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README.md

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-# Toxic Comment Classification using BERT
-
-A sophisticated machine learning project that uses BERT (Bidirectional Encoder Representations from Transformers) to classify toxic comments. This project provides both a web interface and CLI tools for detecting various types of toxic comments.
-
-## 🌟 Features
-
-- Real-time toxic comment classification
-- Interactive web interface using Streamlit
-- Command-line interface for batch processing
-- Support for multiple toxicity categories
-- Visualization of toxicity scores using Plotly
-- GPU acceleration support (when available)
-
-## 🛠️ Prerequisites
-
-- Python 3.7+
-- CUDA-compatible GPU (optional, for faster processing)
-- Git
-
-## 📦 Installation
-
-1. Clone the repository:
-   ```bash
-   git clone https://github.com/yourusername/commentclassification_using_bert_model.git
-   cd commentclassification_using_bert_model
-   ```
-
-2. Create and activate a virtual environment:
-   ```bash
-   python -m venv venv
-   source venv/bin/activate  # On Windows, use: venv\Scripts\activate
-   ```
-
-3. Install required packages:
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-## 🚀 Usage
-
-### Web Interface
-
-1. Start the Streamlit application:
-   ```bash
-   streamlit run app.py
-   ```
-2. Open your browser and navigate to the displayed URL (typically http://localhost:8501)
-3. Enter text in the input field to get toxicity predictions
-4. View the visualization of toxicity scores through an interactive chart
-
-### Docker Container
-
-1. Build the Docker image:
-   ```bash
-   docker build -t toxic-comment-classifier .
-   ```
-2. Run the Docker container:
-   ```bash
-   docker run -p 7860:7860 toxic-comment-classifier
-   ```
-3. Open your browser and navigate to http://localhost:7860
-
-### Hugging Face Spaces Deployment
-
-This project can be deployed to Hugging Face Spaces using Docker:
-
-1. Create a new Space on Hugging Face with Docker SDK
-2. Push this repository to the Space
-3. Hugging Face will automatically build and deploy the Docker container
-
-For detailed deployment instructions, see [DEPLOY_TO_HUGGINGFACE.md](DEPLOY_TO_HUGGINGFACE.md)
-
-### Command Line Interface
-
-For interactive testing:
-```bash
-python CLI_interactive_test.py
-```
-
-For model training:
-```bash
-python train.py
-```
-
-For running tests:
-```bash
-python test_model.py
-```
-
-## 🏗️ Project Structure
-
-```
-├── app.py                  # Streamlit web application
-├── CLI_interactive_test.py # Command line interface
-├── train.py               # Model training script
-├── test_model.py          # Model testing utilities
-├── cuda.py               # CUDA availability check
-├── requirements.txt       # Project dependencies
-├── setup.py              # Package setup configuration
-├── Dockerfile            # Docker configuration for containerization
-├── .dockerignore         # Files to exclude from Docker image
-├── .space                # Hugging Face Spaces configuration
-├── DEPLOY_TO_HUGGINGFACE.md # Deployment instructions for Hugging Face
-├── deploy_to_huggingface.sh # Script to help with Hugging Face deployment
-├── src/                  # Source code directory
-├── models/               # Saved model checkpoints
-└── data/                 # Training and test datasets
-```
-
-## 🔧 Model Architecture
-
-The project uses a fine-tuned BERT model (bert-base-uncased) with additional classification layers to detect different types of toxicity in text. The model is implemented using PyTorch and the Transformers library.
-
-Key components:
-- BERT base model for text encoding
-- Custom classification head for toxicity detection
-- Multi-label classification support
-- Real-time inference capabilities
-
-## 📊 Performance
-
-The model is trained to classify text into multiple toxicity categories with high accuracy. It can process text in real-time and provides confidence scores for each category of toxicity:
-- Toxic
-- Severe Toxic
-- Obscene
-- Threat
-- Insult
-- Identity Hate
-
-## 💻 Dependencies
-
-Key dependencies include:
-- transformers >= 4.35.0
-- torch >= 1.9.0
-- streamlit >= 1.24.0
-- fastapi >= 0.68.0
-- plotly >= 5.13.0
-- pandas >= 1.3.0
-- numpy >= 1.19.0
-
-## 🤝 Contributing
-
-Contributions are welcome! Please feel free to submit a Pull Request. Here's how you can contribute:
-1. Fork the repository
-2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
-3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
-4. Push to the branch (`git push origin feature/AmazingFeature`)
-5. Open a Pull Request
-
-## 📝 License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-## 🙏 Acknowledgments
-
-- Hugging Face for the Transformers library
-- The BERT team at Google Research
-- The Streamlit team for the excellent web framework
-- The PyTorch team for the deep learning framework
+---
+# ======= Configuration Block (YAML Front Matter) =======
+# This section configures your Hugging Face Space.
+# Values are based on the documentation you provided.
+
+# --- Basic Info ---
+# (Required) Title shown on the Space page and card
+title: My Awesome App 
+# (Required) Emoji shown on the Space card (find emojis at https://getemoji.com/)
+emoji: 🚀 
+# (Optional) Color gradient for the Space card
+colorFrom: blue 
+colorTo: green 
+# (Required) The type of application: gradio, streamlit, docker, or static
+sdk: gradio # IMPORTANT: Change this if you are using Streamlit, Docker, or just HTML files!
+# (Optional) Specify the Python version (default is 3.10)
+python_version: 3.10 
+# (Optional) Specify the SDK version (e.g., Gradio version). If omitted, HF uses a default.
+# sdk_version: 4.1.0 # Uncomment and set if you need a specific Gradio/Streamlit version
+# (Optional) Specify the main application file (default is app.py for Gradio/Streamlit)
+# app_file: my_application_script.py # Uncomment and change if your main file isn't called app.py
+
+# --- Optional Info ---
+# (Optional) A short description for the Space card
+short_description: A cool demo of [Your App's Technology/Purpose]. 
+# (Optional) List of tags to help others find your Space
+# tags: [text-generation, machine-learning, demo] # Uncomment and add relevant tags
+# (Optional) Keep this Space pinned at the top of your profile
+# pinned: false 
+
+# ======= End of Configuration Block =======
+---
+
+# ======= Description Content (Markdown) =======
+# This part is displayed on your Space's page.
+# Write in Markdown format (https://www.markdownguide.org/basic-syntax/).
+
+# My Awesome App 🚀
+
+**➡️ Short Description:** [**Replace this with a one-sentence description of what your application does.**] 
+*Example: This Space uses Gradio to demonstrate a simple image classification model.*
+
+## 🤔 What does it do?
+
+[**Replace this with a more detailed explanation of your application. What problem does it solve? What features does it have?**]
+*Example:*
+*   *Upload an image.*
+*   *The app predicts what object is in the image.*
+*   *It shows the top 3 predictions and their confidence scores.*
+
+## 🚀 How to use it?
+
+[**Replace this with simple instructions for users.**]
+*Example:*
+*1. Click on the 'Upload Image' box or drag and drop an image file.*
+*2. Wait for the prediction to appear below.*
+*3. That's it!*
+
+## 🛠️ Dependencies
+
+This application requires the libraries listed in the `requirements.txt` file. Hugging Face Spaces automatically installs these when the Space builds.
+
+## 📄 Files
+
+*   `app.py`: The main application code (using Gradio/Streamlit). [**Update if your filename is different!**]
+*   `requirements.txt`: Lists the Python libraries needed.
+*   `README.md`: This file (configuration and description).
+*   [**Add any other important files here, like model files, helper scripts, etc.**]
+
+---
+*Space created by [Your Name/Username]*