Introduction This is a simple web application that implements a chatbot using Flask and the DialoGPT model from the Hugging Face Transformers library.

Setup To run this application, follow these steps:

1. Install the required dependencies by running the following command: pip install flask transformers torch
2. Download the pre-trained DialoGPT-large model by executing the following Python code: from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-large') model = AutoModelForCausalLM.from_pretrained('microsoft/DialoGPT-large')
3. Save the code provided in the app.py file.
4. Run the application by executing the following command: python app.py
5. Open your web browser and navigate to http://localhost:5000 to access the chatbot. localhost might be replaced with the actual address.

Main part The chatbot web application provides a simple user interface for interacting with the chatbot. It consists of a chatbox where messages are displayed, and an input field where the user can enter their queries.

When a user sends a query by clicking the "Send" button or pressing Enter, the application sends an HTTP POST request to the server, passing the user's input as a parameter. The server then generates a response using the DialoGPT model and returns it to the client, which displays the response in the chatbox.

The conversation history is stored on the server using global variables. Each user query is appended to the conversation history before generating a response. The conversation history is updated with each interaction to maintain context.

Files app.py: The main Flask application file containing the server-side logic. index.html: The HTML template file for the chatbot interface. style.css: The CSS file for styling the chatbot interface. script.js: The JavaScript file for handling user interactions and updating the chatbox.

Dependencies The application relies on the following Python packages: Flask: A micro web framework for building web applications. Transformers: A library for state-of-the-art natural language processing using pre-trained models. Torch: A machine learning library that provides support for deep learning algorithms. These dependencies can be installed using pip.

Referemce The application utilizes the following resources: DialoGPT: The DialoGPT model from Microsoft, trained on a large corpus of conversational data. Hugging Face Transformers: The Transformers library provided by Hugging Face, which offers a high-level API for working with transformer models.