Create Your AI-Powered Health Dashboard with Falcon 180B

Developing an AI-Powered Personal Health Dashboard

Welcome to an exciting guide on creating your very own Personal Health Dashboard leveraging the power of the Falcon 180B language model! At the forefront of artificial intelligence and healthcare, this tutorial will walk you through the creation of a personal health management tool that analyzes medical reports to provide actionable insights tailored to your needs.

Introduction to Falcon 180B

This remarkable model is developed by the Technology Innovation Institute (TII) and is an open-source language model featuring a staggering 180 billion parameters. Its extensive training dataset, encompassing around 3.5 trillion tokens, puts it on par with top models like GPT-3.5 and GPT-4. With such capabilities, Falcon 180B is exceptionally adept at navigating the complexities of medical report analysis.

Project Overview

The Personal Health Dashboard serves as your digital health companion. Its primary function is to process medical reports, analyze health metrics, and suggest personalized health recommendations based on the analyzed data.

• Data Parsing: Upon report upload, Falcon 180B will interpret the content of the document, extracting essential health indicators and identifying relevant trends.
• Visual Presentation: Health metrics will be displayed in an intuitive format, allowing users to quickly gain insights into their health.
• Longitudinal Tracking: Users can track their health indicators over time, assessing the impacts of lifestyle changes or medical interventions.
• Personalized Recommendations: The AI will generate tailored health tips, reminders for tests, and alerts for potential health risks.

Setting Up Your Development Environment

Before diving into coding, it is crucial to configure our development environment. We will need Python for backend development and Node.js for frontend work.

Prerequisites

• Install Python and Node.js.
• Clone the project repository from GitHub.

Backend Development with FastAPI

Using FastAPI, we will build our application's backend responsible for handling file uploads, processing medical reports, and interfacing with the AI model.

Core Components of main.py

• Imports & Setup: Essential libraries are imported, including fastapi, pdf processing tools, and logging for troubleshooting purposes.
• CORS Configuration: Ensures the frontend running on a different port can communicate with the backend.
• API Setup: Connects with the Falcon 180B model API for analysis requests.
• File Upload: Robust handling of uploaded PDF files, with checks for file type and size.

Medical Report Analysis

This section focuses on how we leverage the Falcon 180B model to extract insights from user-submitted reports. The aim is to present coherent results that users can easily understand and act upon.

The Importance of Dynamic Prompts

Our prompt structure allows flexibility, adapting across various types of medical data without needing code changes. The AI can provide context-aware insights and tailor recommendations unique to each user's health profile.

Running the Application

To see our work come to life, we will guide you through running the application using Uvicorn as the server:

1. Start your backend by navigating to the root project directory and running python main.py.
2. Start your frontend by navigating to the my-app directory and executing npm run dev.
3. Access your application at http://localhost:3000.

Conclusion

You have built an innovative AI-powered Personal Health Dashboard! This project empowers users to engage with their health data on a personalized level, utilizing modern AI technology in conjunction with robust web development practices. Consider expanding this project further with user authentication, data persistence, or enhanced visualizations to make it even more robust.

Have fun in exploring the intricacies of health technology innovation!