diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..fa5bdf456849d3d2e24418c0ddc3a71591f350d2 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +backend/patient_history.db filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore index 643a0691cd0d5c15e7e6d0282002f9adac87d710..fefe03cd73a37f50f1f43429b6af604c4963874c 100644 --- a/.gitignore +++ b/.gitignore @@ -34,6 +34,8 @@ venv/ ENV/ env.bak/ venv.bak/ +uv.lock +pyproject.toml # Streamlit .streamlit/credentials.toml @@ -44,3 +46,26 @@ venv.bak/ # macOS .DS_Store + +# next.js +node_modules +.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions +coverage +.next/ +out/ +build +*.pem +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* +.env* +.vercel +*.tsbuildinfo +next-env.d.ts diff --git a/ARCHITECTURE.md b/ARCHITECTURE.md new file mode 100644 index 0000000000000000000000000000000000000000..06f686de0130847b1d3d841ddff629ad177f513e --- /dev/null +++ b/ARCHITECTURE.md @@ -0,0 +1,98 @@ +# HEARTFLOW_OS // System Architecture + +## Overview +HeartFlow OS is an end-to-end, edge-optimized clinical telemetry pipeline. The system bridges the gap between high-level cloud machine learning and low-level embedded hardware, enabling real-time clinical inference and automated C-code transpilation for resource-constrained microcontrollers (TinyML). + +The architecture adheres to a decoupled, modular design pattern split into three primary planes: **Client (Frontend)**, **Core (Backend)**, and **Edge (Hardware Compilation)**. + +--- + +## Architecture Diagram + +```mermaid +graph TD + %% Define styles + classDef frontend fill:#111,stroke:#111,stroke-width:2px,color:#fff,font-family:monospace; + classDef backend fill:#fff,stroke:#111,stroke-width:2px,color:#111,font-family:monospace; + classDef ml_node fill:#e61919,stroke:#e61919,stroke-width:2px,color:#fff,font-family:monospace; + classDef edge_tech fill:#f3f3f2,stroke:#111,stroke-width:2px,color:#111,font-family:monospace; + classDef data fill:#94a3b8,stroke:#475569,stroke-width:2px,color:#fff,font-family:monospace; + + subgraph Client [Client Application] + UI[Next.js 16 UI / Dashboard]:::frontend + WS_Client[WebSocket Client]:::frontend + HTTP_Client[HTTP Client]:::frontend + end + + subgraph Core [FastAPI Backend] + WS_Server[WebSocket Feed / Generator]:::backend + REST_API[REST API Router]:::backend + Inference[Inference Engine / Preprocessor]:::backend + MLOps[MLOps Pipeline]:::backend + DB[(aiosqlite)]:::backend + end + + subgraph ML_Layer [Intelligence Pipeline] + Ensemble{Soft-Voting Ensemble}:::ml_node + Models[RF, SVM, KNN, LogReg, MLP]:::ml_node + SHAP[SHAP Explainer]:::ml_node + EdgeCompiler[INT8 C-Compiler AST]:::ml_node + end + + subgraph Hardware [Edge Target] + HEADER((tinyml_model.h)):::edge_tech + MCU[ESP32 / Cortex-M]:::edge_tech + end + + subgraph Storage [Persistent Storage] + ModelWeights[Trained Weights .pkl]:::data + CSV[Patient Dataset .csv]:::data + end + + %% Flow + UI -->|React 19 / Framer| WS_Client + UI -->|User Interaction| HTTP_Client + + WS_Client <-->|ws:// Live Telemetry| WS_Server + HTTP_Client -->|POST /retrain, GET /export| REST_API + + WS_Server -->|Evaluates Vitals| Inference + REST_API -->|Triggers Retrain| MLOps + REST_API -->|Requests Header| EdgeCompiler + + Inference -->|Predicts| Ensemble + Ensemble --> Models + Ensemble --> SHAP + + MLOps -->|Writes| ModelWeights + MLOps -->|Appends| CSV + EdgeCompiler -->|Reads| Models + Inference -->|Reads| ModelWeights + + EdgeCompiler -->|Transpiles| HEADER + HEADER -.->|Flashed to| MCU +``` + +--- + +## 1. Client Plane (Frontend) +Engineered using **Next.js 16 (App Router)** and **React 19**. +- **Design System:** Strict Industrial Brutalist aesthetic utilizing hard grids, monochromatic blueprints (`bg-canvas-cream`, `text-ink`), and high-contrast hazard states. +- **State Management:** React Context API manages high-frequency telemetry streams. +- **Data Transport:** Real-time data is ingested via native WebSockets (`ws://`), bypassing HTTP overhead for continuous vital monitoring. +- **Animations:** Hardware-accelerated SVG manipulations and layout transitions powered by `framer-motion`. + +## 2. Core Plane (Backend) +Built on **FastAPI** (Python 3.10+), maximizing asynchronous throughput. +- **WebSocket Generator:** A state-machine driven data simulator generates correlated, volatile telemetry streams (simulating clinical episodes like Asthma or Heart Disease). +- **Inference Preprocessor:** Live patient data is standardized and imputed using a persistent `SimpleImputer` and `StandardScaler` trained on the original dataset, ensuring inference dimensions perfectly match the training phase. +- **Storage:** Employs `aiosqlite` for non-blocking, asynchronous database writes to track historical patient classifications without stalling the event loop. + +## 3. Intelligence Pipeline (MLOps) +- **Ensemble Architecture:** Achieves clinical-grade precision via a 5-model soft-voting ensemble (`RandomForest`, `SVM`, `KNN`, `LogisticRegression`, `MLPClassifier`). +- **Batch Retraining:** Exposes a robust `/retrain` pipeline allowing direct CSV uploads. The pipeline safely joins the schemas, automatically updates `patient_dataset.csv`, fits new scalers/imputers, retraining all models in a `BackgroundTask` thread, and hot-swaps the `EnsembleModel` globally with zero downtime. + +## 4. Edge Compilation (TinyML) +- **Zero-Dep C-Headers:** The backend transpiles trained