THIS README IS NOT FIXED AND WILL BE CHANGED AFTER THE PROJECT COMPLETION

Project Overview

This project is a Flask-based application designed to integrate multiple components for handling imagery data, classifying animals, and providing detailed information about them through a custom QnA chatbot. The system uses MongoDB for storage, OpenCV for image classification, and a custom RAG (Retrieval-Augmented Generation) model for answering questions about animals based on a custom dataset.

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Features

1. Image Classification:

   • Users can upload images of animals.
   • OpenCV processes the image, and a machine learning model classifies the animal.

2. Custom QnA Chatbot:

   • Uses a custom-trained RAG model to answer user queries about animals.
   • Knowledge base includes books, datasets, and additional resources.

3. MongoDB Integration:

   • Imagery data and metadata are stored in MongoDB for efficient retrieval.

4. Frontend Integration:

   • Flask connects to a ReactJS/HTML-based frontend for user interaction.
   • Users can upload images and chat with the chatbot through a single interface.

5. Modular Design:

   • Supports multiple chatbots integrated into a single platform.

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Architecture

1. Frontend:

   • ReactJS/HTML for user interface.
   • Features include image upload and chatbot interaction.

2. Backend (Flask):

   • REST API endpoints for:
     • Image upload and classification.
     • Chatbot communication.
   • Modular architecture to manage multiple chatbots.

3. Image Processing:

   • OpenCV used for preprocessing and classification.
   • Model trained on labeled animal datasets using TensorFlow/PyTorch.

4. Chatbot (RAG Model):

   • Retrieval-Augmented Generation model trained on a custom animal dataset.
   • Combines a retriever for finding relevant knowledge and a generator for answering queries.

5. Database:

   • MongoDB for storing:
     • Images and metadata.
     • User queries and session history.

Architecture Diagram

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Installation

Prerequisites

• Python 3.8+
• MongoDB
• Node.js (for frontend)
• OpenCV
• TensorFlow/PyTorch
• Flask

Steps

1. Clone the Repository:

   git clone https://github.com/username/animal-classification-chatbot.git
   cd animal-classification-chatbot

2. Set Up Backend:

   • Install Python dependencies:

     pip install -r requirements.txt

   • Run the Flask server:

     python app.py

3. Set Up Frontend:

   • Navigate to the frontend directory:

     cd frontend

   • Install dependencies and start the frontend:

     npm install
     npm start

4. Run MongoDB:

   • Ensure MongoDB is running locally or use a cloud-based MongoDB instance.

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Usage

1. Upload Image:

   • Navigate to the frontend.
   • Upload an animal image.
   • The backend processes the image and returns the classification result.

2. Interact with Chatbot:

   • Ask questions about animals (e.g., "Tell me about lions").
   • The chatbot fetches relevant information from the custom knowledge base and provides detailed answers.

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Endpoints

Image Classification

• POST /classify
  • Input: Image file
  • Output: Classified animal name and confidence score.

Chatbot

• POST /chatbot
  • Input: User query
  • Output: Chatbot response.

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Tech Stack

• Frontend: ReactJS, HTML, CSS
• Backend: Flask, OpenCV, TensorFlow/PyTorch
• Database: MongoDB
• Chatbot: Custom RAG model

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Future Enhancements

1. Add live camera feed processing for real-time animal classification.
2. Enhance chatbot with multilingual support.
3. Deploy the system on cloud platforms like AWS/GCP for scalability.
4. Integrate user authentication for personalized experiences.

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Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

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License

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

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Kuch aur customize karna ho toh bolna, bhai! 😊