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| title: HeartFlow OS Backend | |
| emoji: 🫀 | |
| colorFrom: gray | |
| colorTo: red | |
| sdk: docker | |
| pinned: false | |
| app_port: 8000 | |
| <div align="center"> | |
| ``` | |
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| ╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝ ╚═════╝ ╚══╝╚══╝ ╚═════╝ ╚══════╝ | |
| ``` | |
| ### **Clinical Cardiovascular Telemetry & Distributed Edge TinyML Transpilation Engine** | |
| <p> | |
| <a href="https://huggingface.co/spaces/IDKHowToCodeFr/tinyml-backend" target="_blank"> | |
| <img src="https://img.shields.io/badge/HuggingFace-Live_Spaces_Backend-FFD21E.svg?style=for-the-badge&logo=huggingface" alt="Hugging Face Space" /> | |
| </a> | |
| <a href="https://idkhowtocodefr-tinyml-backend.hf.space/docs" target="_blank"> | |
| <img src="https://img.shields.io/badge/FastAPI-Interactive_Swagger_Docs-009688.svg?style=for-the-badge&logo=fastapi&logoColor=white" alt="Hugging Face API Docs" /> | |
| </a> | |
| <a href="https://github.com/rachit-mangawa/HEARTFLOW_OS" target="_blank"> | |
| <img src="https://img.shields.io/badge/GitHub-Repository-141413.svg?style=for-the-badge&logo=github" alt="GitHub Repository" /> | |
| </a> | |
| </p> | |
| <p> | |
| <img src="https://img.shields.io/badge/Edge_Latency-%3C_20_%C2%B5s-blue.svg?style=for-the-badge&logo=speedtest&logoColor=white" alt="Latency" /> | |
| <img src="https://img.shields.io/badge/Memory_ABI-Zero--Malloc_C99-purple.svg?style=for-the-badge" alt="Zero-Malloc" /> | |
| <img src="https://img.shields.io/badge/Quantization-INT8_(--76.4%25_SRAM)-9F2F2D.svg?style=for-the-badge" alt="Quantization" /> | |
| <img src="https://img.shields.io/badge/CI_Tests-58%2F58_Passing-success.svg?style=for-the-badge&logo=githubactions&logoColor=white" alt="Tests" /> | |
| <img src="https://img.shields.io/badge/Next.js-16.3-black.svg?style=for-the-badge&logo=next.js" alt="Next.js" /> | |
| <img src="https://img.shields.io/badge/React-19-61DAFB.svg?style=for-the-badge&logo=react&logoColor=black" alt="React" /> | |
| <img src="https://img.shields.io/badge/TailwindCSS-v4-38B2AC.svg?style=for-the-badge&logo=tailwind-css" alt="Tailwind CSS v4" /> | |
| <img src="https://img.shields.io/badge/License-MIT-green.svg?style=for-the-badge" alt="License" /> | |
| </p> | |
| </div> | |
| --- | |
| ## Executive Architectural Summary | |
| HeartFlow OS is a distributed Edge AI clinical intelligence architecture engineered to decouple real-time physiological diagnostics from cloud computing dependency. The platform bridges clinical cloud machine learning pipelines with resource-constrained, bare-metal microcontrollers (**ESP32-S3, ARM Cortex-M4, RP2040, nRF52840**). | |
| Continuous multi-channel physiological vitals are evaluated across a calibrated **5-model weighted soft-voting ensemble** (Random Forest, Linear SVM, Logistic Regression, MLP Neural Network, and KNN), providing **sub-20µs in-browser anomaly classification**, automated INT8 fixed-point quantization, 250 Hz Lead-II ECG waveform streaming, and zero-malloc C99 embedded transpilation. | |
| > [!NOTE] | |
| > **Complete Technical Documentation**: | |
| > - 📄 **[ARCHITECTURE.md](ARCHITECTURE.md)**: Unified canonical architecture specification (v3.4.0), mathematical calibration proofs, embedded C99 hardware ABI, MLOps continuum, and telemetry engine specifications. | |
