Update README.md

README.md

@@ -9,8 +9,7 @@ license: mit
 short_description: 'FactChecker: Fake News Detector'
 ---
 
-# <img src="build/logo.png" alt="FactChecker Logo" width="30" height="30"> FactChecker: Fake News Detection Web Application
-
+# ![FactChecker Logo](build/logo.png) FactChecker: Fake News Detection Web Application
 
 FactChecker is a web application that detects fake news using various machine learning models. 
 The system analyzes text input and predicts whether the content is likely to be real or fake news, 
@@ -49,16 +48,17 @@ providing confidence scores and visualizations to help users understand the resu
 - Regular expressions for text cleaning
 
 ## Project Structure
+```
 FactChecker/
 ├── build/                    # React build files(compiled frontend)
 │   ├── static/
 │   │   ├── css/              # Compiled CSS
 │   │   └── js/               # Compiled JavaScript
-│   ├── asset-manifest.json   
+│   ├── asset-manifest.json
 │   ├── index.html            # Main HTML file
-│   ├── logo.ico              
-│   ├── logo.png              
-│   └── manifest.json         
+│   ├── logo.ico
+│   ├── logo.png
+│   └── manifest.json
 ├── model_training/           # Model training materials
 │   ├── visualizations/       # Generated visualization images
 │   └── model_training.ipynb  # Jupyter notebook for model training
@@ -67,33 +67,35 @@ FactChecker/
 │   ├── lr_model.pkl          # Logistic Regression model
 │   ├── rf_model.pkl          # Random Forest model
 │   └── distilbert_model.pt   # DistilBERT model
-├── .gitattributes            
+├── .gitattributes
 ├── Dockerfile                # Docker configuration
-├── README.md                 
+├── README.md
 ├── app.py                    # Flask application
 └── requirements.txt          # Python dependencies
+```
 
-### Steps
+## Steps
 
-#### For Backend:
+### For Backend:
 1. Clone the repository
-2. Create a virtual environment and install the dependencies.
-  1. pip install -r requirements.txt
-3. Download NLTK resources:
-  1. python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet')"
-4. Run the application
-  1. python app.py
+2. Create a virtual environment and install the dependencies.  
+     ```pip install -r requirements.txt```
+3. Download NLTK resources:  
+     ```python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet')"```
+4. Run the application  
+     ```python app.py```
 
 #### For Frontend:
-1. Install dependencies:
-  1. npm install 
-3. Build the frontend:
-  1. npm run build
+1. Install dependencies:  
+     ```npm install ```
+3. Build the frontend:  
+     ```npm run build```
 
 #### Model Training
 To retrain the models:
 1. Upload the notebook in Google Colab.
 2. Download the ISOT(true.csv, fake.csv) datasets and upload it to the google drive.
-3. Change the runtime type to ideally run GPU instance.
-4. Activate the runtime.
-5. Run the Cells.
+3. Set runtime type to GPU for optimal performance:  
+    ```Go to Runtime → Change runtime type → GPU → Save```  
+4. Activate the runtime.  
+5. Execute the notebook cells sequentially to retrain the models.