Add application file

README.md

@@ -1,276 +1,55 @@
-# Profanity Detection in Speech and Text
+---
+title: Profanity Detection & Replacement System
+emoji: 🚫
+colorFrom: red
+colorTo: blue
+sdk: gradio
+sdk_version: 4.14.0
+app_file: profanity_detector.py
+pinned: false
+---
 
-A robust multimodal system for detecting and rephrasing profanity in both speech and text, leveraging advanced NLP models to ensure accurate filtering while preserving conversational context.
+# Profanity Detection & Replacement System
 
-![Profanity Detection System](https://img.shields.io/badge/AI-NLP%20System-blue)
-![Python](https://img.shields.io/badge/Python-3.12%2B-green)
-![Transformers](https://img.shields.io/badge/HuggingFace-Transformers-yellow)
+This app provides a comprehensive solution for detecting and cleaning profanity from both text and audio content. It uses state-of-the-art machine learning models to analyze content, identify inappropriate language, and generate clean alternatives.
 
-## 📋 Features
+## Features
 
-- **Multimodal Analysis**: Process both written text and spoken audio
-- **Context-Aware Detection**: Goes beyond simple keyword matching
-- **Automatic Content Refinement**: Intelligently rephrases content while preserving meaning
-- **Audio Synthesis**: Converts rephrased content into high-quality spoken audio
-- **Classification System**: Categorises content by toxicity levels
-- **User-Friendly Interface**: Intuitive Gradio-based UI
-- **Real-time Streaming**: Process audio in real-time as you speak
-- **Adjustable Sensitivity**: Fine-tune profanity detection threshold
-- **Visual Highlighting**: Instantly identify problematic words with visual highlighting
-- **Toxicity Classification**: Automatically categorize content from "No Toxicity" to "Severe Toxicity"
-- **Performance Optimization**: Half-precision support for improved GPU memory efficiency
+- 🔍 Real-time profanity detection with adjustable sensitivity
+- 🔄 Automatic text rephrasing to clean alternatives
+- 🎤 Speech-to-text conversion with profanity filtering
+- 🗣️ Text-to-speech generation for clean content
+- 💻 User-friendly Gradio interface
+- 🔄 Real-time streaming support for live audio processing
 
-## 🧠 Models Used
+## Models Used
 
-The system leverages four powerful models:
+- Profanity Detection: `parsawar/profanity_model_3.1`
+- Text Detoxification: `s-nlp/t5-paranmt-detox`
+- Speech Recognition: OpenAI Whisper (large)
+- Text-to-Speech: Microsoft SpeechT5
 
-1. **Profanity Detection**: `parsawar/profanity_model_3.1` - A RoBERTa-based model trained for offensive language detection
-2. **Content Refinement**: `s-nlp/t5-paranmt-detox` - A T5-based model for rephrasing offensive language
-3. **Speech-to-Text**: OpenAI's `Whisper` (large) - For transcribing spoken audio
-4. **Text-to-Speech**: Microsoft's `SpeechT5` - For converting rephrased text back to audio
+## Requirements
 
-## 🔧 Installation
+- Python 3.10
+- PyTorch with CUDA support
+- Gradio
+- Transformers
+- OpenAI Whisper
+- Other dependencies listed in `requirements.txt`
 
-### Prerequisites
+## Interface
 
-- Python 3.10+
-- CUDA-compatible GPU recommended (but CPU mode works too)
-- FFmpeg for audio processing
+The app provides three main interaction modes:
 
-### Option 1: Using Conda (Recommended for Local Development)
+1. **Text Analysis**: Enter text to detect and clean profanity
+2. **Audio Analysis**: Upload or record audio for profanity detection
+3. **Real-time Streaming**: Process live audio with instant profanity filtering
 
-```bash
-# Clone the repository
-git clone https://github.com/yourusername/profanity-detection.git
-cd profanity-detection
+## Technical Details
 