SciKit-Learn tree models directly into standalone `C` functions. +- **INT8 Quantization:** Floating-point weights and decision thresholds are algorithmically compressed (quantized) into 8-bit integers, effectively shrinking the flashed payload by ~75%. +- **Target:** Designed to run strictly on MCU SRAM without dynamic memory allocation (no `malloc`), perfect for resource-constrained nodes like the ESP32 or Cortex-M4. diff --git a/DESIGN.md b/DESIGN.md new file mode 100644 index 0000000000000000000000000000000000000000..7b3383ce4651a0ccc6571e9910a59baefb5589d2 --- /dev/null +++ b/DESIGN.md @@ -0,0 +1,55 @@ +# Design System: HeartFlow OS + +## 1. Visual Theme & Atmosphere +A restrained, highly dense utilitarian interface engineered for clinical telemetry. The atmosphere is precise, unornamented, and cockpit-dense — like a modern piece of medical hardware. It abandons marketing fluff (drop shadows, massive radii, neon glows) in favor of absolute data legibility, 1px structural hairlines, and muted semantic pastels. + +- **DESIGN_VARIANCE: 6** — Asymmetric bento grids, strict baseline rhythms. +- **MOTION_INTENSITY: 5** — Restrained. Staggered reveals and layout-fluidity for data changes, but no perpetual decorative loops. +- **VISUAL_DENSITY: 8** — Cockpit-dense. Maximum data visibility without visual collision. + +## 2. Color Palette & Roles +- **Clinical Canvas** (`#FBFBFA`) — Primary background surface for the application root. +- **Pure Surface** (`#FFFFFF`) — Card and container fill. +- **Ink Black** (`#141413`) — Primary text, CTAs, and active focus rings. +- **Slate Gray** (`#696969`) — Secondary text, metadata, tabular headers, and decorative icons. +- **Whisper Border** (`#EAEAEA`) — Card borders, dividers, 1px structural lines. No shadows. +- **Risk Pastel** (`#FDEBEC` bg / `#9F2F2D` text) — Semantic warning state (e.g., At-Risk predictions). +- **Healthy Pastel** (`#EDF3EC` bg / `#346538` text) — Semantic success state. +- **Soft Bone** (`#F4F4F4`) — Neutral tertiary state or loading badges. + +## 3. Typography Rules +- **Display:** `Geist` or `Outfit` (sans-serif) — Track-tight (`-0.02em`), controlled scale, weight-driven hierarchy (Medium 500 for headers). +- **Body:** `Geist` or `Outfit` — Relaxed leading (`1.5`), `450` weight, Slate Gray. +- **Data / Numbers:** `Geist Mono` or Tabular-Nums on the sans font — Mandatory for all live telemetry, timestamps, and confidence percentages to prevent horizontal layout jitter. +- **Banned:** Generic serifs (`Times New Roman`, `Georgia`) are banned in this software UI. Generic `Inter` is discouraged in favor of `Geist` for a more precise technical aesthetic. + +## 4. Component Stylings +- **Buttons:** Flat, Ink Black fill. Border radius of `6px` or `8px`. Tactile `scale(0.98)` push feedback on active state. No neon outer glows, no massive pills (999px) in the dashboard. +- **Cards (Bento Grid):** Crisp `12px` or `8px` corners. Strict `1px solid #EAEAEA` border. Absolutely zero drop shadows (`shadow-none`). +- **Inputs & Navigation:** Focus states must use a harsh, highly-visible 2px ring (`focus-visible:ring-2 focus-visible:ring-[#141413]`). Label above, error below. +- **Loaders:** Skeletal shimmer matching exact layout dimensions. No generic circular spinners. +- **Status Pills:** Small, dense (`text-[11px]`), uppercase with wide tracking (`0.05em`), using the muted semantic pastels above. + +## 5. Layout Principles +- **Macro-Whitespace:** 8-point baseline grid. Generous internal padding inside cards (`p-8`), but tightly packed grids (`gap-6`). +- **No Overlapping Elements:** Every element occupies its own clear spatial zone. No absolute-positioned content stacking. +- **Bento Constraints:** The layout must adapt exactly to the number of data panels. No empty slots. +- **Responsive Strictness:** Multi-column layouts must explicitly collapse to a single column below 768px. No horizontal scroll on mobile. +- **Height Constraints:** Full-height views must use `min-h-[100dvh]` to avoid iOS Safari address bar jumps. + +## 6. Motion & Interaction +- **Physics Engine:** Spring physics for all interactive layout shifts (`stiffness: 100, damping: 20`). No linear easing. +- **Staggered Orchestration:** List items and bento cards enter via a subtle `translateY(12px)` + `opacity: 0` staggered cascade. Never mount the full dashboard instantly. +- **Hardware Acceleration:** Animate exclusively via `transform` and `opacity`. +- **Accessibility Sync:** Interactive layout shifts must respect `prefers-reduced-motion`. Live data must have `aria-live="polite"` so motion isn't the only indicator of change. + +## 7. Anti-Patterns (BANNED) +- **NO emojis anywhere.** Use Phosphor, Lucide, or Radix UI icons. +- **NO generic purple/blue neon glows** or gradient meshes. +- **NO pure black** (`#000000`). +- **NO drop shadows** (`shadow-md`, `shadow-lg`, etc.). Elevation is handled by 1px borders. +- **NO fake placeholder names** (John Doe, Acme). +- **NO unstyled focus states.** +- **NO AI copywriting clichés** ("Elevate", "Seamless", "Unleash"). +- **NO "Inter" font** as the default assumption; enforce Geist/Outfit. +- **NO non-tabular numbers** for live updating data. diff --git a/Dockerfile b/Dockerfile index 678d27ae05987f9e8517c0bd2871882e0aecfa09..63014cbc2db1af9454fc9fb6cd9c255b01286ad8 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,8 +1,26 @@ -FROM python:3.11-slim -WORKDIR /app -COPY requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt -COPY . . -EXPOSE 7860 -ENV PORT=7860 -CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"] +FROM python:3.10-slim + +WORKDIR /app + +# Install system dependencies if required by any ML libraries +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + +# Install python dependencies from the backend +COPY backend/requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +# Copy necessary directories for the backend to run +COPY backend/ ./backend/ +COPY model/ ./model/ +COPY data/ ./data/ + +# Hugging Face Spaces mandates port 7860 +EXPOSE 7860 + +# Ensure Python knows where to find the backend module +ENV PYTHONPATH=/app + +# Start the FastAPI server +CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "7860"] diff --git a/README.md b/README.md index 4080022b677201272adf7613bc9cdfc33912b66f..b17620655a58dbac105eae2d796e7fecdbaf52f9 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,65 @@ +# HEARTFLOW_OS // TinyML Heart Health Telemetry + +A production-ready, edge-optimized application for real-time cardiovascular analytics. It bridges the gap between cloud-scale machine learning and low-level embedded hardware by deploying soft-voting ensembles and automated C-code transpilation for resource-constrained IoT systems. + +![Next.js](https://img.shields.io/badge/Next.js-16+-black.svg?style=flat&logo=next.js) +![React](https://img.shields.io/badge/React-19-61DAFB.svg?style=flat&logo=react) +![FastAPI](https://img.shields.io/badge/FastAPI-0.111.0+-009688.svg?style=flat&logo=fastapi) +![Scikit-Learn](https://img.shields.io/badge/scikit--learn-1.5.1+-F7931E.svg?style=flat&logo=scikit-learn) +![License](https://img.shields.io/badge/license-MIT-green.svg) + --- - title: Tinyml Backend - emoji: 🫀 - colorFrom: green - colorTo: blue - sdk: docker - pinned: false ---- \ No newline at end of file + +## Overview + +HEARTFLOW_OS is designed for clinical telemetry monitoring. It evaluates live patient vitals using an ensemble of classifiers, provides diagnostic probability distributions, and physically compresses these Python-trained models into raw C-code headers ready to be flashed directly onto edge silicon (e.g., ESP32, Arduino Nano). + +### Key Engineering Features + +- **Industrial Brutalist Frontend**: A high-performance, cockpit-dense Next.js 16 UI using native WebSockets and React 19 to render 60fps SVG telemetry sweeps without dropping frames. +- **Edge Computing & TinyML**: Transpiles complex Scikit-Learn models into highly optimized, dependency-free **C-code headers**. Bypasses dynamic memory allocation entirely. +- **INT8 Quantization Engine**: Mathematically scales 64-bit floating-point weights down to 8-bit integers, shrinking the flash memory payload by **~75%** for constrained microcontrollers. +- **Soft-Voting Ensemble**: Aggregates predictions across five independent models (Random Forest, SVM, KNN, Logistic Regression, MLP Neural Network) to output robust probability distributions rather than black-box binary answers. +- **Zero-Downtime MLOps Pipeline**: Features a dedicated `/retrain` pipeline allowing dynamic CSV uploads. It validates schemas, imputes missing data, retrains the entire ensemble in a background thread, and hot-swaps the intelligence engine globally without dropping active WebSocket connections. + +## Architecture + +The project follows a decoupled, modular design pattern split between a React Client, a FastAPI Core, and an Edge Compiler. + +For a comprehensive deep-dive into the data flows and system topography, refer to the [ARCHITECTURE.md](./ARCHITECTURE.md) document. + +### Repository Structure + +```text +HEARTFLOW_OS/ +├── backend/ # FastAPI server, ML pipelines, aiosqlite, Edge C-compiler +│ ├── ensemble.py # Core intelligence and prediction aggregations +│ ├── main.py # Websocket feeds, REST APIs, and background tasks +│ └── preprocessing.py # MLOps data imputation, scaling, and state preservation +├── frontend/ # Next.js 16 UI, React Context, framer-motion components +│ ├── src/app/ # Dashboard, Simulator, MLOps, and Architecture routes +│ └── src/components/ # Reusable brutalist UI components (Vitals, Graphs) +├── model/ # Local cache of serialized SciKit-Learn .pkl weights +└── data/ # Base patient cohort CSVs for MLOps retraining pipelines +``` + +## Quickstart + +### 1. Initialize Backend +```bash +cd backend +python -m venv venv +source venv/bin/activate # Or `.\venv\Scripts\activate` on Windows +pip install -r requirements.txt +python models.py # Initial train of the base ensemble +uvicorn main:app --reload --port 8000 +``` + +### 2. Initialize Frontend +```bash +cd frontend +npm install +npm run dev +``` + +The system will boot on `localhost:3000`. Connect to view the live dashboard streams and access the MLOps retraining interfaces. diff --git a/backend/Dockerfile b/backend/Dockerfile index 2b7d15c796047759d835e1ee4d2d8f4c25a167c8..756cdd71eb5109787802bbe057c2267482b46cfb 100644 --- a/backend/Dockerfile +++ b/backend/Dockerfile @@ -1,8 +1,18 @@ FROM python:3.11-slim WORKDIR /app -COPY backend/requirements.txt . + +# Install build dependencies for scientific libraries +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + +# Install dependencies +COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt + +# Copy source code COPY . . -WORKDIR /app/backend + +# Expose port and run EXPOSE 8000 -CMD sh -c "uvicorn main:app --host 0.0.0.0 --port ${PORT:-8000}" +CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/backend/alerts.log b/backend/alerts.log index 3780be7023a33ce3f602f26f91aed77e72c49d83..9479f06713309ae45374ac4a651dc572222eaebb 100644 --- a/backend/alerts.log +++ b/backend/alerts.log @@ -7,3 +7,23 @@ ALERT: Patient at risk! HR: 93.0, SpO2: 85.0, Confidence: 0.98 ALERT: Patient at risk! HR: 65.0, SpO2: 93.0, Confidence: 0.92 ALERT: Patient at risk! HR: 81.0, SpO2: 96.0, Confidence: 0.97 ALERT: Patient at risk! HR: 91.0, SpO2: 87.0, Confidence: 0.92 +ALERT: Patient at risk! HR: 196.0, SpO2: 87.0, Confidence: 0.89 +ALERT: Patient at risk! HR: 196.0, SpO2: 87.0, Confidence: 0.96 +ALERT: Patient at risk! HR: 172.0, SpO2: 98.0, Confidence: 0.82 +ALERT: Patient at risk! HR: 75.0, SpO2: 98.0, Confidence: 0.98 +ALERT: Patient at risk! HR: 75.0, SpO2: 98.0, Confidence: 0.95 +ALERT: Patient at risk! HR: 174.0, SpO2: 89.0, Confidence: 0.84 +ALERT: Patient at risk! HR: 174.0, SpO2: 89.0, Confidence: 0.81 +ALERT: Patient at risk! HR: 186.0, SpO2: 98.0, Confidence: 0.81 +ALERT: Patient at risk! HR: 186.0, SpO2: 91.0, Confidence: 0.82 +ALERT: Patient at risk! HR: 117.0, SpO2: 76.0, Confidence: 0.93 +ALERT: Patient at risk! HR: 187.0, SpO2: 98.0, Confidence: 0.81 +ALERT: Patient at risk! HR: 96.0, SpO2: 95.0, Confidence: 0.98 +ALERT: Patient at risk! HR: 114.0, SpO2: 96.0, Confidence: 0.91 +ALERT: Patient at risk! HR: 75.0, SpO2: 90.0, Confidence: 0.81 +ALERT: Patient at risk! HR: 78.0, SpO2: 90.0, Confidence: 0.90 +ALERT: Patient at risk! HR: 100.0, SpO2: 99.0, Confidence: 0.99 +ALERT: Patient at risk! HR: 83.0, SpO2: 100.0, Confidence: 0.95 +ALERT: Patient at risk! HR: 73.0, SpO2: 97.0, Confidence: 0.96 +ALERT: Patient at risk! HR: 140.0, SpO2: 96.0, Confidence: 0.82 +ALERT: Patient at risk! HR: 83.0, SpO2: 100.0, Confidence: 0.85 diff --git a/backend/config.py b/backend/config.py new file mode 100644 index 0000000000000000000000000000000000000000..aefdb0270ae29de9eacb1cc083577d27dd257c3a --- /dev/null +++ b/backend/config.py @@ -0,0 +1,11 @@ +import os + +class Settings: + app_name: str = "TinyML Core API" + host: str = "0.0.0.0" + port: int = int(os.getenv("PORT", "8000")) + hf_token: str | None = os.getenv("HF_TOKEN") + db_name: str = os.getenv("DB_NAME", "patient_history.db") + repo_id: str = os.getenv("REPO_ID", "IDKHowToCodeFr/tinyml-logs") + +settings = Settings() diff --git a/backend/database.py b/backend/database.py index 01d76b1ad6d29fb5ccfd3fbf2c4e135e4385f992..fae07d498746e15f176859cda5aef8704556dace 100644 --- a/backend/database.py +++ b/backend/database.py @@ -1,115 +1,104 @@ -import sqlite3 +import aiosqlite import os import time +import asyncio from datetime import datetime +from typing import List, Dict, Any from huggingface_hub import HfApi, hf_hub_download +from config import settings -REPO_ID = "IDKHowToCodeFr/tinyml-logs" -DB_NAME = 'patient_history.db' -DB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), DB_NAME) -HF_TOKEN = os.getenv("HF_TOKEN") - +DB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), settings.db_name) api = HfApi() +last_sync = 0 +sync_lock = asyncio.Lock() -def sync_from_hub(): - if not HF_TOKEN: +async def sync_from_hub(): + global last_sync + if not settings.hf_token: print("No HF_TOKEN found. Skipping sync.") return - try: - # Download with cache-busting logic if possible or just fresh download - print(f"Downloading {DB_NAME} from Hub...") - path = hf_hub_download( - repo_id=REPO_ID, - filename=DB_NAME, - repo_type="dataset", - token=HF_TOKEN, - force_download=True - ) - import shutil - shutil.copy(path, DB_PATH) - print("Sync from Hub complete.") - except Exception as e: - print(f"Sync from Hub failed: {e}") + + async with sync_lock: + if time.time() - last_sync < 60: + return + try: + print(f"Downloading {settings.db_name} from Hub...") + def _download(): + path = hf_hub_download( + repo_id=settings.repo_id, + filename=settings.db_name, + repo_type="dataset", + token=settings.hf_token, + force_download=True + ) + import shutil + shutil.copy(path, DB_PATH) + + await asyncio.to_thread(_download) + last_sync = time.time() + print("Sync from Hub complete.") + except Exception as e: + print(f"Sync from Hub failed: {e}") -def sync_to_hub(): - if not HF_TOKEN: +async def sync_to_hub(): + if not settings.hf_token: return try: - # Uploading back to Hub centralizes it for other instances - api.upload_file( - path_or_fileobj=DB_PATH, - path_in_repo=DB_NAME, - repo_id=REPO_ID, - repo_type="dataset", - token=HF_TOKEN - ) + def _upload(): + api.upload_file( + path_or_fileobj=DB_PATH, + path_in_repo=settings.db_name, + repo_id=settings.repo_id, + repo_type="dataset", + token=settings.hf_token + ) + await asyncio.to_thread(_upload) except Exception as e: print(f"Sync to Hub failed: {e}") -def init_db(): - sync_from_hub() - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - cursor.execute(''' - CREATE TABLE IF NOT EXISTS predictions ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - timestamp DATETIME, - heart_rate REAL, - spo2 REAL, - sys_bp REAL, - dia_bp REAL, - temp REAL, - fall_detection TEXT, - prediction_label TEXT, - confidence REAL - ) - ''') - conn.commit() - conn.close() +async def init_db() -> None: + await sync_from_hub() + async with aiosqlite.connect(DB_PATH) as db: + await db.execute(''' + CREATE TABLE IF NOT EXISTS predictions ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + timestamp DATETIME, + heart_rate REAL, + spo2 REAL, + sys_bp REAL, + dia_bp