This is a Flask-based chatbot application that uses Google's Gemini API to provide algorithmic explanations and approaches for DSA problems. The application includes a history panel for accessing past conversations and uses MongoDB for data storage, along with NumPy and Pandas for data analysis.

• Text-based interface for inputting DSA problems
• Algorithmic approach explanations using Google Gemini API
• Focus on explaining time/space complexity and data structures
• Step-by-step approach explanation without full code solutions
• History panel for accessing past conversations (like ChatGPT/Gemini)
• MongoDB database for storing conversations and user sessions
• Data analysis using NumPy and Pandas

• Python 3.8+
• Flask
• Google Generative AI SDK
• MongoDB
• PyMongo
• NumPy
• Pandas
• HTML/CSS/JavaScript
• Bootstrap 5

1. Clone or download the project files
2. Create a virtual environment (optional but recommended):

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

3. Install the required dependencies:

   pip install -r requirements.txt

4. Get a Google Gemini API key from Google AI Studio
5. Rename .env.example to .env and fill in your configuration:

   GEMINI_API_KEY=your_gemini_api_key_here
   MONGO_URI=mongodb://localhost:27017/chatbot_db
   SECRET_KEY=your_secret_key_here
   

6. Make sure MongoDB is running on your system
7. Run the application:

   python app.py

8. Open your browser and go to http://localhost:5000

• Type your DSA problem or question in the input field at the bottom
• The chatbot will provide algorithmic explanations without code
• Access your conversation history in the left sidebar
• Click on any previous conversation to continue from where you left off
• Use the "New Chat" button to start a fresh conversation
• Use the "Clear All" button to delete all conversations

The application includes analytics features that use NumPy and Pandas to analyze:

• User engagement patterns
• Message length statistics
• Conversation frequency
• Activity time trends

Analytics can be accessed via the /api/analytics/usage endpoint.

chatbot/
├── app/
│   ├── __init__.py
│   ├── models/
│   │   ├── database.py
│   │   └── models.py
│   ├── routes/
│   │   ├── main.py
│   │   ├── history.py
│   │   ├── api.py
│   │   └── analytics.py
│   ├── static/
│   │   └── css/
│   │       └── style.css
│   └── templates/
│       ├── base.html
│       ├── index.html
│       └── history.html
├── app.py
├── requirements.txt
├── .env
└── README.md

• GET / - Main chat interface
• POST /api/chat - Send a message and get a response
• GET /api/conversations - Get all conversations for a user
• GET /api/conversation/<id> - Get a specific conversation with its messages
• POST /api/new_conversation - Start a new conversation
• GET /api/history/conversations - Get conversation history for the history panel
• DELETE /api/history/conversation/<id> - Delete a specific conversation
• DELETE /api/history/conversations - Delete all conversations
• GET /api/analytics/usage - Get usage analytics

The application uses three collections:

1. users - Stores user information
2. conversations - Stores conversation metadata (title, timestamps)
3. messages - Stores individual messages within conversations

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request