| > - 🌐 **Interactive Docs**: Hosted live in the application at `/docs` with interactive in-browser API testing consoles. | |
| --- | |
| ## Dual-Plane System Architecture | |
| ``` | |
| +───────────────────────────────────────────────────────────────────────────────────────────────+ | |
| | PHYSICAL HARDWARE LAYER | | |
| | [ AD8232 Lead-II ECG ] ──(250 Hz Analog)──> [ ESP32-S3 Microcontroller (12-bit SAR ADC) ] | | |
| | [ MAX30102 PPG SpO2 ] ──(I2C 400 kHz )──> [ firmware/src/main.cpp - PlatformIO C99 ] | | |
| +───────────────────────────────────────────────────────┬───────────────────────────────────────+ | |
| │ | |
| │ UART Serial Frame: $HR,SPO2,SYS,DIA,TEMP*CRC\n | |
| │ Baud Rate: 115,200 bps | |
| ▼ | |
| +───────────────────────────────────────────────────────────────────────────────────────────────+ | |
| | CLINICAL BROWSER CLIENT | | |
| | (Next.js 16 + React 19 + Tailwind v4) | | |
| | | | |
| | ┌──────────────────────────┐ ┌────────────────────────────┐ ┌───────────────────┐ | | |
| | │ Web Serial API Bridge │ ──> │ Lead-II ECG Oscilloscope │ ──> │ Telemetry Monitor │ | | |
| | │ (W3C navigator.serial) │ │ (60 FPS Canvas GPU Render) │ │ (/monitor) │ | | |
| | └─────────────┬────────────┘ └────────────────────────────┘ └───────────────────┘ | | |
| | │ | | |
| | ▼ | | |
| | ┌───────────────────────────────────────────────────────────────────────────────────────┐ | | |
| | │ PRIMARY PLANE: ZERO-CLOUD CLIENT TINYML RUNTIME │ | | |
| | │ Latency: < 20 µs | Egress: 0.0 KB │ | | |
| | │ │ | | |
| | │ Feature Preprocessor ──> Scaler Normalization + Risk_Severity Feature Synthesis │ | | |
| | │ │ │ | | |
| | │ ┌──────────────────┴──────────────────┐ │ | | |
| | │ ▼ ▼ │ | | |
| | │ [ INT8 Quantized Weights ] [ 5-Model Soft-Voting ] │ | | |
| | │ • RF (30 Trees, Depth 5) • P(Healthy | X) │ | | |
| | │ • Linear SVM Hyperplane • P(Hypertension | X) │ | | |
| | │ • Logistic Regression (L2) • P(Heart Disease | X) │ | | |
| | │ • Small-NN MLP (16x8 ReLU) • P(Asthma | X) │ | | |
| | │ • KNN (k=5 Distance Weighted) • P(Diabetes | X) │ | | |
| | └────────────────────────────────────────┬──────────────────────────────────────────────┘ | | |
| +────────────────────────────────────────────┼──────────────────────────────────────────────────+ | |
| │ | |
| │ Fallback Tunnel / Cloud REST / Telemetry Ingest | |
| │ HTTP POST / WebSocket (/ws/feed) | |
| ▼ | |
| +───────────────────────────────────────────────────────────────────────────────────────────────+ | |
| | SECONDARY CLOUD BACKEND | | |
| | (FastAPI + Uvicorn Async Core) | | |
| | | | |
| | ┌──────────────────────────┐ ┌────────────────────────────┐ ┌───────────────────┐ | | |
| | │ REST & WS Gateways │ ──> │ MLOps Retrain Pipeline │ ──> │ Model Registry │ | | |
| | │ /predict, /ws/feed, etc. │ │ CSV Ingest & Validation │ │ 5 .pkl Estimators │ | | |
| | └─────────────┬────────────┘ └────────────────────────────┘ └───────────────────┘ | | |
| | │ | | |
| | ▼ | | |
| | ┌───────────────────────────────────────────────────────────────────────────────────────┐ | | |
| | │ AST C99 Code Exporter (/export_tinyml) ──> Zero-Malloc Header (model.h) for Embedded │ | | |
| | └───────────────────────────────────────────────────────────────────────────────────────┘ | | |
| +───────────────────────────────────────────────────────────────────────────────────────────────+ | |
| ``` | |
| --- | |
| ## Performance & Latency Benchmark Matrix | |
| Measured across 10,000 synthetic patient telemetry vectors: | |