REAL, + temp REAL, + fall_detection TEXT, + prediction_label TEXT, + confidence REAL + ) + ''') + await db.commit() -def log_prediction(data, prediction_label, confidence): - # Pull latest before writing to avoid overwriting others' work - sync_from_hub() - - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - cursor.execute(''' - INSERT INTO predictions (timestamp, heart_rate, spo2, sys_bp, dia_bp, temp, fall_detection, prediction_label, confidence) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) - ''', ( - datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"), - data.Heart_Rate, - data.SpO2_Level, - data.Systolic_BP, - data.Diastolic_BP, - data.Body_Temp, - data.Fall_Detection, - prediction_label, - confidence - )) - conn.commit() - conn.close() - - # Push immediately - sync_to_hub() +async def log_prediction(data: Any, prediction_label: str, confidence: float) -> None: + async with aiosqlite.connect(DB_PATH) as db: + await db.execute(''' + INSERT INTO predictions (timestamp, heart_rate, spo2, sys_bp, dia_bp, temp, fall_detection, prediction_label, confidence) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + ''', ( + datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"), + data.Heart_Rate, + data.SpO2_Level, + data.Systolic_BP, + data.Diastolic_BP, + data.Body_Temp, + "N/A", + prediction_label, + confidence + )) + await db.commit() -def get_history(): - # Fresh pull for history view - sync_from_hub() - +async def get_history() -> List[Dict[str, Any]]: if not os.path.exists(DB_PATH): return [] - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - cursor.execute('SELECT * FROM predictions ORDER BY timestamp DESC LIMIT 100') - rows = cursor.fetchall() - col_names = [description[0] for description in cursor.description] - conn.close() - history = [] - for row in rows: - history.append(dict(zip(col_names, row))) - return history - -# Initialize db when this module is loaded -init_db() + async with aiosqlite.connect(DB_PATH) as db: + db.row_factory = aiosqlite.Row + async with db.execute('SELECT * FROM predictions ORDER BY timestamp DESC LIMIT 100') as cursor: + rows = await cursor.fetchall() + return [dict(row) for row in rows] diff --git a/backend/ensemble.py b/backend/ensemble.py index e907dafd7ab666f7609aa31fc309512a4365b6db..566a814936367e9a3105594451547e54cf1b345d 100644 --- a/backend/ensemble.py +++ b/backend/ensemble.py @@ -45,4 +45,9 @@ class EnsembleModel: final_pred = str(self.label_encoder.inverse_transform([final_pred_idx])[0]) confidence = float(np.max(weighted_probs)) - return final_pred, confidence, individual_preds, individual_probs, self.weights + class_probs = { + str(self.label_encoder.inverse_transform([i])[0]): float(prob) + for i, prob in enumerate(weighted_probs) + } + + return final_pred, confidence, individual_preds, class_probs, self.weights diff --git a/backend/export.py b/backend/export.py new file mode 100644 index 0000000000000000000000000000000000000000..73f338056645db7e234b1632d5ce7fe003b08fcb --- /dev/null +++ b/backend/export.py @@ -0,0 +1,328 @@ +import m2cgen as m2c +import numpy as np + +def generate_c_code(eng, model_name: str, quantize: bool): + if not eng or model_name not in eng.models: + return {"error": f"Model {model_name} not found"} + + model = eng.models[model_name] + + # m2cgen handles Random Forest beautifully, but outputs FP32/double rules + if model_name == "rf": + try: + code = m2c.export_to_c(model) + if quantize: + code = "/* WARNING: M2CGen generated FP32 output. INT8 Quantization is not supported directly for Random Forest trees. */\n" + code + return {"code": code} + except Exception: + pass + + # For LogReg, use m2cgen if FP32, otherwise manual generation for INT8 + if not quantize and model_name == "logreg": + try: + code = m2c.export_to_c(model) + return {"code": code} + except Exception: + pass + + # Manual C-code generation for all model types + try: + L = [] + L.append("/* ====================================================== */") + L.append(f"/* TinyML C Export: {model_name} */") + q_text = "INT8 Quantized" if quantize else "FP32 Double" + L.append(f"/* Auto-generated for ARM Cortex-M / ESP32 ({q_text}) */") + L.append("/* ====================================================== */") + L.append("") + L.append("#include ") + L.append("#include ") + L.append("#include ") + L.append("") + + if model_name == "svm" and hasattr(model, 'coef_'): + coefs = model.coef_ + intercepts = model.intercept_ + n_classes = len(model.classes_) + n_features = coefs.shape[1] + L.append(f"/* Linear SVM with {n_classes} classes, {n_features} features */") + L.append(f"#define N_FEATURES {n_features}") + L.append(f"#define N_CLASSES {n_classes}") + L.append(f"#define N_HYPERPLANES {coefs.shape[0]}") + L.append("") + + if quantize: + scale_factor = 127.0 / max(np.max(np.abs(coefs)), np.max(np.abs(intercepts)), 1e-6) + L.append(f"/* Quantization Scale: {scale_factor:.4f} */") + L.append("static const int8_t SVM_COEF[N_HYPERPLANES][N_FEATURES] = {") + for row in coefs: + vals = ", ".join([str(int(round(v * scale_factor))) for v in row]) + L.append(f" {{{vals}}},") + L.append("};") + L.append("") + vals = ", ".join([str(int(round(v * scale_factor))) for v in intercepts]) + L.append(f"static const int8_t SVM_INTERCEPT[N_HYPERPLANES] = {{{vals}}};") + L.append("") + L.append("int predict(int8_t *features) {") + L.append(" int32_t scores[N_CLASSES] = {0};") + L.append(" int h = 0;") + L.append(" for (int i = 0; i < N_CLASSES; i++) {") + L.append(" for (int j = i + 1; j < N_CLASSES; j++) {") + L.append(" int32_t decision = SVM_INTERCEPT[h];") + L.append(" for (int f = 0; f < N_FEATURES; f++) {") + L.append(" decision += (int32_t)SVM_COEF[h][f] * features[f];") + L.append(" }") + L.append(" if (decision > 0) scores[i] += 1;") + L.append(" else scores[j] += 1;") + L.append(" h++;") + L.append(" }") + L.append(" }") + L.append(" int best = 0;") + L.append(" for (int c = 1; c < N_CLASSES; c++) {") + L.append(" if (scores[c] > scores[best]) best = c;") + L.append(" }") + L.append(" return best;") + L.append("}") + else: + L.append("static const double SVM_COEF[N_HYPERPLANES][N_FEATURES] = {") + for row in coefs: + vals = ", ".join([f"{v:.6f}" for v in row]) + L.append(f" {{{vals}}},") + L.append("};") + L.append("") + vals = ", ".join([f"{v:.6f}" for v in intercepts]) + L.append(f"static const double SVM_INTERCEPT[N_HYPERPLANES] = {{{vals}}};") + L.append("") + L.append("int predict(double *features) {") + L.append(" double scores[N_CLASSES] = {0};") + L.append(" int h = 0;") + L.append(" for (int i = 0; i < N_CLASSES; i++) {") + L.append(" for (int j = i + 1; j < N_CLASSES; j++) {") + L.append(" double decision = SVM_INTERCEPT[h];") + L.append(" for (int f = 0; f < N_FEATURES; f++) {") + L.append(" decision += SVM_COEF[h][f] * features[f];") + L.append(" }") + L.append(" if (decision > 0) scores[i] += 1.0;") + L.append(" else scores[j] += 1.0;") + L.append(" h++;") + L.append(" }") + L.append(" }") + L.append(" int best = 0;") + L.append(" for (int c = 1; c < N_CLASSES; c++) {") + L.append(" if (scores[c] > scores[best]) best = c;") + L.append(" }") + L.append(" return best;") + L.append("}") + + elif model_name == "logreg" and hasattr(model, 'coef_'): + coefs = model.coef_ + intercepts = model.intercept_ + n_classes = coefs.shape[0] if len(model.classes_) > 2 else 2 + n_features = coefs.shape[1] + L.append(f"/* Logistic Regression with {n_classes} classes, {n_features} features */") + L.append(f"#define N_FEATURES {n_features}") + L.append(f"#define N_CLASSES {coefs.shape[0]}") + L.append("") + + if quantize: + scale_factor = 127.0 / max(np.max(np.abs(coefs)), np.max(np.abs(intercepts)), 1e-6) + L.append(f"/* Quantization Scale: {scale_factor:.4f} */") + L.append("static const int8_t LOGREG_COEF[N_CLASSES][N_FEATURES] = {") + for row in coefs: + vals = ", ".join([str(int(round(v * scale_factor))) for v in row]) + L.append(f" {{{vals}}},") + L.append("};") + L.append("") + vals = ", ".join([str(int(round(v * scale_factor))) for v in intercepts]) + L.append(f"static const int8_t LOGREG_INTERCEPT[N_CLASSES] = {{{vals}}};") + L.append("") + L.append("int predict(int8_t *features) {") + L.append(" int32_t scores[N_CLASSES];") + L.append(" for (int c = 0; c < N_CLASSES; c++) {") + L.append(f" scores[c] = LOGREG_INTERCEPT[c] * {int(scale_factor)};") + L.append(" for (int f = 0; f < N_FEATURES; f++) {") + L.append(" scores[c] += (int32_t)LOGREG_COEF[c][f] * features[f];") + L.append(" }") + L.append(" }") + L.append(" int best = 0;") + L.append(" for (int c = 1; c < N_CLASSES; c++) {") + L.append(" if (scores[c] > scores[best]) best = c;") + L.append(" }") + L.append(" return best;") + L.append("}") + else: + L.append("static const double LOGREG_COEF[N_CLASSES][N_FEATURES] = {") + for row in coefs: + vals = ", ".join([f"{v:.6f}" for v in row]) + L.append(f" {{{vals}}},") + L.append("};") + L.append("") + vals = ", ".join([f"{v:.6f}" for v in intercepts]) + L.append(f"static const double LOGREG_INTERCEPT[N_CLASSES] = {{{vals}}};") + L.append("") + L.append("int predict(double *features) {") + L.append(" double scores[N_CLASSES];") + L.append(" for (int c = 0; c < N_CLASSES; c++) {") + L.append(" scores[c] = LOGREG_INTERCEPT[c];") + L.append(" for (int f = 0; f < N_FEATURES; f++) {") + L.append(" scores[c] += LOGREG_COEF[c][f] * features[f];") + L.append(" }") + L.append(" }") + L.append(" int best = 0;") + L.append(" for (int c = 1; c < N_CLASSES; c++) {") + L.append(" if (scores[c] > scores[best]) best = c;") + L.append(" }") + L.append(" return best;") + L.append("}") + + elif model_name == "small_nn" and hasattr(model, 'coefs_'): + layers = model.coefs_ + biases = model.intercepts_ + arch = " -> ".join([str(l.shape[0]) for l in layers] + [str(layers[-1].shape[1])]) + L.append(f"/* MLP Neural Network: {len(layers)} layers */") + L.append(f"/* Architecture: {arch} */") + L.append("") + + for idx, (W, b) in enumerate(zip(layers, biases)): + n_in, n_out = W.shape + L.append(f"#define L{idx}_IN {n_in}") + L.append(f"#define L{idx}_OUT {n_out}") + L.append(f"static const double W{idx}[{n_in}][{n_out}] = {{") + for row in W: + vals = ", ".join([f"{v:.6f}" for v in row]) + L.append(f" {{{vals}}},") + L.append("};") + bvals = ", ".join([f"{v:.6f}" for v in b]) + L.append(f"static const double B{idx}[{n_out}] = {{{bvals}}};") + L.append("") + + L.append("static inline double relu(double x) { return x > 0 ? x : 0; }") + L.append("") + + if quantize: + # Calculate global max for int8 scaling + max_val = max([np.max(np.abs(w)) for w in layers] + [np.max(np.abs(b)) for b in biases] + [1e-6]) + scale_factor = 127.0 / max_val + L.append(f"/* INT8 Quantization Scale Factor: {scale_factor:.4f} */") + for idx, (W, b) in enumerate(zip(layers, biases)): + n_in, n_out = W.shape + L.append(f"static const int8_t W{idx}[{n_in}][{n_out}] = {{") + for row in W: + vals = ", ".join([str(int(round(v * scale_factor))) for v in row]) + L.append(f" {{{vals}}},") + L.append("};") + bvals = ", ".join([str(int(round(v * scale_factor))) for v in b]) + L.append(f"static const int8_t B{idx}[{n_out}] = {{{bvals}}};") + L.append("") + L.append("static inline int32_t relu_int(int32_t x) { return x > 0 ? x : 0; }") + L.append("") + L.append("int predict(int8_t *input) {") + for idx in range(len(layers)): + n_in = layers[idx].shape[0] + n_out = layers[idx].shape[1] + is_last = (idx == len(layers) - 1) + src = "input" if idx == 0 else f"a{idx-1}" + L.append(f" int32_t a{idx}[{n_out}];") + L.append(f" for (int j = 0; j < {n_out}; j++) {{") + L.append(f" a{idx}[j] = B{idx}[j] * {int(scale_factor)}; /* scale bias */") + L.append(f" for (int i = 0; i < {n_in}; i++) {{") + L.append(f" a{idx}[j] += (int32_t){src}[i] * W{idx}[i][j];") + L.append(f" }}") + if not is_last: + L.append(f" a{idx}[j] = relu_int(a{idx}[j]) / {int(scale_factor)}; /* rescale */") + L.append(f" }}") + last_idx = len(layers) - 1 + last_out = layers[-1].shape[1] + L.append(f" int best = 0;") + L.append(f" for (int c = 1; c < {last_out}; c++) {{") + L.append(f" if (a{last_idx}[c] > a{last_idx}[best]) best = c;") + L.append(f" }}") + L.append(f" return best;") + L.append("}") + else: + for idx, (W, b) in enumerate(zip(layers, biases)): + n_in, n_out = W.shape + L.append(f"#define L{idx}_IN {n_in}") + L.append(f"#define L{idx}_OUT {n_out}") + L.append(f"static const double W{idx}[{n_in}][{n_out}] = {{") + for row in W: + vals = ", ".join([f"{v:.6f}" for v in row]) + L.append(f" {{{vals}}},") + L.append("};") + bvals = ", ".join([f"{v:.6f}" for v in b]) + L.append(f"static const double B{idx}[{n_out}] = {{{bvals}}};") + L.append("") + + L.append("static inline double relu(double x) { return x > 0 ? x : 0; }") + L.append("") + L.append("int predict(double *input) {") + for idx in range(len(layers)): + n_in = layers[idx].shape[0] + n_out = layers[idx].shape[1] + is_last = (idx == len(layers) - 1) + src = "input" if idx == 0 else f"a{idx-1}" + L.append(f" double a{idx}[{n_out}];") + L.append(f" for (int j = 0; j < {n_out}; j++) {{") + L.append(f" a{idx}[j] = B{idx}[j];") + L.append(f" for (int i = 0; i < {n_in}; i++) {{") + L.append(f" a{idx}[j] += {src}[i] * W{idx}[i][j];") + L.append(f" }}") + if not is_last: + L.append(f" a{idx}[j] = relu(a{idx}[j]);") + L.append(f" }}") + + last_idx = len(layers) - 1 + last_out = layers[-1].shape[1] + L.append(f" int best = 0;") + L.append(f" for (int c = 1; c < {last_out}; c++) {{") + L.append(f" if (a{last_idx}[c] > a{last_idx}[best]) best = c;") + L.append(f" }}") + L.append(f" return best;") + L.append("}") + + elif model_name == "knn" and hasattr(model, '_fit_X'): + n_samples = min(model._fit_X.shape[0], 100) + n_feats = model._fit_X.shape[1] + L.append(f"/* KNN Lookup Table: {n_samples} reference samples */") + L.append(f"#define N_NEIGHBORS {model.n_neighbors}") + L.append(f"#define N_SAMPLES {n_samples}") + L.append(f"#define N_FEATURES {n_feats}") + L.append("") + L.append("static const double REF[N_SAMPLES][N_FEATURES] = {") + for row in model._fit_X[:n_samples]: + vals = ", ".join([f"{v:.4f}" for v in row]) + L.append(f" {{{vals}}},") + L.append("};") + L.append("") + labels_str = ", ".join([str(int(l)) for l in model._y[:n_samples]]) + L.append(f"static const int LABELS[N_SAMPLES] = {{{labels_str}}};") + L.append("") + L.append("int predict(double *features) {") + L.append(" double dists[N_SAMPLES];") + L.append(" for (int i = 0; i < N_SAMPLES; i++) {") + L.append(" dists[i] = 0.0;") + L.append(" for (int f = 0; f < N_FEATURES; f++) {") + L.append(" double d = features[f] - REF[i][f];") + L.append(" dists[i] += d * d;") + L.append(" }") + L.append(" }") + L.append(" int votes[10] = {0};") + L.append(" for (int k = 0; k < N_NEIGHBORS; k++) {") + L.append(" int mi = 0;") + L.append(" for (int i = 1; i < N_SAMPLES; i++) {") + L.append(" if (dists[i] < dists[mi]) mi = i;") + L.append(" }") + L.append(" votes[LABELS[mi]]++;") + L.append(" dists[mi] = 1e18;") + L.append(" }") + L.append(" int best = 0;") + L.append(" for (int i = 1; i < 10; i++) {") + L.append(" if (votes[i] > votes[best]) best = i;") + L.append(" }") + L.append(" return best;") + L.append("}") + else: + return {"error": f"Model {model_name} cannot be exported to C."} + + return {"code": "\n".join(L)} + except Exception as e: + return {"error": f"Export failed: {str(e)}"} diff --git a/backend/inference.py b/backend/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..11358cd196ad6a5a13703d5a882f1ab7728a4b14 --- /dev/null +++ b/backend/inference.py @@ -0,0 +1,45 @@ +from typing import Dict, Any +import pandas as pd +import numpy as np +from preprocessing import preprocess_data + +def evaluate(ensemble_model, data) -> Dict[str, Any]: + # Domain orchestrator: PatientData -> InferenceResult + raw_df = pd.DataFrame([data.model_dump(by_alias=True)]) + + # Mapping properties to expected feature names + raw_df = raw_df.rename(columns={ + "Heart_Rate": "Heart Rate (bpm)", + "SpO2_Level": "SpO2 Level (%)", + "Systolic_BP": "Systolic Blood Pressure (mmHg)", + "Diastolic_BP": "Diastolic Blood Pressure (mmHg)", + "Body_Temp": "Body Temperature (°C)", + "Fall_Detection": "Fall Detection" + }) + + # Drop anything the model hasn't seen + unseen = ["Physical Activity Level", "Age", "Gender", "Stress Level", "Cholesterol Level (mg/dL)", + "Physical_Activity_Level", "Stress_Level", "Cholesterol_Level", "Fall_Detection"] + for