

# 3️⃣ Install dependencies
pip install -r requirements.txt

# 4️⃣ Add your OpenAI API key (for explanations)
set OPENAI_API_KEY=your_api_key_here  # Windows
export OPENAI_API_KEY=your_api_key_here  # macOS/Linux

# 5️⃣ Run Flask app
python app.py

Then open http://localhost:5000 🎯

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🧪 Sample Output

Input	Prediction	Explanation
“NASA confirms aliens discovered near Mars base.”	FAKE	“This resembles tabloid-style unverifiable claims.”
“UN reports global hunger dropped by 10% in 2024.”	REAL	“The phrasing and reference to official data suggest credibility.”

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🧮 Model Performance

Metric	Value
Accuracy	93.13%
Classifier	PassiveAggressiveClassifier
Vectorizer	TF-IDF (max_df=0.7, stop_words='english')

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🏷️ Badges

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📦 API Endpoint Example

POST /predict

curl -X POST http://127.0.0.1:5000/predict      -H "Content-Type: application/json"      -d '{"text": "Breaking: New vaccine approved by WHO"}'

Response:

{
  "prediction": "REAL",
  "explanation": "WHO approvals are verified through credible institutional sources."
}

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💡 Future Enhancements

• Integrate news source credibility scoring
• Add multilingual detection
• Deploy using Docker + Render
• Support voice-based input (Speech-to-Text)

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👨‍💻 Author

Mekala Ramarao
AMD India
Focus: AI/ML applications in NLP, GPU analytics, and intelligent automation.
📧 LinkedIn