This code is importing the OpenAI package and setting the API key. It then loads a file from storage using Google Colab. The code snippet after that reads the file contents using PyPDF2 for PDF files, docx for Word documents, and plain text files for text files. It then defines a function that takes the source and target languages, the text to summarize, and the desired summary length as inputs. The function uses OpenAI's GPT-4 language model to translate the input text from the source to the target language and generate a summary of the specified length. Finally, the function is called with the loaded text, English as the source language, Hindi as the target language, and a desired summary length of 20 sentences or less. The resulting summary is printed to the console.
- Based on a REAL-WORLD USE CASE, our Helix team members want to connect and communicate more effectively with business partners on opposite sides of the world.
- Two of our six Helix team members actually worked on a project in 1Q2023 with one of the TOP 3 VEHICLE MANUFACTURERS in the world. The vehicles are manufactured in India and sold around the world.
- However, in North America, some of the vehicles are ‘road tested’ by drivers who only speak English. The manufacturer has more than 3,000 engineers, but some of them only speak in Hindi. They need to share lots of feedback quickly, accurately and efficiently.
We solved this challenge!
- Improving cross-functional collaborations between multilingual peers.
- Helix will provide cross-language context between members of separate languages by both: Translating and Summarizing text
- Enables peers to quickly understand and respond.