Building a Conversational AI with OpenAI’s ChatGPT and Python: A FastAPI Guide

Introduction:

In recent years, OpenAI’s GPT (Generative Pretrained Transformer) models have revolutionized the field of Natural Language Processing. One of the models, ChatGPT, is a highly capable conversational AI model that can answer questions and engage in dialogue. In this article, we’ll explore how to use ChatGPT in Python and build a FastAPI API to serve the model.

Setting up the Environment:

To use the ChatGPT model, we first need to install the Hugging Face’s transformers library. We’ll also need to install FastAPI and uvicorn for building the API. The installation can be done via pip:

pip install transformers
pip install fastapi
pip install uvicorn

Loading the Model:

Once we have installed the required packages, we can load the ChatGPT model with the following code:

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

model = GPT2LMHeadModel.from_pretrained("distilgpt2")
tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")

GPT2Tokenizer and GPT2LMHeadModel are classes from the transformers library that are used to work with OpenAI's GPT-2 language model.

• GPT2Tokenizer is a tokenizer that is used to encode and decode text into numerical representations (tokens). Tokens are the basic building blocks that the GPT-2 model uses to understand and generate text. The tokenizer takes in text, encodes it into a sequence of tokens, and returns the encoded representation. It can also take the encoded representation and decode it back into text.
• GPT2LMHeadModel is a language modeling head that is used to generate text given a prompt. It is a type of Transformer network that has been pre-trained on a massive amount of text data. When given an input prompt, the GPT-2 model generates the continuation of the text. The GPT2LMHeadModel class provides methods to fine-tune the pre-trained model on specific tasks and generate responses to text inputs.

Together, the GPT2Tokenizer and GPT2LMHeadModel classes provide a convenient and high-level interface for working with the GPT-2 model.

Generating Responses:

With the model loaded, we can generate responses to questions using the following code:

def generate_response(input_text):
    input_ids = torch.tensor(tokenizer.encode(input_text, return_tensors="pt")).unsqueeze(0)
    response = model.generate(input_ids)
    return tokenizer.decode(response[0], skip_special_tokens=True)

Building the API:

With the response generation code in place, we can now build the API using FastAPI. The API will take an input text and return the generated response.

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
async def root(input_text: str):
    response = generate_response(input_text)
    return {"response": response}

Running the API:

We can run the API using the following command:

uvicorn main:app --reload

Conclusion:

In this article, we’ve explored how to use OpenAI’s ChatGPT model in Python and build a FastAPI API to serve the model. The API can be used to build a wide range of conversational AI applications. With the power of OpenAI’s models, the possibilities are endless!