🚀 Text Summarization App

A streamlined and efficient web-based application for summarizing text, built using 🧠 LangChain, 🌟 Streamlit, and the Gemma2-9b-It language model.

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✨ Features

• 📝 Text Summarization: Generate concise and to-the-point summaries of any input text, regardless of length.
• ⚡ Powered by Cutting-Edge LLM: Utilizes the Gemma2-9b-It model via LangChain for accurate and context-aware summaries.
• 💻 User-Friendly Interface: Simple, fast, and intuitive interface for seamless text summarization.

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🤖 About the Model: Gemma2-9b-It

The Gemma2-9b-It model, created by Google, is a lightweight yet powerful open large language model designed for a variety of text generation tasks, including summarization. This app leverages the model's capabilities to produce concise and to-the-point summaries.

Key Features

• Expertise in Summarization: Designed to produce high-quality, contextually relevant summaries that respect the nuances of the input text.
• Scalability: Efficiently handles varying input lengths while maintaining summary quality.
• Efficient Deployment: Optimized for environments with limited resources, such as laptops, desktops, or cloud infrastructure.
• Open-Source: Provides accessible innovation, fostering development in Responsible AI.

Model Information

• Type: Text-to-text, decoder-only large language model.
• Training Data:
  • Trained on 8 trillion tokens from diverse sources, including web documents, code, and mathematical texts.
  • Focus on high-quality English-language data.
• Evaluation Benchmarks:
  • Achieves state-of-the-art results in summarization and text generation.
  • Benchmarks include:
    • MMLU (5-shot): 71.3%
    • ARC-e (0-shot): 88.0%
    • GSM8K (5-shot): 68.6%
• Maximum Token Limit: Capable of processing up to 8,192 tokens, ensuring robust handling of lengthy texts.

Model Training and Hardware

• Hardware: Trained using TPUv5p (Tensor Processing Units), which provide high performance and scalability.
• Software: Built with JAX and ML Pathways, allowing efficient training and deployment.

Intended Usage

• Primary Tasks:
  • Text summarization for research papers, reports, and long-form content.
  • General text generation and reasoning tasks.
• Limitations:
  • Model outputs rely on training data quality and may not always provide accurate factual statements.
  • Context ambiguity and subtle language nuances can affect response quality.

Ethical Considerations

• Bias Mitigation: Rigorous filtering applied to training data to minimize biases and harmful content.
• Privacy: Data preprocessing ensures removal of Personally Identifiable Information (PII).
• Responsible Use: Users are encouraged to implement content safety measures and adhere to guidelines provided in the Responsible Generative AI Toolkit.

Why Gemma2-9b-It?

This model combines cutting-edge technology with accessibility, making it a perfect choice for developers and researchers seeking powerful, open large language models tailored for Responsible AI development.

For more technical details, visit the Gemma Model Page.

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🛠️ Installation

1. Clone the Repository

git clone https://github.com/your-username/text-summarization-app.git
cd text-summarization-app

2. Set up a virtual environment (recommended):

python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

pip install -r requirements.txt

*4. Configure API Key

• Add your GROQ_API_KEY to Streamlit secrets.
• If running locally, create a .env file in the root directory:

GROQ_API_KEY=your_groq_api_key_here

4. Run the app:

streamlit run app.py

🎮 Usage

1. Open the app in your browser (default: http://localhost:8501).
2. Get your summary instantly!