-# Method A: Create environment from environment.yml (recommended)
-conda env create -f environment.yml
-conda activate llm_project
-
-# Method B: Create a new conda environment manually
-conda create -n profanity-detection python=3.10
-conda activate profanity-detection
-
-# Install PyTorch with CUDA support (adjust CUDA version if needed)
-conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
-
-# Install FFmpeg for audio processing
-conda install -c conda-forge ffmpeg
-
-# Install Pillow properly to avoid DLL errors
-conda install -c conda-forge pillow
-
-# Install additional dependencies
-pip install -r requirements.txt
-
-# Set environment variable to avoid OpenMP conflicts (recommended)
-conda env config vars set KMP_DUPLICATE_LIB_OK=TRUE
-conda activate profanity-detection  # Re-activate to apply the variable
-```
-
-### Option 2: Using Docker
-
-```bash
-# Clone the repository
-git clone https://github.com/yourusername/profanity-detection.git
-cd profanity-detection
-
-# Build and run the Docker container
-docker-compose build --no-cache
-
-docker-compose up
-```
-
-## 🚀 Usage
-
-### Running the Application
-
-```bash
-# Set environment variable to avoid OpenMP conflicts (if not set in conda config)
-# For Windows:
-set KMP_DUPLICATE_LIB_OK=TRUE
-
-# For Linux/Mac:
-export KMP_DUPLICATE_LIB_OK=TRUE
-
-# Run the application
-python profanity_detector.py
-```
-
-The Gradio interface will be accessible at http://127.0.0.1:7860 in your browser.
-
-### Using the Interface
-
-1. **Initialise Models**
-   - Click the "Initialize Models" button when you first open the interface
-   - Wait for all models to load (this may take a few minutes on first run)
-
-2. **Text Analysis Tab**
-   - Enter text into the text box
-   - Adjust the "Profanity Detection Sensitivity" slider if needed
-   - Click "Analyze Text"
-   - View results including profanity score, toxicity classification, and rephrased content
-   - See highlighted profane words in the text
-   - Listen to the audio version of the rephrased content
-
-3. **Audio Analysis Tab**
-   - Upload an audio file or record directly using your microphone
-   - Click "Analyze Audio"
-   - View transcription, profanity analysis, and rephrased content
-   - Listen to the cleaned audio version of the rephrased content
-
-4. **Real-time Streaming Tab**
-   - Click "Start Real-time Processing"
-   - Speak into your microphone
-   - Watch as your speech is transcribed, analyzed, and rephrased in real-time
-   - Listen to the clean audio output
-   - Click "Stop Real-time Processing" when finished
-
-## 🔧 Deployment Options
-
-### Local Deployment with Conda
-
-For the best development experience with fine-grained control:
-
-```bash
-# Create and configure environment
-conda env create -f environment.yml
-conda activate llm_project
-
-# Run with sharing enabled (accessible from other devices)
-python profanity_detector.py
-```
-
-### Docker Deployment (Production)
-
-For containerised deployment with predictable environment:
-
-#### Basic CPU Deployment
-```bash
-docker-compose up --build
-```
-
-#### GPU-Accelerated Deployment
-```bash
-# Automatic detection (recommended)
-docker-compose up --build
-
-# Or explicitly request GPU mode
-docker-compose up --build profanity-detector-gpu
-```
-
-No need to edit any configuration files - the system will automatically detect and use your GPU if available.
-
-#### Custom Port Configuration
