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1. #OPENAI CHATBOT PYTHON HOW TO#
2. #OPENAI CHATBOT PYTHON INSTALL#
3. #OPENAI CHATBOT PYTHON GENERATOR#
4. #OPENAI CHATBOT PYTHON FULL#

We capture the response from the aicontent function and make it available to be displayed in the front-end for the user.We will call the aicontent function from the Flask View function with: aicontent.openAIQuery(query).Query = "Write a cold email to potential clients about: ”.format(submission) - is the final query being sent to the API, the submission is what the user entered. Return render_template('index.html', methods=) Inside the Flask Application - rewrite the route for cold email generation to look like so: from flask import Flask, render_template, requestĪpp.om_object(nfig)Īpp.register_error_handler(404, methods=) Provide it with a query word like this statement we tested above: Generate a cold email about CRM Software.Īnd it will return a response from the API, which we will parse to get to the answer we can display back to the user.

This code will allow you to call the function: openAIQuery(query) create a new file called aicontent.py and in this file, you can paste the following code: import openaiĪnswer = 'Opps sorry, you beat the AI this time' We are now ready to export this code to our flask application that we downloaded from GitHub.

#OPENAI CHATBOT PYTHON GENERATOR#

You can now click on the “view code” button at the top to get the code for Python. Extract the Python API code from Playground However, if you had a brand or keyword that is not popular, you might want to add some description to help the AI along. You can play around with the instruction prompt, changing the text, until you get the results that you want, but in our case the instruction we gave worked pretty well for CRM Software. Also note the model we are using is davinci-instruct-beta-v3 which is much better at this type of use-cases, where you give it an instruction for completion.Įnter the following text into the input: Generate a cold email about CRM Software. Let us take the “cold email” use case, for example, head over to the Playground and try to generate some email content.Īdjust the response output to 200, and leave the temperature at 0.7. This is a great tool to test your prompts and adjust the model parameters to get the results you seek. The very first step is to try and use the playground tool, that OpenAI has made available to test the code and functionality.

#OPENAI CHATBOT PYTHON FULL#

You can also watch the full video tutorial on YouTube here: Video Tutorial - AI Content Generator with Python Flask and GPT-3 Then get your API key from OpenAI and add it to the config file and you are ready to go.

#OPENAI CHATBOT PYTHON INSTALL#

Then install the required libraries: pip install flask openai GitHub - skolo-online/ai-content-starting-template The code already includes a working flask app with all the HTML pages, routes, and files that you will need. In the interest of time, I have made the Python Flask code available for our application. Starting Code - AI Content Generator with Python Flask and GPT-3 Imagine training a model with just 10 TB of data yourself. I am particularly excited about GPT-3 because, for the first time, AI functionality is going to be made available to average developers who do not have the skill-set to create and train their own models. This means that we suggest to it what we want, and it completes the writing on our behalf, using artificial intelligence to determine the outputs. The model we will be working with today is a “completion” model. GPT-3 was trained on over 45TB of data from the internet and is capable of producing human-like content.

#OPENAI CHATBOT PYTHON HOW TO#

In this tutorial we are going to show you step by step how to use the OpenAI API based on GPT-3 to generate AI content for the following use cases: