{"id":15063730,"url":"https://github.com/dnyaneshvn/text-summarizer","last_synced_at":"2026-01-02T06:56:46.863Z","repository":{"id":256608117,"uuid":"855875443","full_name":"Dnyaneshvn/Text-Summarizer","owner":"Dnyaneshvn","description":"Text Summarizer: Conquer information overload with this smart app that distills key points from lengthy texts and PDFs.","archived":false,"fork":false,"pushed_at":"2024-09-11T15:57:00.000Z","size":81,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-22T08:37:32.755Z","etag":null,"topics":["bart","pypdf2","streamlit","text-summarizer","transformers"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Dnyaneshvn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-11T15:44:26.000Z","updated_at":"2024-09-11T16:01:50.000Z","dependencies_parsed_at":"2024-09-12T02:42:29.046Z","dependency_job_id":"2bd79abf-6268-4f76-88f5-19667d93cf86","html_url":"https://github.com/Dnyaneshvn/Text-Summarizer","commit_stats":null,"previous_names":["dnyaneshvn/text-summarizer"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dnyaneshvn%2FText-Summarizer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dnyaneshvn%2FText-Summarizer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dnyaneshvn%2FText-Summarizer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dnyaneshvn%2FText-Summarizer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Dnyaneshvn","download_url":"https://codeload.github.com/Dnyaneshvn/Text-Summarizer/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243779062,"owners_count":20346654,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bart","pypdf2","streamlit","text-summarizer","transformers"],"created_at":"2024-09-25T00:06:36.831Z","updated_at":"2026-01-02T06:56:46.816Z","avatar_url":"https://github.com/Dnyaneshvn.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Text Summarizer\n\nText Summarizer is a Streamlit web application that uses the BART (Bidirectional and Auto-Regressive Transformers) model to generate concise summaries of long texts. It supports both direct text input and PDF file uploads.\n\n![Text Summarizer Interface](output.jpg)\n\n## Features\n\n- Summarize text input directly\n- Summarize content from uploaded PDF files\n- Uses the state-of-the-art BART model for high-quality summaries\n- User-friendly interface built with Streamlit\n\n## Requirements\n\n- Python 3.7+\n- Streamlit\n- PyTorch\n- Transformers (Hugging Face)\n- PyPDF2\n\n## Installation\n\n1. Clone this repository:\n   ```\n   git clone https://github.com/yourusername/text-summarizer.git\n   cd text-summarizer\n   ```\n\n2. Install the required packages:\n   ```\n   pip install -r requirements.txt\n   ```\n\n   Note: Make sure to create a `requirements.txt` file with the following content:\n   ```\n   streamlit\n   torch\n   transformers\n   PyPDF2\n   ```\n\n## Usage\n\n1. Run the Streamlit app:\n   ```\n   streamlit run app.py\n   ```\n\n2. Open your web browser and go to `http://localhost:8501`.\n\n3. Choose your input method:\n   - \"Enter Text\": Type or paste your text directly into the text area.\n   - \"Upload PDF\": Upload a PDF file to summarize its content.\n\n4. Click the \"Summarize\" button to generate the summary.\n\n## How it Works\n\n1. The app uses the BART model (`facebook/bart-large-cnn`) for text summarization.\n2. For PDF inputs, it extracts text using PyPDF2 before summarization.\n3. The model generates a summary with a maximum length of 150 tokens and a minimum length of 50 tokens.\n\n## Customization\n\nYou can adjust the summarization parameters in the `summarize_text` function:\n\n- `max_length`: Maximum length of the generated summary (default: 150)\n- `min_length`: Minimum length of the generated summary (default: 50)\n\n## Contributing\n\nContributions are welcome! 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