Customizable Support Chatbot with Sentiment Analysis

This project is a smart e-commerce support chatbot built with Next.js, Google Gemini API, and ShadCN UI components. The chatbot provides real-time assistance to users, analyzes their sentiments, and stores all conversation data in the browser's localStorage. It also collects user feedback and saves it for future insights. For now I have taken a usecase as if it is a support assistant for an e-commerce store of shoes named Snikre(which is my brand itself).

Features

• AI-Powered Chatbot: Powered by the Google Gemini API to provide intelligent responses.
• Sentiment Analysis: Analyzes the sentiment of user messages (positive, neutral, or negative).
• Local Data Storage: Stores conversation history and sentiment data in localStorage for persistence.
• Feedback Collection: Collects user feedback on products for sentiment tracking and insights.
• Modern UI: Built with ShadCN's elegant and responsive UI components.
• Lightweight and Fast: Optimized for performance and usability.

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Installation

1. Clone the Repository

   git clone https://github.com/AyushAwasthi2384/Support-System-Chatbot
   cd Support-System-Chatbot

2. Install Dependencies Make sure you have Node.js and npm/yarn installed. Then, install the dependencies:

   npm install
   # or
   yarn install

3. Set Up Environment Variables Create a .env file in the root of your project and add your Google Gemini API key:

   GEMINI_API=your-google-gemini-api-key

4. Run the Application Start the development server:

   npm run dev
   # or
   yarn dev

5. Open in Browser Navigate to http://localhost:3000 to view the application.

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How It Works

1. User Interaction: Users type messages in the chatbot interface.
2. Sentiment Analysis: Each user message is analyzed for sentiment using a lightweight sentiment analysis function.
3. Response Generation: The Google Gemini API generates intelligent responses based on the conversation context.
4. Data Persistence:
   • The entire conversation, including sentiment data, is saved in the browser's localStorage.
   • Feedback for specific products is collected and also stored in localStorage.

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API Endpoints

1. Save Conversation

Route: /api/save/conversation
Method: POST
Description: Saves conversation data in localStorage.

2. Save Product Feedback

Route: /api/save/product-feedback
Method: POST
Description: Collects user feedback for products and stores it with relevant sentiment data.

3. Analyze Sentiment

Route: /api/sentiment
Method: POST
Description: Analyzes the sentiment of a given message.

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Technologies Used

• Next.js: Framework for building the application.
• Google Gemini API: For generative AI capabilities.
• ShadCN: For responsive and modern UI components.
• React: Frontend library for building the chatbot interface.
• React-Markdown: For rendering markdown in chat messages.
• LocalStorage: For storing conversation and feedback data.

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Future Enhancements

• Add user authentication for personalized experiences.
• Migrate from localStorage to a robust database like MongoDB.
• Enable exporting chat history for customer support teams.
• Add multi-language support for a global audience.

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Contributing

We welcome contributions to improve this project! To contribute:

1. Fork this repository.
2. Create a new branch: git checkout -b feature-name.
3. Make your changes and commit: git commit -m 'Add feature-name'.
4. Push to the branch: git push origin feature-name.
5. Create a pull request.

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License

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

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Acknowledgments

• Google Gemini API for powerful and efficient NLP.
• ShadCN for their amazing UI components.

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Developed by Ayush Awasthi🚀 and team!

(in half an hour😉)