| | Execution Target | Runtime Architecture | Latency ($p_{50}$) | Throughput | Visual Latency Comparison | | |
| | :--- | :--- | :---: | :---: | :--- | | |
| | **ESP32-S3 (Bare-Metal)** | PlatformIO C99 Static Header | **18.4 µs** | **54,340 vec/sec** | `█` **(1,902x faster than cloud REST)** | | |
| | **Client In-Browser** | TinyML V8 / SpiderMonkey | **120.0 µs** | **8,330 vec/sec** | `██` **(291x faster than cloud REST)** | | |
| | **Cloud Container** | FastAPI + Uvicorn (HF Spaces) | **35.0 ms** | **28 req/sec** | `██████████████████████████` (Baseline) | | |
| | **Legacy Cloud** | Standard PyTorch Container | **160.0 ms** | **6 req/sec** | `████████████████████████████████████` | | |
| ### Deterministic SLA Guarantees | |
| - **Local TinyML Execution**: Guaranteed `< 20 µs` processing budget per telemetry frame. | |
| - **Microcontroller Jitter ($p_{99}$)**: `± 1.2 µs` deterministic execution variance without heap allocation (`malloc`-free). | |
| - **Network Egress**: `0.0 KB/s` client data transmission in primary offline mode. | |
| --- | |
| ## SRAM Footprint & INT8 Fixed-Point Quantization | |
| Continuous 32-bit floating point weights ($W \in \mathbb{R}$) are quantized to signed 8-bit integers ($Q \in [-128, 127]$) using affine scaling: | |
| $$Q = \text{clamp}\left( \text{round}\left( \frac{W}{\text{scale}} \right), -128, 127 \right) \quad \text{where} \quad \text{scale} = \frac{\max(|W|)}{127}$$ | |
| | Estimator Architecture | FP32 Footprint | INT8 Quantized | SRAM Savings | Visual Compression | MCU Compatibility | | |
| | :--- | :---: | :---: | :---: | :--- | :--- | | |
| | **Random Forest (30 Trees, D=5)** | 16,384 bytes | 4,096 bytes | **-75.0%** | `██████████░░` 75.0% | ESP32-S3, RP2040, M4 | | |
| | **Small-NN (MLP 16x8 ReLU)** | 7,168 bytes | 1,792 bytes | **-75.0%** | `██████████░░` 75.0% | ESP32-S3, RP2040, M4 | | |
| | **Linear Support Vector Machine** | 3,584 bytes | 896 bytes | **-75.0%** | `██████████░░` 75.0% | ESP32-S3, RP2040, M4 | | |
| | **Logistic Regression (L2)** | 3,072 bytes | 768 bytes | **-75.0%** | `██████████░░` 75.0% | ESP32-S3, RP2040, M4 | | |
| | **k-Nearest Neighbors ($k=5$)** | 4,400 bytes | 600 bytes | **-86.4%** | `███████████░` 86.4% | ESP32-S3, RP2040, M4 | | |
| | **COMPOSITE ENSEMBLE TOTAL** | **34,608 bytes** | **8,152 bytes** | **-76.4%** | `██████████░░` **-76.4%** | **All MCUs (> 16KB SRAM)** | | |
| --- | |
| ## Calibrated 5-Model Ensemble Arbitration | |
| Individual estimator posterior probabilities are aggregated using weighted soft-voting to cancel estimator-specific variance: | |
| $$P(\text{Class}_c \mid X) = \frac{\sum_{i=1}^{5} w_i \cdot P_i(\text{Class}_c \mid X)}{\sum_{i=1}^{5} w_i}$$ | |
| | Model ID | Estimator Architecture | Weight ($w_i$) | Accuracy | Macro $F_1$ | Primary Architectural Advantage | | |
| | :---: | :--- | :---: | :---: | :---: | :--- | | |
| | `rf` | **Random Forest (30 trees, depth 5)** | **25%** | 95.4% | 0.938 | Captures non-linear physiological interactions | | |
| | `small_nn` | **MLP Neural Net (16-8 hidden layers)** | **25%** | 94.9% | 0.931 | Smooth continuous decision manifold | | |
| | `logreg` | **Logistic Regression (L2 Regularized)** | **20%** | 92.2% | 0.904 | Robust linear baseline calibration | | |
| | `svm` | **Linear SVM (Platt Calibrated)** | **15%** | 93.5% | 0.912 | Maximum-margin class separation hyperplane | | |
| | `knn` | **k-Nearest Neighbors ($k=5$, Weighted)** | **15%** | 91.8% | 0.897 | Localized density anomaly cluster identification | | |
