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Built using the `Streamlit` framework and the Hugging Face Transformers library. VibeCheck provides users with insights into the tone and essence of their input text.\n\n![VibeCheck](appImage.png)\n\nDeployment\nVibeCheck is deployed on Streamlit Sharing. You can access the live application at: [VibeCheck](https://vibecheck.streamlit.app/)\n\n## Features\n- **Text Summarization**: Input any piece of text, and VibeCheck will provide a concise summary, capturing the main points effectively.\n- **Sentiment Analysis**: Get an immediate understanding of the sentiment behind the text. The application categorizes the sentiment as positive, negative, or neutral along with a confidence score.\n- **User-Friendly Interface**: Designed with simplicity in mind, VibeCheck allows users to easily input text and view results instantly.\n\n## Technologies Used\n- **Python**: The core programming language for backend processing.\n- **Streamlit**: For building the interactive web application interface.\n- **Hugging Face Transformers**: For advanced NLP tasks, including text summarization and sentiment analysis.\n - Condenses lengthy texts into concise summaries using the `T5 model`\n - Classifies the sentiment of the text or its summary as positive, negative, or neutral using `DistilBERT`.\n- **PyTorch**: As the underlying framework for the machine learning models used in this application.\n\n## Acknowledgments\n- Hugging Face for providing the Transformers library.\n- The creators of the T5 and DistilBERT models for their contributions to NLP research.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdas-amlan%2FVibeCheck---Text-Summarizer-Sentiment-Analyzer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdas-amlan%2FVibeCheck---Text-Summarizer-Sentiment-Analyzer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdas-amlan%2FVibeCheck---Text-Summarizer-Sentiment-Analyzer/lists"}