-To change the default port (7860):
-1. Edit docker-compose.yml and change the port mapping (e.g., "8080:7860")
-2. Run `docker-compose up --build`
-
-## ⚠️ Troubleshooting
-
-### OpenMP Runtime Conflict
-
-If you encounter this error:
-```
-OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
-```
-
-**Solutions:**
-
-1. **Temporary fix**: Set environment variable before running:
-   ```bash
-   set KMP_DUPLICATE_LIB_OK=TRUE  # Windows
-   export KMP_DUPLICATE_LIB_OK=TRUE  # Linux/Mac
-   ```
-
-2. **Code-based fix**: Add to the beginning of your script:
-   ```python
-   import os
-   os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
-   ```
-
-3. **Permanent fix for Conda environment**:
-   ```bash
-   conda env config vars set KMP_DUPLICATE_LIB_OK=TRUE -n profanity-detection
-   conda deactivate
-   conda activate profanity-detection
-   ```
-
-### GPU Memory Issues
-
-If you encounter CUDA out of memory errors:
-
-1. Use smaller models:
-   ```python
-   # Change Whisper from "large" to "medium" or "small"
-   whisper_model = whisper.load_model("medium").to(device)
-   
-   # Keep the TTS model on CPU to save GPU memory
-   tts_model = SpeechT5ForTextToSpeech.from_pretrained(TTS_MODEL)  # CPU mode
-   ```
-
-2. Run some models on CPU instead of GPU:
-   ```python
-   # Remove .to(device) to keep model on CPU
-   t5_model = AutoModelForSeq2SeqLM.from_pretrained(T5_MODEL)  # CPU mode
-   ```
-
-3. Use Docker with specific GPU memory limits:
-   ```yaml
-   # In docker-compose.yml
-   deploy:
-     resources:
-       reservations:
-         devices:
-           - driver: nvidia
-             count: 1
-             capabilities: [gpu]
-             options:
-               memory: 4G  # Limit to 4GB of GPU memory
-   ```
-
-### Docker-Specific Issues
-
-1. **Permission issues with mounted volumes**:
-   ```bash
-   # Fix permissions (Linux/Mac)
-   sudo chown -R $USER:$USER .
-   ```
-
-2. **No GPU access in container**:
-   - Verify NVIDIA Container Toolkit installation
-   - Check GPU driver compatibility
-   - Run `nvidia-smi` on the host to confirm GPU availability
-
-### First-Time Slowness
-
-When first run, the application downloads all models, which may take time. Subsequent runs will be faster as models are cached locally. The text-to-speech model requires additional download time on first use.
-
-## 📄 Project Structure
-
-```
-profanity-detection/
-├── profanity_detector.py    # Main application file
-├── Dockerfile               # For containerised deployment
-├── docker-compose.yml       # Container orchestration
-├── requirements.txt         # Python dependencies
-├── environment.yml          # Conda environment specification
-└── README.md                # This file
-```
-
-## 📚 References
-
-- [HuggingFace Transformers](https://huggingface.co/docs/transformers/index)
-- [OpenAI Whisper](https://github.com/openai/whisper)
-- [Microsoft SpeechT5](https://huggingface.co/microsoft/speecht5_tts)
-- [Gradio Documentation](https://gradio.app/docs/)
-
-## 📝 License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-## 🙏 Acknowledgments
-
-- This project utilises models from HuggingFace Hub, Microsoft, and OpenAI
-- Inspired by research in content moderation and responsible AI
+- GPU acceleration supported for faster processing
+- Memory-optimized with FP16 precision where available
+- Configurable profanity detection threshold
+- Built-in error handling and logging
+- Dark mode support