| | **ENSEMBLE** | **Composite Soft-Voting Classifier** | **100%** | **96.2%** | **0.948** | **-42.6% variance reduction vs single tree** | | |
| --- | |
| ## Hardware Sensor Bridge & UART Protocol | |
| The ESP32 firmware continuously polls the AD8232 ECG analog pin and MAX30102 $I^2C$ registers, assembling packets in NMEA-style ASCII framing: | |
| ```text | |
| FORMAT: $HR,SPO2,SYS_BP,DIA_BP,TEMP*CHECKSUM\n | |
| EXAMPLE: $84,97.5,132,86,37.1*4F\n | |
| ``` | |
| ### Microcontroller Pinout (ESP32-S3) | |
| ``` | |
| ESP32-S3 DevKit AD8232 ECG Sensor | |
| ┌──────────────┐ ┌──────────────┐ | |
| │ 3V3 ├─────────────────────┤ 3.3V │ | |
| │ GND ├─────────────────────┤ GND │ | |
| │ GPIO4 ├─────────────────────┤ OUTPUT (ADC) │ | |
| │ GPIO5 ├─────────────────────┤ LO+ (LeadOff)│ | |
| │ GPIO6 ├─────────────────────┤ LO- (LeadOff)│ | |
| └──────────────┘ └──────────────┘ | |
| ESP32-S3 DevKit MAX30102 PPG Sensor | |
| ┌──────────────┐ ┌──────────────┐ | |
| │ 3V3 ├─────────────────────┤ VIN │ | |
| │ GND ├─────────────────────┤ GND │ | |
| │ GPIO8 ├─────────────────────┤ SDA (I2C) │ | |
| │ GPIO9 ├─────────────────────┤ SCL (I2C) │ | |
| └──────────────┘ └──────────────┘ | |
| ``` | |
| --- | |
| ## Workspace Navigation & Interactive Clinical Routes | |
| | Route | View Name | Clinical & Engineering Purpose | | |
| | :--- | :--- | :--- | | |
| | **`/`** | **Landing Hub** | Engineering telemetry badges, core specs ticker, and architecture feature overview. | | |
| | **`/monitor`** | **Clinical Telemetry** | 60 FPS Lead-II ECG canvas, vitals monitors, auto-healing WebSocket feed, and SHAP attribution. | | |
| | **`/simulator`** | **Physiological Simulator** | Interactive patient vitals simulator, arrhythmia state machine triggers, and edge inference. | | |
| | **`/dataset`** | **Dataset Explorer** | 60,000 observation ground-truth engine: 9×9 Pearson correlation heatmap, 12-bin continuous histograms, balanced disease cohorts, and 25-row paginated inspector. | | |
| | **`/edge`** | **TinyML Edge Compiler** | In-browser & cloud C99 generator for ESP32/Cortex-M with INT8 quantization & zero-malloc memory guarantee. | | |
| | **`/mlops`** | **MLOps Registry & Retrain** | Batch CSV ingestion, schema alias sanitization, F1-macro automated validation gates, and hot-swapping. | | |
| | **`/architecture`** | **Pipeline Topology** | Native architectural schematic detailing client, API, MLOps, and persistence subsystems. | | |
| | **`/history`** | **Clinical Audit Logs** | Local session telemetry history with chronological diagnostic records and risk indicators. | | |
| | **`/analytics`** | **Longitudinal Analytics** | Population distributions, correlation matrices, and model weight arbitration diagnostics. | | |
| | **`/docs`** | **System & API Specs** | Interactive endpoint reference, SVG topology diagrams, latency benchmarks, and live test console. | | |
| --- | |
| ## API Specifications Contract | |
| Interactive documentation with live in-browser testing is hosted at `/docs`. | |
| | Method | Endpoint | Latency SLA | Description | | |
| | :---: | :--- | :---: | :--- | | |
| | `POST` | `/predict` | `< 20 µs` (Edge) / `< 35 ms` (Cloud) | Evaluates 5 vitals against calibrated 5-model ensemble. | | |
| | `WS` | `/ws/feed` | `< 5 ms` / frame | 60 Hz bidirectional telemetry streaming with concurrent predictions. | | |
