Here's a temporary README.md file for your project based on the folder structure in the screenshot and the provided tech stack (FastAPI, ReactJS, PostgreSQL, Streamlit):

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# 🧠 Multi-Disease Prediction System

This is a full-stack web application that enables users to upload data (images or form inputs) and receive predictions for various medical conditions using machine learning models.

## 🚀 Tech Stack

- **Backend:** FastAPI
- **Frontend:** ReactJS
- **Database:** PostgreSQL
- **ML App Interface:** Streamlit

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## 🗂️ Project Structure

backend/ │ ├── data/ # Sample or user-uploaded data │ ├── alzheimer-mri/ │ ├── braintumor-mri/ │ ├── heart/ │ └── kidney/ │ ├── models/ # Trained ML models │ ├── preprocessing/ # Input preprocessing logic │ ├── testing/ # Model inference logic │ ├── venv/ # Python virtual environment │ ├── app.py # FastAPI entry point ├── requirements.txt # Python dependencies ├── runtime.txt # Deployment config │ frontend/ # ReactJS frontend │ .gitignore README.md

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## ✅ Features

- 🔍 Predicts:
  - Alzheimer's from MRI scans
  - Brain Tumor from MRI scans
  - Heart Disease from clinical data
  - Kidney Disease from test parameters
- 🔐 User login & history tracking
- 🧠 AI-based chatbot integration (planned)
- 📈 Streamlit dashboards for quick visualization

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## 🛠️ Setup Instructions

### Backend (FastAPI)

```bash
cd backend
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt
uvicorn app:app --reload

Frontend (ReactJS)

cd frontend
npm install
npm start

Streamlit (ML Prediction UI)

streamlit run path/to/streamlit_app.py

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🗃️ Database

Make sure to have PostgreSQL installed and running.

CREATE DATABASE medical_predictions;

Configure DB connection in .env or directly in your FastAPI settings.

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