SECRET_KEY

ANSWERS_FILE_ID

SEARCH_FILE_ID

CLASIFICATIONS_FILE_ID

Learn the OpenAI API with Python and NLP by mastering API endpoints, text generation, moderation, embeddings, and Flask integration for real-world tasks.

Using OpenAI API for Natural Language Processing in Python.png

dockerfile.tar.gz

openai_completion

embeddings

python

flask

This course introduces OpenAI API and NLP in Python, teaching you how to use the OpenAI API for real-world natural language processing tasks. You’ll begin by exploring the OpenAI API with Python, setting up your account, and learning how to access key endpoints. The completions endpoint will be covered for generating, classifying, and transforming text.

The course then covers advanced NLP in Python techniques using other OpenAI endpoints like moderations and embeddings for in-depth text analysis. You’ll practice these methods to efficiently analyze and manipulate text data.

Finally, you will integrate your OpenAI skills with Flask to build interactive, NLP-powered applications. The course emphasizes real-world projects, helping you use OpenAI’s capabilities to solve problems in content generation, text classification, and more. After completing this course, you can apply NLP in Python using OpenAI API to create scalable solutions.

Using OpenAI API for Natural Language Processing in Python

# Overview
The chat completions endpoint has the ability to translate from one language to another language, summarize the text, and convert the text to emojis or vice versa.




# Translation
The OpenAI API can translate from one language to another language.

Let's see how to provide the prompt to translate from English to other languages.

```text
Translate this into 1. French, 2. Spanish and 3. Japanese.

We provide an online learning platform made by developers, created for developers.
```

## Example

Let's try the above example in the code widget below:

#Translating from english to other languages

from openai import OpenAI
client = OpenAI(api_key="{{SECRET_KEY}}")

response = client.chat.completions.create(
  model="gpt-3.5-turbo",
  response_format={ "type": "json_object" },
  messages=[
    {"role": "system", "content": "Translate this into 1. French, 2. Spanish and 3. Japanese. Provide output in json"},
    {"role": "user", "content": "We provide an online learning platform made by developers, created for developers."}
  ],
  temperature=0,
  max_tokens=130,
  top_p=1.0,
)
print(response.choices[0].message.content)

In the code above, `temperature` is set to `0` because we don’t need randomness in the translation, and the value of `max_tokens` is set to `100` because we know the approximate length of output tokens. All other parameter values are the default values.

Similarly, we can design a prompt to translate it back into English:
```text
Translate the following into the English language:

1. Nous fournissons une plateforme d'apprentissage en ligne créée par des développeurs, pour des développeurs.
2. Proporcionamos una plataforma de aprendizaje en línea creada por desarrolladores, para desarrolladores.
3. 開発者のために
```

Learn how to translate and summarize text, and convert it to emojis using the OpenAI API.

Introduction

The Completions Endpoint

Miscellaneous Endpoints

APIs Integration in Flask

Conclusion

Appendix

Transformation

Overview

Translation

Example