How to Install ChatGPT Locally

ChatGPT is a state-of-the-art language model trained by OpenAI. It is based on the GPT architecture and has been trained on a massive amount of text data. ChatGPT is capable of generating coherent and contextually relevant responses to user input. This makes it an ideal candidate for use in chatbots and other natural language processing applications
In this blog post, we will discuss how to host ChatGPT locally. We will walk you through the steps needed to set up a local environment for hosting ChatGPT, and provide sample code that you can use to interact with the model.
Step 1: Install Required Dependencies
To host ChatGPT locally, you will need to install the following dependencies:

Python 3.7 or later
PyTorch
Transformers
Flask

You can install these dependencies using pip. Open up your terminal or command prompt and run the following commands:

pip install torch
pip install transformers
pip install flask

Step 2: Download the Pre-Trained Model
Updates: OpenAI has recently removed the download page of chatGPT, hence I would rather suggest to use PrivateGPT

The next step is to download the pre-trained ChatGPT model from the OpenAI website. You can download the model from the following link:
https://beta.openai.com/docs/guides/chat-gpt/download
Once you have downloaded the model, extract the files to a directory of your choice.
Step 3: Create a Flask App
The next step is to create a Flask app that will host the ChatGPT model. Create a new file called app.py and add the following code:

from flask import Flask, request, jsonify
from transformers import GPT2LMHeadModel, GPT2Tokenizer
app = Flask(__name__)
tokenizer = GPT2Tokenizer.from_pretrained('path/to/model')
model = GPT2LMHeadModel.from_pretrained('path/to/model')
model.eval()
@app.route('/generate', methods=['POST'])
def generate():
    data = request.get_json()
    prompt = data['prompt']
    length = data['length']
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    output = model.generate(input_ids, max_length=length, do_sample=True)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return jsonify({'response': response})

In this code, we import the necessary libraries, create a Flask app, load the pre-trained ChatGPT model, and define a route for generating responses. The generate function takes a prompt and a length as input, generates a response using the model, and returns the response as a JSON object.
Step 4: Test the Flask App
To test the Flask app, run the following command in your terminal:

export FLASK_APP=app.py
flask run

This will start the Flask app on your local machine. You can now send a POST request to the /generate endpoint to generate responses. Here's an example using the requests library:

import requests
url = 'http://localhost:5000/generate'
data = {'prompt': 'Hello, how are you?', 'length': 50}
response = requests.post(url, json=data).json()
print(response['response'])

This code sends a POST request to the Flask app with a prompt and a desired response length. The app generates a response using ChatGPT and returns it as a JSON object, which we then print to the console.
Conclusion
In this blog post, we have shown you how to host ChatGPT locally using Flask. We have provided sample code that you can use to interact with the model and generate responses. By hosting ChatGPT locally, you can take advantage of its powerful language processing capabilities without relying on a remote API, which can be more secure and faster. Additionally, hosting ChatGPT locally gives you more control over the model and allows you to customize it to your specific needs.
However, it's important to note that hosting ChatGPT locally requires significant computing resources. The pre-trained model is very large, and generating responses can be computationally expensive. You will need a powerful CPU and enough RAM to load and run the model. Additionally, generating responses in real-time may not be feasible on a low-end machine.
If you plan to use ChatGPT in a production environment, it's recommended to use a cloud-based solution, such as OpenAI's API or a cloud-hosted version of the model. These solutions offer scalable and reliable access to the model without requiring you to manage the infrastructure.
In conclusion, hosting ChatGPT locally using Flask is a great way to experiment with the model and explore its capabilities. With the provided code and a little bit of Python knowledge, you can create your own chatbot or natural language processing application. However, keep in mind that hosting ChatGPT locally requires significant computing resources, so it may not be suitable for all use cases.