🤖 AI Personality Chatbot

An interactive chatbot web app built with Streamlit and Groq AI that features personality-based responses with strict domain boundaries.

✨ Features

• 🎭 Personality Selection: Choose from 5 distinct personalities:

  • Math Teacher: Only answers math-related questions
  • Doctor: Only answers health and medical queries
  • Travel Guide: Only provides travel advice and tips
  • Chef: Only answers cooking and recipe questions
  • Tech Support: Only answers technical troubleshooting

• 🧠 Model Selection: Choose from current Groq AI free tier models:

  • LLaMA 3.1 8B (Fast - default)
  • LLaMA 3.3 70B (Powerful)
  • Qwen 3 32B (Balanced)

• 💾 Session Memory: Maintains conversation context throughout the session

• 🛡️ Personality Enforcement: Chatbot strictly stays within personality boundaries and politely refuses out-of-scope questions

• ⚡ Real-time Streaming: Responses stream in real-time for better UX

🚀 Getting Started

Prerequisites

• Python 3.8+
• Groq API Key (get one at https://console.groq.com)

Installation

1. Clone or download this repository

cd 1

2. Create a virtual environment (optional but recommended)

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

3. Install dependencies

pip install -r requirements.txt

4. Set up environment variables

Create a .env file in the project root and add your Groq API key:

GROQ_API_KEY=your_groq_api_key_here

The .env file is already created with your API key.

Running Locally

streamlit run app.py

The app will open in your browser at http://localhost:8501

📁 Project Structure

1/
├── app.py                 # Main Streamlit application
├── .env                   # Environment variables (API keys)
├── .gitignore            # Git ignore rules
├── requirements.txt      # Python dependencies
├── README.md             # This file
└── src/
    ├── __init__.py       # Package initialization
    ├── personalities.py  # Personality definitions and system prompts
    └── utils.py          # Utility functions for boundary enforcement

🎯 How It Works

1. Personality System

Each personality has:

• System Prompt: Instructions for the AI on how to behave
• Keywords: Domain-specific keywords for boundary detection
• Refuse Message: Polite message when question is out of scope

2. Personality Enforcement

The system prompt is designed to make the AI naturally stay within its domain:

• The AI reads the system prompt with clear instructions on what it can help with
• If a question is out of scope, the AI politely declines and redirects
• No pre-check filtering - the AI handles personality boundaries intelligently

3. Session Management

• Conversation history is stored in Streamlit's session state
• Context is maintained throughout the user's session
• Users can clear history with the "Clear Chat History" button

🎨 Customization

Adding a New Personality

Edit src/personalities.py and add a new entry to the PERSONALITIES dictionary:

"New Personality": {
    "description": "Brief description",
    "system_prompt": """Your detailed system prompt here""",
    "keywords": ["keyword1", "keyword2", "keyword3"],
    "refuse_message": "Polite refusal message"
}

Changing System Prompts

Modify the system_prompt in src/personalities.py for any personality to customize behavior.

📊 Testing

Test the chatbot with questions for each personality:

• Math Teacher: "2+2", "What is calculus?", "Solve x² + 5x + 6 = 0"
• Doctor: "What are symptoms of flu?", "I have a headache", "Is diabetes treatable?"
• Travel Guide: "Where should I visit in Japan?", "Best hotels in Paris", "How to get around London?"
• Chef: "How do I make homemade pasta?", "Chocolate cake recipe", "Cooking tips for beginners"
• Tech Support: "My computer is slow", "How do I fix WiFi?", "How to install Python?"

Try asking out-of-scope questions to see how each personality handles them gracefully.

🚢 Deployment to Streamlit Cloud

1. Push to GitHub

git init
git add .
git commit -m "Initial commit: AI Personality Chatbot"
git remote add origin https://github.com/yourusername/chatbot-repo.git
git push -u origin main

2. Deploy on Streamlit Cloud

• Go to https://streamlit.io/cloud
• Click "New app"
• Select your GitHub repository
• Choose the branch and file path (app.py)
• Deploy

3. Set Environment Variables

In Streamlit Cloud dashboard:

• Go to your app settings
• Add secret: GROQ_API_KEY = your_groq_api_key

📝 Environment Variables

• GROQ_API_KEY: Your Groq API key (required)

🔒 Security Notes

• Never commit .env file to version control (already in .gitignore)
• Use Streamlit Cloud secrets for sensitive information in production
• API key is loaded from environment variables for security

🐛 Troubleshooting

"GROQ_API_KEY not found"

• Ensure .env file exists in the project root
• Verify your API key is correctly set

Model not responding

• Check your internet connection
• Verify your Groq API key is valid
• Try switching to a different model
• Check rate limits on https://console.groq.com

Error: "model has been decommissioned"

• The model has been deprecated by Groq
• Select a different model from the dropdown
• Available models are: LLaMA 3.1 8B, LLaMA 3.3 70B, Qwen 3 32B

🤝 Contributing

Feel free to customize personalities, add new ones, or improve the boundary enforcement logic.

📄 License

This project is open source and available for educational use.

🔗 Resources

• Streamlit Documentation
• Groq API Documentation
• Groq Python SDK
• Available Groq Models

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Built with ❤️ using Streamlit and Groq AI