Anveshak Neo 🤖

Anveshak Neo is an AI-powered chatbot that detects emotions from text input. It provides real-time emotional analysis using a trained model, allowing users to engage in meaningful conversations. The chatbot stores past conversations in a PostgreSQL database using SQLAlchemy ORM, enabling users to revisit and continue previous chats.

Features ✨

• Emotion Detection: Analyzes text input to determine the user's emotional state.
• Chat History Management: Stores past conversations in a PostgreSQL database.
• User-Friendly Interface: Built with Streamlit for a seamless chat experience.
• Multi-Session Support: Allows users to navigate between different chat sessions.
• AI-Powered Responses: Utilizes an LLM model to generate intelligent replies.
• Delete Chat Option: Users can remove unwanted chat sessions.

Tech Stack 🛠️

• Frontend: Streamlit
• Backend: Python, SQLAlchemy ORM
• Database: PostgreSQL
• AI Model: LLM - Gemini

Dataset 📊

Anveshak Neo is trained on the Emotion Raw Dataset, which contains labeled emotion data for accurate sentiment analysis.

Installation 🚀

1. Clone the repository:

   git clone https://github.com/PythonicVarun/Anveshak-Neo.git
   cd Anveshak-Neo

2. Create a virtual environment and activate it:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Set up the PostgreSQL database and update the .env configuration.
5. Run the chatbot:

   python run.py

Running with Docker 🐳

To run Anveshak Neo using Docker, follow these steps:

1. Ensure you have Docker installed on your system.
2. Build and start the services using Docker Compose:

   docker-compose up --build

3. The PostgreSQL database and chatbot service will start automatically.
4. Access the chatbot at http://localhost:8501/.

Environment Variables 🌍

Create a .env file based on .env.example and update the required credentials:

# Database Configuration
POSTGRES_USER=your_postgres_user
POSTGRES_PASSWORD=your_postgres_password
POSTGRES_DB=your_postgres_db_name

# API Key for Gemini
GEMINI_API_KEY=your_gemini_api_key

# Database URL
DATABASE_URL=postgresql://your_postgres_user:your_postgres_password@localhost:5432/your_postgres_db_name

# Logging
LOG_LEVEL=DEBUG

Usage 📝

1️⃣ Starting a New Chat

• Click "Start New Chat" in the sidebar to begin a fresh conversation.
• A new chat session is created and stored in the database.

2️⃣ Navigating Between Chats

• Previous chats are displayed in the sidebar.
• Click on any chat title to continue a past conversation.

3️⃣ Sending Messages

• Type your message in the input box and press enter.
• Anveshak Neo will analyze and respond with an emotion-based reply.

4️⃣ Deleting a Chat

• Click the ❌ button next to a chat title in the sidebar to delete it.

Project Structure 📂

Anveshak-Neo/
├── .env.example
├── LICENSE
├── README.md
├── docker-compose.yml
├── models/
│   ├── dataset/
│   │   └── emotion_dataset_raw.csv
│   └── text_emotion.pkl
├── requirements-dev.txt
├── requirements.txt
├── results/
├── run.py
├── src/
│   ├── __init__.py
│   ├── app/
│   │   ├── __init__.py
│   │   ├── core/
│   │   │   ├── emotions.py
│   │   │   └── llm_response.py
│   │   ├── database/
│   │   │   └── db.py
│   │   ├── main.py
│   │   └── stylesheets/
│   │       └── styles.css
├── train-v2.py
└── train.py

Future Enhancements 🚀

• Implement voice input and output.
• Add support for multiple languages.
• Improve emotion detection with deep learning models.
• Integrate with external APIs for enhanced chatbot capabilities.

Contributing 🤝

Contributions are welcome! Feel free to submit a pull request or open an issue.

License 📜

See the BSD 3-Clause License for more details.

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© Varun Agnihotri 2025
All Rights Reserved