ChatGPT build API and Database Program

ChatGPT – you must have experience with ChatGPT. You Must have experience building database programs. I want to build a simple database program with a front end. The front end is the input. I want to input blog articles and other content. I want to create prompts in the back end to pass to ChatGPT so that it creates the output and it stored in the database. The output should be catagorized and tagged so that we can use that ChatGPT writings in the future.

I am the CEO and not a programmer. I’m looking for someone who has EXPERIENCE and can GUIDE me to the right solution. This is project #1. I have many more database projects that we need for our company.

I need someone who wants to do continual part-time work and not disappear after the job is done.

You can work in YOUR time zone if it is better for you.

THIS CODE MUST MAKE SENSE TO YOU: I can provide you with an example of how you could write the code using Python, which is a popular programming language for natural language processing tasks. Here is an example of how you could accomplish the tasks you described using the OpenAI API and Python:

import openai_secret_manager

# Authenticate with the OpenAI API
secrets = openai_secret_Click to view_secrets(“openai”)
print(secrets)

import openai
Click to view_key = secrets[“api_key”]

# Get the input blog article
article = input(“Enter the text of the blog article: “)

# Use ChatGPT to generate a summary
response = Click to view(
engine=”text-davinci-002″,
prompt=f”Please summarize the following article: {article}”,
max_tokens=50
)
summary = response[“choices”][0][“text”]

# Use ChatGPT to generate comments on the article and summary
comments = []
for i in range(18):
response = Click to view(
engine=”text-davinci-002″,
prompt=f”Please write a 2-line comment on the following article and summary: {article} {summary}”,
max_tokens=20
)
Click to view(response[“choices”][0][“text”])

# Categorize the output and store it in a database
# (You will need to write code to accomplish this step)

print(“Summary: ” + summary)
print(“Comments:”)
for comment in comments:
print(comment)

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and is this something that you have experience with:

Hugging Face’s Transformers is a library for natural language processing (NLP) that provides pre-trained models for various NLP tasks such as text classification, language translation, and question answering. The library is built on top of PyTorch, which is a popular deep learning framework.

PyTorch is an open-source machine learning library based on the Torch library. It provides a wide range of tools for building and training machine learning models, including neural networks. PyTorch is particularly well-suited for deep learning tasks, and is often used in computer vision and natural language processing (NLP) applications.

TensorFlow is an open-source library for machine learning. It provides a wide range of tools for building and training machine learning models, and is particularly well-suited for deep learning tasks. TensorFlow is often used in computer vision and natural language processing (NLP) applications, as well as in other areas such as speech recognition and natural language understanding.

Both PyTorch and TensorFlow are widely used by researchers and practitioners in the field of machine learning and deep learning. They are both powerful libraries with a large community of users, and many pre-trained models and tutorials are available for them.