https://github.com/balaji1233/text_summary_pro

This is a text summarization app.
https://github.com/balaji1233/text_summary_pro

nlp python3 streamlit

Last synced: 21 days ago
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This is a text summarization app.

Host: GitHub
URL: https://github.com/balaji1233/text_summary_pro
Owner: balaji1233
License: mit
Created: 2023-12-04T08:52:41.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2023-12-13T10:25:39.000Z (almost 2 years ago)
Last Synced: 2024-01-29T08:51:37.942Z (almost 2 years ago)
Topics: nlp, python3, streamlit
Language: Python
Homepage: https://textsummarypro-dtkjxijmqktiku8t3tbxer.streamlit.app/
Size: 39.1 KB
Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

# Text-Summarizer-Pro
This is a Text Summary generator app.Either you can give it a text to summarize or you can upload a document in the upload option.It will seamlessly summarize your text paragraphs and documents.

Abstractive summarization is the process of generating a summary of a text by understanding its meaning and creating a new text that conveys the same information in a shorter form. Abstractive methods employ more powerful natural language processing techniques to interpret text and generate new summary text, as opposed to selecting the most representative existing excerpts to perform the summarization.
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The app is built using "txtai" a powerful NLP library. Txtai builds embeddings databases, which are a union of vector indexes and relational databases. This enables similarity search with SQL. Embeddings databases can stand on their own and/or serve as a powerful knowledge source for large language model (LLM) prompts.
Read More

My Streamlit app allows us to process both raw text and PDF files to get a summary.

## Resources
- Click for Live Demo

- Click for Documentation

# Pre-requisites
* [x] Any IDE
* [x] txtai[all] `pip install txtai`
* [x] streamlit `pip install streamlit`
* [x] PyPDF2 `pip install pyPDF2`

# Run the App
- Clone the repository
- Install the dependencies
- Execute `streamlit run app.py`

# Sample Output