A Python-based email analysis and response generation system that uses NLP models to analyze emails, detect sentiment, generate summaries, and create appropriate responses.

• 📧 Email Analysis
• 😊 Sentiment Detection
• 📝 Smart Summarization
• 💬 Response Generation
• 🎨 Modern Web Interface
• ⚡ Real-time Processing

The project consists of two main components:

1. Core Engine (email_agent.py):

   • Email analysis and processing
   • Sentiment detection using DistilBERT
   • Summarization using BART
   • Response generation using GPT-2

2. Web Interface (app.py):

   • Streamlit-based UI
   • Real-time processing
   • Beautiful and responsive design

1. Clone the repository:

git clone https://github.com/rishabharaj/Email-AI-agent.git
cd Email-AI-agent

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

1. Run the Streamlit app:

streamlit run app.py

2. Open your browser and navigate to the provided URL (usually http://localhost:8501)

3. Paste your email text and click "Analyze Email"

Email-AI-agent/
├── app.py              # Streamlit web interface
├── email_agent.py      # Core email processing logic
├── requirements.txt    # Project dependencies
├── README.md          # Project documentation
└── .gitignore         # Git ignore file

Contributions are welcome! Please feel free to submit a Pull Request.

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

This project is licensed under the MIT License - see the LICENSE file for details.

• Hugging Face for the transformer models
• Streamlit for the web framework
• NLTK for natural language processing tools

• Email Analysis: Automatically analyzes email content
• Sentiment Detection: Identifies the emotional tone of the email
• Smart Summarization: Generates concise summaries of email content
• Response Generation: Creates appropriate responses when needed
• Modern Web Interface: Beautiful and user-friendly UI using Streamlit
• Real-time Processing: Instant analysis and results

1. EmailAgent Class (email_agent.py)

   • Uses three main NLP pipelines:
     • Sentiment Analysis: Using DistilBERT model
     • Summarization: Using BART model
     • Text Generation: Using GPT-2 model
   • Key Functions:
     • analyze_email(): Performs simultaneous summarization and sentiment analysis
     • needs_response(): Determines if a response is needed based on sentiment
     • generate_response(): Creates a response using the email summary
     • process_email(): Orchestrates the entire process

2. Web Interface (app.py)

   • Built with Streamlit
   • Features:
     • Clean, modern UI design
     • Real-time progress tracking
     • Color-coded sentiment analysis
     • Responsive layout
     • Error handling and user feedback

1. DistilBERT (for sentiment analysis)

   • Model: distilbert-base-uncased-finetuned-sst-2-english
   • Purpose: Analyzes email sentiment (positive/negative)
   • Output: Sentiment label and confidence score

2. BART (for summarization)

   • Model: facebook/bart-large-cnn
   • Purpose: Generates concise email summaries
   • Parameters:
     • max_length: 80 characters
     • min_length: 20 characters
     • num_beams: 4
     • length_penalty: 0.8

3. GPT-2 (for response generation)

   • Model: gpt2
   • Purpose: Generates appropriate email responses
   • Parameters:
     • max_length: 100 characters
     • temperature: 0.7
     • top_p: 0.9
     • repetition_penalty: 1.2

• torch: PyTorch for deep learning operations
• transformers: Hugging Face Transformers library for NLP models
• nltk: Natural Language Toolkit for text processing
• streamlit: Web application framework
• python-dotenv: Environment variable management

1. Create a virtual environment:

python -m venv venv

2. Activate the virtual environment:

• Windows:

.\venv\Scripts\activate

• Linux/Mac:

source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Run the application:

streamlit run app.py

The application will open in your default web browser at http://localhost:8501

1. Input Email

   • Paste your email text into the input area
   • Click "Analyze Email" button

2. Analysis Process

   • The system will:
     1. Generate a concise summary
     2. Analyze sentiment
     3. Determine if a response is needed
     4. Generate a response (if required)

3. Results Display

   • Summary of the email
   • Sentiment analysis with color coding
   • Response decision
   • Generated response (if applicable)

You can customize the following aspects:

1. Summary Length

   • Adjust max_length and min_length in analyze_email()
   • Modify length_penalty for different summary styles

2. Response Generation

   • Change prompt in generate_response()
   • Adjust generation parameters (temperature, top_p, etc.)

3. UI Styling

   • Modify CSS in app.py
   • Adjust layout and colors

1. Email Analysis

   • Text is processed through BART model for summarization
   • DistilBERT analyzes sentiment simultaneously
   • Results are combined for comprehensive analysis

2. Response Decision

   • Based on sentiment and confidence score
   • Triggers response generation when:
     • Sentiment is negative, or
     • Positive sentiment with high confidence (>0.9)

3. Response Generation

   • Uses email summary as context
   • Generates professional, concise responses
   • Ensures complete sentences and proper formatting

• First run might take time as it downloads ML models
• Requires stable internet connection for model downloads
• Processing time depends on email length and complexity

Feel free to contribute to this project by:

1. Forking the repository
2. Creating a feature branch
3. Submitting a pull request

This project is open-source and available under the MIT License.