Update README.md

README.md

@@ -1,35 +1,24 @@
-# Fake News Detector
-
-This project is a Fake News Detection system using DistilBERT and PyTorch, with a Streamlit web app for user interaction.
-
-## Features
-- Data preprocessing and visualization (Jupyter Notebook)
-- Model training using DistilBERT embeddings
-- Streamlit app for real-time news classification
-
-## Files
-- `Fakke_news_detector.ipynb`: Data analysis, preprocessing, model training
-- `app.py`: Streamlit web app for fake news detection
-- `News_classifier.pt`: Trained PyTorch model weights
-- `tokenizer_distilbert/`: Saved tokenizer files
-
-## Usage
-1. **Install dependencies**
-   ```powershell
-   pip install -r requirements.txt
-   ```
-2. **Run the Streamlit app**
-   ```powershell
-   streamlit run app.py
-   ```
-3. **Interact**
-   - Paste news text in the app to check if it is Fake or True.
-
-## Training
-- See the notebook for data loading, preprocessing, model training, and evaluation steps.
-
-## Model & Tokenizer
-- The model and tokenizer are saved after training and loaded in the app for inference.
-
-## Requirements
-See `requirements.txt` for all required Python packages.
+---
+title: Fake News Detector
+emoji: 💻
+colorFrom: indigo
+colorTo: blue
+sdk: streamlit
+sdk_version: 1.36.0
+app_file: app.py
+pinned: false
+---
+# Fake News Detector
+
+This project is a Fake News Detection system using DistilBERT and PyTorch, with a Streamlit web app for user interaction.
+
+## Features
+- Data preprocessing and visualization (Jupyter Notebook)
+- Model training using DistilBERT embeddings
+- Streamlit app for real-time news classification
+
+## Files
+- `Fakke_news_detector.ipynb`: Data analysis, preprocessing, model training
+- `app.py`: Streamlit web app for fake news detection
+- `News_classifier.pt`: Trained PyTorch model weights
+- `tokenizer_distilbert/`: Saved tokenizer files