Custom ChatGPT model to build a Flask API

Here is an example of how you might use a custom ChatGPT model to build a Flask API in Python:

First, you will need to install the openai and flask libraries using the following commands:

pip install openai
pip install flask

Next, you will need to load your custom ChatGPT model using the openai library:

import openai

# Load the custom ChatGPT model
model = openai.Model.load("my_model")

Once you have loaded the model, you can use the following code to create a Flask API that allows users to send input text and receive responses from the model:

import flask
from flask import request

app = flask.Flask(__name__)

@app.route("/predict", methods=["POST"])
def predict():
    # Get the input text from the request
    input_text = request.form["input"]

    # Set the context
    context = "I'm a chatbot."

    # Generate a response using the custom ChatGPT model
    response = model.prompt(input_text, context)

    # Return the response as a JSON object
    return {"response": response}

if __name__ == "__main__":
    app.run()

This code creates a Flask API with a single endpoint, /predict, that accepts POST requests with a form field called input. When the endpoint receives a request, it uses the custom ChatGPT model to generate a response based on the input text and the specified context, and returns the response as a JSON object.

To test the API, you can send a POST request to the /predict endpoint using a tool like curl or Postman, or you can use the following Python code:

import requests

# Set the input text
input_text = "Hello, how are you today?"

# Send a request to the API
response = requests.post("http://localhost:5000/predict", data={"input": input_text})

# Print the response
print(response.json())

This will send a POST request to the /predict endpoint with the input text, and print the response returned by the API.

Keep in mind that this is just a simple example, and there are many other options and settings that you can use to customize the behavior of the API. You can find more information about these options in the Flask documentation and the OpenAI API documentation.