#chat bot API using GPT-3+DALL-E-2+celery+flask dockerize

8 messages · Page 1 of 1 (latest)

sharp canyon
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Hi! Just make a chatbot API back-end with GPT-3 and DALL-E-2.
Anyone can use it.
The chat bot built using the Flask web framework and will be powered by the Celery task queue to handle multiple requests concurrently. The project will be containerized using Docker to ensure consistency and ease of deployment. The primary goal of this project is to create a robust and efficient chat bot API that can understand and respond to a wide range of inputs in a natural way.

https://github.com/shamspias/chat-gpt-celery-flask.git

Thanks a lot.

GitHub

A sample implementation of a chatbot using Flask, Celery, and GPT-3. It demonstrates how to create a simple chatbot that uses GPT-3 as the language generation model and Celery for handling asynchro...

full marsh
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Does it remember previous conversations also?

sharp canyon
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For now nop.
But will add it soon.

restive swallow
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What value does celery add over just using flask?

sharp canyon
# restive swallow What value does celery add over just using flask?

Celery is a task queue library that allows you to run background tasks asynchronously. It allows you to offload long-running tasks from your web application so that it can continue to handle incoming requests. This is useful in situations where a task takes a long time to complete, such as sending an email or processing an image, and you don't want the user to wait for the task to finish before receiving a response.

Celery also provides a way to distribute tasks across multiple worker processes or even multiple machines, which can help to increase the overall performance and scalability of your application. It also provides a way to track the status of tasks, retry failed tasks, and handle task dependencies.

Benefits of using Celery with Flask include:

Improved user experience: Tasks that take a long time to complete can be run in the background, allowing the web application to respond to other requests more quickly.

Scalability: Celery allows you to distribute tasks across multiple worker processes or machines, making it easier to scale your application as needed.

Flexibility: Celery supports multiple message brokers, such as Redis, RabbitMQ, and Amazon SQS, giving you more flexibility in choosing the best option for your needs.

Task monitoring: Celery provides a way to track the status of tasks, retry failed tasks, and handle task dependencies. This can help you to better understand and optimize the performance of your application.

In summary, Celery allows you to run background tasks in an efficient, scalable, and flexible way, which is particularly useful when building web applications with Flask or any other web framework.

restive swallow
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Did chatGPT write this?

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If I'm using a distributed service to run flask like Google App Engine would this still help me?

sharp canyon