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Welcome toMediSight.AI, a local-first full-stack application designed to analyze medical PDFs using the AI model Apollo2-2B. This project emphasizes privacy and patient-friendly insights without relying on external APIs or cloud services.

• Features
• Technologies Used
• Installation
• Usage
• Contributing
• License
• Contact
• Releases

• Local Processing: All data processing occurs on your device, ensuring that sensitive information remains private.
• AI-Powered Insights: Leverage the Apollo2-2B model to gain valuable insights from medical reports.
• User-Friendly Interface: A clean and intuitive chat interface allows users to interact seamlessly with the application.
• FastAPI Backend: The backend is built using FastAPI, providing a robust and efficient server for handling requests.
• PDF Analysis: Extract and analyze information from medical PDFs with ease.
• Responsive Design: Built with Tailwind CSS, the app is mobile-friendly and visually appealing.

This project utilizes a variety of technologies to create a smooth user experience:

• AI & Machine Learning: Apollo2-2B model for medical analysis
• Backend: FastAPI for efficient API development
• Frontend: Tailwind CSS for styling and responsiveness
• PDF Processing: Libraries for extracting data from PDF files
• Data Handling: PyTorch for machine learning operations
• Transformers: For natural language processing tasks

To get started with MediSight.AI, follow these steps:

1. Clone the Repository:

   git clone https://github.com/natharmatron/MediSight.AI.gitcd MediSight.AI

2. Set Up the Environment:It is recommended to use a virtual environment. You can create one using:

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

3. Install Dependencies:Install the required packages using pip:

   pip install -r requirements.txt

4. Run the Application:Start the server with:

   uvicorn app.main:app --reload

5. Access the Application:Open your web browser and go tohttp://localhost:8000.

Using MediSight.AI is straightforward:

1. Upload a Medical PDF: Drag and drop your PDF file into the designated area.
2. Interact with the AI: Use the chat interface to ask questions or request specific insights from the uploaded document.
3. Review Insights: The AI will provide responses based on the content of the PDF, ensuring you get the information you need.

• User: "What are the key findings in this report?"
• AI: "The report indicates elevated cholesterol levels and recommends further testing."

We welcome contributions to MediSight.AI! If you'd like to help, please follow these steps:

1. Fork the Repository: Click on the "Fork" button at the top right of the page.
2. Create a New Branch:

   git checkout -b feature/YourFeatureName

3. Make Your Changes: Implement your feature or fix.
4. Commit Your Changes:

   git commit -m"Add your message here"

5. Push to Your Fork:

   git push origin feature/YourFeatureName

6. Open a Pull Request: Go to the original repository and click "New Pull Request".

This project is licensed under the MIT License. See theLICENSE file for details.

For any questions or feedback, feel free to reach out:

• Email:your-email@example.com
• GitHub:natharmatron

To access the latest releases of MediSight.AI, visit ourReleases section. Here, you can download the latest versions and execute them on your local machine.

MediSight.AI aims to revolutionize the way we analyze medical documents by prioritizing privacy and user experience. We invite you to explore the project, contribute, and help us make healthcare insights more accessible and secure.

Thank you for your interest in MediSight.AI! Your feedback and contributions are highly appreciated.