AI-Powered Personal Health Dashboard with Falcon 180B Tutorial

Developing an AI-Powered Personal Health Dashboard

In the era of digital health management, leveraging artificial intelligence to analyze personal health data is becoming increasingly crucial. This article outlines how to create an AI-powered Personal Health Dashboard that harnesses the advanced capabilities of the Falcon 180B language model.

Introduction

Welcome, I'm Sanchay Thalnerkar. In this guide, we're going to construct a Personal Health Dashboard that allows users to analyze their medical reports and obtain actionable health insights through AI. The Falcon 180B, developed by the Technology Innovation Institute (TII), is a significant player in this domain, boasting 180 billion parameters and trained on around 3.5 trillion tokens, similar to GPT-4.

Project Overview

This dashboard serves individuals by simplifying interaction with medical data.
It can ingest and analyze medical reports and present health metrics in an understandable format.
Users can view trends over time, empowering them to track the impact of lifestyle changes.
The AI provides personalized health recommendations based on the analysis.

Setting Up Your Development Environment

Before building the dashboard, setup is needed:

Prerequisites: Ensure Python and Node.js are installed.
Cloning the Repository: Use Git to clone the project repo.

API Key Setup

Obtain your Falcon API key:

Visit the AI71 Marketplace and create an account.
Create a new API key and store it securely.

Guide to main.py: Backend Setup

The main.py file manages uploads and processes reports. It does the following:

Imports essential libraries including FastAPI for API development.
Configures CORS to allow cross-origin requests from the frontend.
Executes file upload and initial processing of medical reports.
Handles PDF content extraction using the PyPDF2 library.
Analyzes the report using the Falcon 180B model.

The Power of Dynamic Prompts

This dashboard's intelligent design uses dynamic prompts tailored for each report type:

Adaptability: Processes diverse data formats.
Personalization: Provides unique health insights.
Future-Proof: Easily integrates new metrics.

Running the Application

Activate the backend using python main.py.
Activate the frontend with npm run dev.
Your application is live at http://localhost:3000.

Conclusion

You've successfully built a Personal Health Dashboard that utilizes AI for health insights. Consider expanding the functionalities like:

User authentication for secure experiences.
Data persistence for tracking health trends.
Enhanced visualization capabilities.

This project illustrates the intersection of healthcare and technology, showcasing how AI can revolutionize health management.

Keywords for SEO

AI-powered health dashboard
Falcon 180B model
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Web development with FastAPI and Next.js