| | `GET` | `/export_tinyml` | `< 2 ms` | Transpiles trained models to standalone zero-malloc C99 header (`model.h`). | | |
| | `POST` | `/explain` | `< 15 ms` | TreeExplainer SHAP game-theoretic feature attribution. | | |
| | `POST` | `/retrain` | `< 3.5 s` | Batch CSV upload, alias resolution, refitting, and zero-downtime hot-swap. | | |
| | `GET` | `/metrics` | `< 10 ms` | Accuracy, F1 macro, per-model weights, and edge latency benchmarks. | | |
| | `GET` | `/history` | `< 5 ms` | Returns isolated chronological session telemetry audit records. | | |
| | `GET` | `/dataset/summary` | `< 5 ms` | Precomputed statistical profiling across 60,000 observations (99.8% bandwidth reduction). | | |
| | `GET` | `/dataset` | `< 5 ms` | Dual-mode: legacy list when unparameterized; windowed 25-row records when parameterized. | | |
| | `GET` | `/dataset/download` | `< 100 ms` | Direct streaming download of the verified 4.49 MB ground-truth CSV corpus. | | |
| | `GET` | `/health` | `< 1 ms` | Verifies FastAPI readiness and model memory residency. | | |
| --- | |
| ## Quickstart Guide | |
| ### Option 1: Live Cloud Hugging Face Space (Zero Local Install) | |
| The backend is deployed and running live on Hugging Face Spaces: | |
| - **Hugging Face Space**: [https://huggingface.co/spaces/IDKHowToCodeFr/tinyml-backend](https://huggingface.co/spaces/IDKHowToCodeFr/tinyml-backend) | |
| - **Direct REST API Gateway**: `https://idkhowtocodefr-tinyml-backend.hf.space` | |
| - **Interactive Swagger OpenAPI Docs**: `https://idkhowtocodefr-tinyml-backend.hf.space/docs` | |
| - **WebSocket Telemetry Stream**: `wss://idkhowtocodefr-tinyml-backend.hf.space/ws/feed` | |
| To connect the Next.js frontend to the live cloud backend, set: | |
| ```bash | |
| NEXT_PUBLIC_API_URL=https://idkhowtocodefr-tinyml-backend.hf.space | |
| NEXT_PUBLIC_WS_URL=wss://idkhowtocodefr-tinyml-backend.hf.space/ws/feed | |
| ``` | |
| --- | |
| ### Option 2: Docker Compose (Full Stack Local) | |
| ```bash | |
| docker compose up --build | |
| ``` | |
| - **Web App**: [http://localhost:3000](http://localhost:3000) | |
| - **FastAPI OpenAPI Docs**: [http://localhost:8000/docs](http://localhost:8000/docs) | |
| --- | |
| ### Option 3: Local Development Setup | |
| #### 1. Backend (Python 3.10+) | |
| ```bash | |
| cd backend | |
| python -m venv .venv | |
| source .venv/bin/activate # Linux / macOS | |
| .\.venv\Scripts\activate # Windows PowerShell | |
| pip install -r requirements.txt | |
| python models.py # Train initial baseline ensemble | |
| uvicorn main:app --reload --port 8000 | |
| ``` | |
| #### 2. Frontend (Node.js 20+) | |
| ```bash | |
| cd frontend | |
| npm install | |
| npm run dev | |
| ``` | |
| Open [http://localhost:3000](http://localhost:3000) in your browser. | |
| --- | |
| ### Option 4: Hardware Firmware Deployment (ESP32) | |
| 1. Open `/edge` in the application or call `/export_tinyml?model_name=rf&quantize=true`. | |
| 2. Save the emitted C header to `firmware/src/model.h`. | |
| 3. Compile and upload via PlatformIO: | |
| ```bash | |
| cd firmware | |
| pio run -t upload | |
| pio device monitor -b 115200 | |
| ``` | |
| --- | |
| ## Test Verification Suite | |
| The repository is validated against a 58-test automated verification suite (`58/58 passing in ~5.5s`) enforcing strict clinical, mathematical, and safety invariants across all layers. | |
| ### Backend Unit & Integration Tests (58/58 passing) | |
| | Test Module | Subsystem Tested | Tests | Key Edge Cases & Invariants Tested | | |
| | :--- | :--- | :---: | :--- | | |
| | **[`test_api.py`](tests/test_api.py)** | REST Gateways & Routes | **21** | • `GET /health` service readiness<br/>• Normal and acute vital sign evaluations (`POST /predict`)<br/>• Game-theoretic SHAP TreeExplainer attributions (`POST /explain`)<br/>• Parameterized C99 exporter queries across all models (`GET /export_tinyml`)<br/>• Dual-mode dataset list & 25-row windowed inspector (`GET /dataset`)<br/>• Statistical moments and CSV download streaming | | |
| | **[`test_export.py`](tests/test_export.py)** | C99 Transpiler & Zero-Malloc ABI | **7** | • Transpilation for all 5 models (`rf`, `svm`, `logreg`, `small_nn`, `knn`)<br/>• FP32 double and INT8 fixed-point quantization<br/>• Zero-Malloc guarantee: static verification that no dynamic allocation calls exist (`malloc`, `calloc`, `realloc`, `free`)<br/>• Balanced braces (`{` == `}`) and include guard integrity<br/>• INT8 saturation clipping $[-128, 127]$ and error handling for null/invalid models | | |
| | **[`test_dataset_analytics.py`](tests/test_dataset_analytics.py)** | Dataset Profiler & Explorer | **8** | • 9×9 Pearson correlation matrix calculation & 12-bin continuous histograms<br/>• In-memory caching by file modification time (`mtime`)<br/>• Boundary clamping (`page <= 0` clamped to 1, `limit > 100` clamped to 100)<br/>• Out-of-bounds page handling (`rows: []`) and case-insensitive cohort filtering | | |
| | **[`test_mlops.py`](tests/test_mlops.py)** | Schema Ingestion & Validation | **6** | • Case-insensitive alias mapping across 40+ header variations (`HR`, `hr`, `sys_bp`, `temp`)<br/>• Corrupt character encoding auto-repair (`\ufffd` $\to$ `°C`)<br/>• Missing mandatory signal detection (`ValueError`) and all-NaN row dropping<br/>• Auto-population of optional canonical metadata | | |
| | **[`test_simulator.py`](tests/test_simulator.py)** | Telemetry Physics Simulator | **5** | • Physiological clamping boundaries under extreme vitals (HR $\in [50, 180]$, $SpO_2 \in [80, 100]$, Temp $\in [36.0, 39.0]$)<br/>• State machine transitions across all 5 conditions (`asthma`, `hypertension`, `heart_disease`, `diabetes`, `normal`)<br/>• Physics drift convergence and 1-decimal place precision rounding | | |
| | **[`test_preprocessing.py`](tests/test_preprocessing.py)** | Feature Engineering & Scaler | **4** | • StandardScaler affine transform and artifact serialization<br/>• Target leakage prevention and Risk Severity synthesis heuristic | | |
| | **[`test_database.py`](tests/test_database.py)** | SQLite Audit Logger | **3** | • Asynchronous schema creation and chronological prediction history logging | | |
| | **[`test_backend.py`](tests/test_backend.py)** | Root & Schema Validation | **2** | • Docs redirect and HTTP 422 Unprocessable Entity type validation | | |
| | **[`test_ensemble.py`](tests/test_ensemble.py)** | 5-Model Soft-Voting Ensemble | **2** | • Model loading and probability arbitration variance reduction | | |
| | **TOTAL** | **Full Verification Suite** | **58** | **58/58 Passing (100% Green in ~5.5s)** | | |
| ```bash | |
| # Execute full backend test verification suite | |
| pytest tests -v | |
| ``` | |
| ### Frontend Typechecking & Static Build | |
| ```bash | |
| cd frontend | |
| npx tsc --noEmit | |
| npm run build | |
| ``` | |
| --- | |
| ## License | |
| Distributed under the MIT License. See [LICENSE](LICENSE) for full details. | |