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https://github.com/gowthamsiddarthademan/pdf_summarizer
This is a PDF Summarizer Web App built using Flask and Hugging Face Transformers. The application allows users to upload PDF files and generate summarized text. The backend uses pre-trained NLP models for text summarization, powered by Hugging Face's Transformers library and PyTorch.
https://github.com/gowthamsiddarthademan/pdf_summarizer
flask huggingface-transformers python pytorch summarizer webapp
Last synced: about 1 month ago
JSON representation
This is a PDF Summarizer Web App built using Flask and Hugging Face Transformers. The application allows users to upload PDF files and generate summarized text. The backend uses pre-trained NLP models for text summarization, powered by Hugging Face's Transformers library and PyTorch.
- Host: GitHub
- URL: https://github.com/gowthamsiddarthademan/pdf_summarizer
- Owner: gowthamsiddarthademan
- Created: 2024-12-28T11:39:58.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-12-28T11:45:37.000Z (about 1 month ago)
- Last Synced: 2024-12-28T12:25:08.736Z (about 1 month ago)
- Topics: flask, huggingface-transformers, python, pytorch, summarizer, webapp
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# **PDF Summarizer Web App using Hugging Face Transformers**
This is a **PDF Summarizer Web App** built using **Flask** and **Hugging Face Transformers**. The application allows users to upload PDF files and generate summarized text. The backend uses pre-trained NLP models for text summarization, powered by **Hugging Face's Transformers** library and **PyTorch**.
#### **Key Features:**
- **PDF Upload**: Users can upload PDF files.
- **Text Extraction**: The app extracts text from the PDF and processes it for summarization.
- **Text Summarization**: Uses Hugging Face pre-trained models (e.g., BART or T5) to summarize the extracted text.
- **Web Interface**: Simple and clean user interface built with HTML and CSS, making it easy for users to interact with the app.
- **Real-time Summary**: After uploading the PDF, the app provides a summarized version of the document, displaying the result below the uploaded text.
#### **Technologies Used:**
- **Flask**: Lightweight web framework for serving the application.
- **Transformers (Hugging Face)**: For text summarization using pre-trained NLP models.
- **PyTorch**: Deep learning framework used by the Hugging Face models.
- **PyPDF2**: A Python library used to extract text from uploaded PDFs.
- **HTML/CSS**: For creating the front-end interface.
#### **Installation:**
1. Install the dependencies:
```bash
Flask==2.3.2
transformers==4.35.1
torch==2.1.0
PyPDF2==3.0.0
```
2. Run the Flask app:
```bash
python app.py
```
3. Open a browser and go to `http://127.0.0.1:5000` to use the app.
#### **How It Works:**
1. The user uploads a PDF file using the web interface.
2. The app extracts the text content from the PDF.
3. The extracted text is passed through a pre-trained summarization model (e.g., BART or T5).
4. The summarized text is displayed to the user.
#### **Usage:**
- Upload any PDF document.
- The app will extract the text and generate a summary based on the content.
- Summarized text is shown below the uploaded PDF content.
#### **Note:**
- The app works best for smaller PDFs. Large PDFs may require more time for processing due to the text extraction and summarization process.