README_.md

@@ -0,0 +1,276 @@
+# Profanity Detection in Speech and Text
+
+A robust multimodal system for detecting and rephrasing profanity in both speech and text, leveraging advanced NLP models to ensure accurate filtering while preserving conversational context.
+
+![Profanity Detection System](https://img.shields.io/badge/AI-NLP%20System-blue)
+![Python](https://img.shields.io/badge/Python-3.12%2B-green)
+![Transformers](https://img.shields.io/badge/HuggingFace-Transformers-yellow)
+
+## 📋 Features
+
+- **Multimodal Analysis**: Process both written text and spoken audio
+- **Context-Aware Detection**: Goes beyond simple keyword matching
+- **Automatic Content Refinement**: Intelligently rephrases content while preserving meaning
+- **Audio Synthesis**: Converts rephrased content into high-quality spoken audio
+- **Classification System**: Categorises content by toxicity levels
+- **User-Friendly Interface**: Intuitive Gradio-based UI
+- **Real-time Streaming**: Process audio in real-time as you speak
+- **Adjustable Sensitivity**: Fine-tune profanity detection threshold
+- **Visual Highlighting**: Instantly identify problematic words with visual highlighting
+- **Toxicity Classification**: Automatically categorize content from "No Toxicity" to "Severe Toxicity"
+- **Performance Optimization**: Half-precision support for improved GPU memory efficiency
+
+## 🧠 Models Used
+
+The system leverages four powerful models:
+
+1. **Profanity Detection**: `parsawar/profanity_model_3.1` - A RoBERTa-based model trained for offensive language detection
+2. **Content Refinement**: `s-nlp/t5-paranmt-detox` - A T5-based model for rephrasing offensive language
+3. **Speech-to-Text**: OpenAI's `Whisper` (large) - For transcribing spoken audio
+4. **Text-to-Speech**: Microsoft's `SpeechT5` - For converting rephrased text back to audio
+
+## 🔧 Installation
+
+### Prerequisites
+
+- Python 3.10+
+- CUDA-compatible GPU recommended (but CPU mode works too)
+- FFmpeg for audio processing
+
+### Option 1: Using Conda (Recommended for Local Development)
+
+```bash
+# Clone the repository
+git clone https://github.com/yourusername/profanity-detection.git
+cd profanity-detection
+
+# Method A: Create environment from environment.yml (recommended)
+conda env create -f environment.yml
+conda activate llm_project
+
+# Method B: Create a new conda environment manually
+conda create -n profanity-detection python=3.10
+conda activate profanity-detection
+
+# Install PyTorch with CUDA support (adjust CUDA version if needed)
+conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
+
+# Install FFmpeg for audio processing
+conda install -c conda-forge ffmpeg
+
+# Install Pillow properly to avoid DLL errors
+conda install -c conda-forge pillow
+
+# Install additional dependencies
+pip install -r requirements.txt
+
+# Set environment variable to avoid OpenMP conflicts (recommended)
+conda env config vars set KMP_DUPLICATE_LIB_OK=TRUE
+conda activate profanity-detection  # Re-activate to apply the variable
+```
+
+### Option 2: Using Docker
+
+```bash
+# Clone the repository
+git clone https://github.com/yourusername/profanity-detection.git
+cd profanity-detection
+
+# Build and run the Docker container
+docker-compose build --no-cache
+
+docker-compose up
+```
+
+## 🚀 Usage
+
+### Running the Application
+
+```bash
+# Set environment variable to avoid OpenMP conflicts (if not set in conda config)
+# For Windows:
+set KMP_DUPLICATE_LIB_OK=TRUE
+
+# For Linux/Mac:
+export KMP_DUPLICATE_LIB_OK=TRUE
+
+# Run the application
+python profanity_detector.py
+```
+
+The Gradio interface will be accessible at http://127.0.0.1:7860 in your browser.
+
+### Using the Interface
+
+1. **Initialise Models**
+   - Click the "Initialize Models" button when you first open the interface
+   - Wait for all models to load (this may take a few minutes on first run)
+
+2. **Text Analysis Tab**
+   - Enter text into the text box
+   - Adjust the "Profanity Detection Sensitivity" slider if needed
+   - Click "Analyze Text"
+   - View results including profanity score, toxicity classification, and rephrased content
+   - See highlighted profane words in the text
+   - Listen to the audio version of the rephrased content
+
+3. **Audio Analysis Tab**
+   - Upload an audio file or record directly using your microphone
+   - Click "Analyze Audio"
+   - View transcription, profanity analysis, and rephrased content
