📝 Multilingual Text Summarizer is a web application that summarizes text, PDFs, and images in multiple languages using a T5 transformer model. The application is built with Streamlit, EasyOCR, and Hugging Face Transformers.
- Summarize text input directly
- Summarize content from uploaded PDF, TXT, and image files
- Detect and handle multiple languages
- Translate summarized text to English
- Chat-like prompt system for refining summaries
You can try the live demo on Streamlit Cloud.
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Clone the repository:
git clone https://github.com/RobinMillford/llm-based-text-summarizer.git cd LLM-Based-Text-Summarizer
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Create and activate a virtual environment:
python -m venv myenv source myenv/bin/activate # On Windows use `myenv\Scripts\activate`
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Install dependencies:
pip install -r requirements.txt
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Run the Streamlit app:
streamlit run app.py
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Pull the Docker image:
docker pull yamin69/summarizer:latest
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Run the Docker container:
docker run -p 8501:8501 yamin69/summarizer:latest
- Navigate to the app URL.
- Choose an input method:
- Direct Text Input
- Upload File (PDF, TXT, Image)
- Enter or upload your content.
- Optionally add prompts to refine the summary.
- Click "Generate Summary" to get the summarized text.
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Fork the repository:
git fork https://github.com/RobinMillford/llm-based-text-summarizer.git
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Create a branch:
git checkout -b feature-branch
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Make your changes and commit them:
git commit -am 'Add new feature'
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Push to the branch:
git push origin feature-branch
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Create a new Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
For any issues, please create a new issue.