Update README.md

README.md

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-# TinyML Heart Health Monitoring Dashboard
-
-A production-ready application for real-time heart health analytics, ensemble prediction modeling, and automated C-code transpilation for resource-constrained embedded systems.
-
-**Live Environments:**
-- 🖥️ **Frontend UI:** [Streamlit Cloud](https://tinyml-heart-health-monitoring-dashboard-8xqogy2hibtlayt7popvs.streamlit.app/)
-- ⚙️ **Backend API:** [Hugging Face Spaces](https://huggingface.co/spaces/IDKHowToCodeFr/tinyml-backend)
-
-## Overview
-
-This application bridges the gap between high-level cloud machine learning and low-level embedded hardware. It evaluates patient vitals using an ensemble of classifiers, provides strict clinical explainability, and compresses these Python-trained models into raw C-code ready to be flashed onto IoT devices.
-
-### Key Engineering Features
-
-- **Edge Computing & TinyML**: Transpiles complex Scikit-Learn models into highly optimized, dependency-free **C-code headers**. This allows predictive intelligence to be deployed directly to the edge, enabling offline, ultra-low latency, and privacy-preserving inference.
-- **INT8 Quantization Engine**: Mathematically scales 64-bit floating-point weights down to 8-bit integers, effectively shrinking the flash memory payload by **~75%** for resource-constrained microcontrollers.
-- **Dynamic Hardware Profiling**: The deployment suite actively simulates target hardware (e.g., *ESP32, Arduino Nano 33 BLE, Raspberry Pi Pico*) to calculate expected inference latency metrics (in µs) and final byte-size footprints prior to flashing.
-- **Interpretable AI (SHAP)**: Solves the medical "black box" concern by generating real-time SHAP feature impact analyses. This visualization proves exactly *why* the AI assigned a specific risk score based on individual patient variables.
-- **Soft-Voting Ensemble**: Rather than relying on a single algorithm, the backend aggregates outputs across five independent models (Random Forest, SVM, KNN, Logistic Regression, Neural Network) to output the highest-confidence predictions.
-
-## Architecture
-
-The project follows a decoupled, modular design pattern separating the client interface from the ML processing pipeline.
-
-```mermaid
-flowchart TD
-    %% Define styles
-    classDef frontend fill:#ff4b4b,stroke:#a30000,stroke-width:2px,color:#fff;
-    classDef backend fill:#009688,stroke:#004d40,stroke-width:2px,color:#fff;
-    classDef ml_node fill:#f2c94c,stroke:#b28900,stroke-width:2px,color:#333;
-    classDef edge_tech fill:#2d9cdb,stroke:#106093,stroke-width:2px,color:#fff;
-    classDef db fill:#9b59b6,stroke:#4a235a,stroke-width:2px,color:#fff;
-
-    %% Core Components
-    UI[🖥️ Streamlit Frontend<br/>Interactive Dashboard]:::frontend
-    API[⚙️ FastAPI Backend<br/>Routing & API Logic]:::backend
-    DB[(🗄️ SQLite DB<br/>Patient History)]:::db
-
-    UI <-->|REST API JSON| API
-    API <-->|Read / Write| DB
-
-    %% ML Subgraph
-    subgraph ML [🧠 Machine Learning & Inference Pipeline]
-        ENS{Ensemble Engine<br/>Soft-Voting}:::ml_node
-        Models[Classifiers:<br/>RF, SVM, LogReg, NN, KNN]:::ml_node
-        SHAP[🔍 SHAP Explainer]:::ml_node
-    end
-
-    %% Edge Quantization Subgraph
-    subgraph Quantization [⚡ Hardware Export]
-        TRANS[C-Code Transpiler<br/>INT8 Quantization]:::edge_tech
-        HEADER((tinyml_model.h)):::edge_tech
-        MCU>ESP32 / Cortex-M Node]:::edge_tech
-    end
-
-    API -->|Predict Request| ENS
-    ENS --> Models
-    API -->|Explain Request| SHAP
-    SHAP -.-> Models
-    API -->|Export Request| TRANS
-    TRANS --> HEADER
-    HEADER -->|Flash Firmware| MCU
-```
-
-### Modular Repository Structure
-
-```text
-TinyML-Dashboard/
-├── backend/               # FastAPI server, ML pipelines, SQLite API, C-code logic
-├── frontend/              # Streamlit UI mapping, Plotly charts, web routing
-├── model/                 # Local cache of serialized SciKit-Learn .pkl weights
-├── data/                  # Patient cohort CSVs for MLOps retraining pipelines
-├── tests/                 # Full PyTest suite for backend continuous integration
-├── .github/workflows/     # CI/CD actions for auto-syncing to Hugging Face
-└── docker-compose.yml     # Orchestration spec for local containerized deployment
-```
-
-## Setup & Installation
-
-### Requirements
-- Python 3.9 or higher
-- Git
-
-### Installation Steps
-
-1. **Clone the repository:**
-```bash
-git clone https://github.com/IDKHowToCodeFR/TinyML-Heart-Health-Monitoring-Dashboard.git
-cd TinyML-Heart-Health-Monitoring-Dashboard
-```
-
-2. **Install global requirements:**
-```bash
-pip install -r requirements.txt
-pip install -r backend/requirements.txt
-pip install -r frontend/requirements.txt
-```
-
-3. **Train the baseline local `.pkl` models (Required on first run):**
-```bash
-cd backend
-python models.py 
-cd ..
-```
-
-## Usage
-
-### 🚀 Quick Start (Windows)
-We've bundled an automated startup script that boots both environments natively:
-```bash
-run.bat
-```
-
-### ���� Docker Deployment
-To avoid local python pathing issues, you can spin up the application via Docker Compose:
-```bash
-docker-compose up --build
-```
-*The Streamlit interface will bind automatically to `http://localhost:8501`, connecting directly to the internal API.*
-
-### 🛠️ Manual Startup
-If you prefer running the stack manually in separate terminal instances:
-
-**Start the Backend:**
-```bash
-cd backend
-uvicorn main:app --host 0.0.0.0 --port 8000
-```
-**Start the Frontend:**
-```bash
-cd frontend
-streamlit run Home.py
-```
-
-## Development & Automation
-
-### API Documentation
-FastAPI serves auto-generated interactive documentation. While the backend is running, you can hit the endpoints directly at:
-- **Swagger UI:** `http://localhost:8000/docs`
-- **ReDoc:** `http://localhost:8000/redoc`
-
-### CI/CD Pipelines
-The project utilizes GitHub actions natively located in `.github/workflows/`:
-1. **PyTest Validation**: Verifies endpoint data sanitization and schema parameters immediately on Push/Pull Requests.
-2. **Hugging Face Sync**: Auto-deploys `main` branch updates precisely to the Hugging Face Space Docker container ensuring CI/CD alignment.
-
-## Support & License
-This project is licensed under the MIT License. Designed as a scalable, educational, and fully interpretable Edge-based Machine Learning architecture.
+ ---
+   title: Tinyml Backend
+   emoji: 🫀
+   colorFrom: green
+   colorTo: blue
+   sdk: docker
+   pinned: false
+   ---