+   - Listen to the cleaned audio version of the rephrased content
+
+4. **Real-time Streaming Tab**
+   - Click "Start Real-time Processing"
+   - Speak into your microphone
+   - Watch as your speech is transcribed, analyzed, and rephrased in real-time
+   - Listen to the clean audio output
+   - Click "Stop Real-time Processing" when finished
+
+## 🔧 Deployment Options
+
+### Local Deployment with Conda
+
+For the best development experience with fine-grained control:
+
+```bash
+# Create and configure environment
+conda env create -f environment.yml
+conda activate llm_project
+
+# Run with sharing enabled (accessible from other devices)
+python profanity_detector.py
+```
+
+### Docker Deployment (Production)
+
+For containerised deployment with predictable environment:
+
+#### Basic CPU Deployment
+```bash
+docker-compose up --build
+```
+
+#### GPU-Accelerated Deployment
+```bash
+# Automatic detection (recommended)
+docker-compose up --build
+
+# Or explicitly request GPU mode
+docker-compose up --build profanity-detector-gpu
+```
+
+No need to edit any configuration files - the system will automatically detect and use your GPU if available.
+
+#### Custom Port Configuration
+To change the default port (7860):
+1. Edit docker-compose.yml and change the port mapping (e.g., "8080:7860")
+2. Run `docker-compose up --build`
+
+## ⚠️ Troubleshooting
+
+### OpenMP Runtime Conflict
+
+If you encounter this error:
+```
+OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
+```
+
+**Solutions:**
+
+1. **Temporary fix**: Set environment variable before running:
+   ```bash
+   set KMP_DUPLICATE_LIB_OK=TRUE  # Windows
+   export KMP_DUPLICATE_LIB_OK=TRUE  # Linux/Mac
+   ```
+
+2. **Code-based fix**: Add to the beginning of your script:
+   ```python
+   import os
+   os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
+   ```
+
+3. **Permanent fix for Conda environment**:
+   ```bash
+   conda env config vars set KMP_DUPLICATE_LIB_OK=TRUE -n profanity-detection
+   conda deactivate
+   conda activate profanity-detection
+   ```
+
+### GPU Memory Issues
+
+If you encounter CUDA out of memory errors:
+
+1. Use smaller models:
+   ```python
+   # Change Whisper from "large" to "medium" or "small"
+   whisper_model = whisper.load_model("medium").to(device)
+   
+   # Keep the TTS model on CPU to save GPU memory
+   tts_model = SpeechT5ForTextToSpeech.from_pretrained(TTS_MODEL)  # CPU mode
+   ```
+
+2. Run some models on CPU instead of GPU:
+   ```python
+   # Remove .to(device) to keep model on CPU
+   t5_model = AutoModelForSeq2SeqLM.from_pretrained(T5_MODEL)  # CPU mode
+   ```
+
+3. Use Docker with specific GPU memory limits:
+   ```yaml
+   # In docker-compose.yml
+   deploy:
+     resources:
+       reservations:
+         devices:
+           - driver: nvidia
+             count: 1
+             capabilities: [gpu]
+             options:
+               memory: 4G  # Limit to 4GB of GPU memory
+   ```
+
+### Docker-Specific Issues
+
+1. **Permission issues with mounted volumes**:
+   ```bash
+   # Fix permissions (Linux/Mac)
+   sudo chown -R $USER:$USER .
+   ```
+
+2. **No GPU access in container**:
+   - Verify NVIDIA Container Toolkit installation
+   - Check GPU driver compatibility
+   - Run `nvidia-smi` on the host to confirm GPU availability
+
+### First-Time Slowness
+
+When first run, the application downloads all models, which may take time. Subsequent runs will be faster as models are cached locally. The text-to-speech model requires additional download time on first use.
+
+## 📄 Project Structure
+
+```
+profanity-detection/
+├── profanity_detector.py    # Main application file
+├── Dockerfile               # For containerised deployment
+├── docker-compose.yml       # Container orchestration
+├── requirements.txt         # Python dependencies
+├── environment.yml          # Conda environment specification
+└── README.md                # This file
+```
+
+## 📚 References
+
+- [HuggingFace Transformers](https://huggingface.co/docs/transformers/index)
+- [OpenAI Whisper](https://github.com/openai/whisper)
+- [Microsoft SpeechT5](https://huggingface.co/microsoft/speecht5_tts)
+- [Gradio Documentation](https://gradio.app/docs/)
+
+## 📝 License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## 🙏 Acknowledgments
+
+- This project utilises models from HuggingFace Hub, Microsoft, and OpenAI
+- Inspired by research in content moderation and responsible AI