This project implements an intelligent chatbot powered by Microsoft's DialoGPT, a state-of-the-art conversational AI model, to enable natural and human-like interactions. The chatbot is integrated with WhatsAuto, a WhatsApp automation tool, allowing it to send and receive messages automatically on the WhatsApp platform. Built using the Flask framework, the system provides a seamless backend for handling user queries and generating context-aware responses. This solution is designed for applications such as customer support, FAQs, and personalized messaging, offering a scalable and efficient way to automate conversations on one of the world's most popular messaging platforms.
Methodology
The implementation of the chatbot involves the following key steps and technologies:
1. Chatbot Development with DialoGPT
DialoGPT Model: Microsoft's DialoGPT, a pre-trained conversational AI model based on GPT architecture, is used to generate human-like responses.
Fine-Tuning (Optional): The model can be fine-tuned on custom datasets to improve its performance for specific use cases.
Response Generation: User inputs are processed by DialoGPT, which generates contextually relevant responses.
2. Backend Development with Flask
Flask Framework: A lightweight Python web framework is used to create the backend server for the chatbot.
API Endpoints: RESTful APIs are implemented to handle incoming requests, process user inputs, and return chatbot responses.
Integration with DialoGPT: The Flask backend communicates with the DialoGPT model to generate responses dynamically.
3. WhatsApp Automation with WhatsAuto
WhatsAuto Integration: The chatbot is integrated with WhatsAuto, a tool for automating WhatsApp messaging.
Message Handling: WhatsAuto listens for incoming messages on WhatsApp, forwards them to the Flask backend, and sends the chatbot's responses back to the user.
Automation Workflow: The system ensures seamless two-way communication between the user and the chatbot on WhatsApp.
4. System Workflow
A user sends a message on WhatsApp.
WhatsAuto captures the message and forwards it to the Flask backend through Ngrok.
The Flask backend processes the message using DialoGPT to generate a response.
The response is sent back to WhatsAuto, which delivers it to the user on WhatsApp.