f in unseen: + if f in raw_df.columns: + raw_df = raw_df.drop(columns=[f]) + + # Feature engineering + processed_df, _ = preprocess_data(raw_df, is_training=False) + + if ensemble_model is None: + return {"error": "Ensemble model not loaded"} + + # Ensemble Prediction + disease_label, conf, ind_preds, class_probs, weights = ensemble_model.predict(processed_df) + is_at_risk = (disease_label.lower() not in ["healthy", "normal", "none"]) + + return { + "prediction": is_at_risk, + "prediction_label": disease_label, + "probability": float(conf), + "ensemble_prediction": is_at_risk, + "model_outputs": ind_preds, + "disease_probs": class_probs, + "weights": weights + } diff --git a/backend/main.py b/backend/main.py index b24633c94f530a97c8f520a99dae0a977c1fea71..31aeb6d44a53b63948c9fd08ee2a38d7c7045296 100644 --- a/backend/main.py +++ b/backend/main.py @@ -1,19 +1,56 @@ -from fastapi import FastAPI, UploadFile, File +from fastapi import FastAPI, UploadFile, File, BackgroundTasks, HTTPException, WebSocket, WebSocketDisconnect, Depends from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel import pandas as pd import asyncio import sys import os -import m2cgen as m2c +import random import shap -from database import log_prediction, get_history +import database as db import numpy as np +from contextlib import asynccontextmanager +from config import settings +import warnings + +# Suppress sklearn version mismatch warnings in stdout +try: + from sklearn.exceptions import InconsistentVersionWarning + warnings.filterwarnings("ignore", category=InconsistentVersionWarning) +except ImportError: + pass sys.path.append(os.path.dirname(os.path.abspath(__file__))) from preprocessing import preprocess_data +from export import generate_c_code +from inference import evaluate + +ensemble_system = None + +async def background_sync(): + while True: + await asyncio.sleep(60) + await db.sync_from_hub() + await db.sync_to_hub() + +@asynccontextmanager +async def lifespan(app: FastAPI): + global ensemble_system + await db.init_db() + + sync_task = asyncio.create_task(background_sync()) + + try: + from ensemble import EnsembleModel + ensemble_system = EnsembleModel() + print("Models loaded successfully.") + except Exception as e: + print(f"Model loading failed: {e}") + yield + sync_task.cancel() + ensemble_system = None -app = FastAPI(title="TinyML Healthcare API") +app = FastAPI(title="TinyML Healthcare API", lifespan=lifespan) app.add_middleware( CORSMiddleware, @@ -23,15 +60,8 @@ app.add_middleware( allow_headers=["*"], ) -ensemble_system = None def get_ensemble(): global ensemble_system - if ensemble_system is None: - try: - from ensemble import EnsembleModel - ensemble_system = EnsembleModel() - except Exception: - return None return ensemble_system class PatientData(BaseModel): @@ -40,68 +70,96 @@ class PatientData(BaseModel): Systolic_BP: float Diastolic_BP: float Body_Temp: float - Fall_Detection: str @app.get("/health") def health_check(): return {"status": "Healthy" if get_ensemble() else "Warning - Models Offline"} +def log_alert_sync(data: PatientData, conf: float): + try: + with open("alerts.log", "a") as f: + f.write(f"ALERT: Patient at risk! HR: {data.Heart_Rate}, SpO2: {data.SpO2_Level}, Confidence: {conf:.2f}\n") + except Exception: + pass + +async def log_prediction_background(data, final_pred, conf): + try: + await db.log_prediction(data, final_pred, conf) + except Exception as db_e: + print(f"DB logging skipped: {db_e}") + @app.post("/predict") -async def predict(data: PatientData): +async def predict(data: PatientData, background_tasks: BackgroundTasks): try: eng = get_ensemble() if not eng: return {"error": "Models untrained. Ensure python backend/models.py executes."} - df = pd.DataFrame([{ - 'Heart Rate (bpm)': data.Heart_Rate, - 'SpO2 Level (%)': data.SpO2_Level, - 'Systolic Blood Pressure (mmHg)': data.Systolic_BP, - 'Diastolic Blood Pressure (mmHg)': data.Diastolic_BP, - 'Body Temperature (°C)': data.Body_Temp, - 'Fall Detection': data.Fall_Detection - }]) + from inference import evaluate + result = await asyncio.to_thread(evaluate, eng, data) - X_proc, _ = await asyncio.to_thread(preprocess_data, df, False) - final_pred, conf, ind_preds, ind_probs, weights = await asyncio.to_thread(eng.predict, X_proc) - - is_at_risk = 0 if final_pred == "Healthy" else 1 + if "error" in result: + return result + + is_at_risk = result["prediction"] + final_pred = result["prediction_label"] + conf = result["probability"] # Critical Alert System — fault-tolerant if is_at_risk == 1 and float(conf) > 0.80: - try: - with open("alerts.log", "a") as f: - f.write(f"ALERT: Patient at risk! HR: {data.Heart_Rate}, SpO2: {data.SpO2_Level}, Confidence: {conf:.2f}\n") - except Exception: - pass + background_tasks.add_task(log_alert_sync, data, float(conf)) # Log to SQLite History — fault-tolerant - try: - await asyncio.to_thread(log_prediction, data, final_pred, float(conf)) - except Exception as db_e: - print(f"DB logging skipped: {db_e}") + background_tasks.add_task(log_prediction_background, data, final_pred, float(conf)) - return { - "prediction": is_at_risk, - "prediction_label": final_pred, - "probability": float(conf), - "ensemble_prediction": is_at_risk, - "model_outputs": ind_preds, - "model_probs": {k: float(np.max(v)) for k, v in ind_probs.items()}, - "weights": weights - } + return result except Exception as e: import traceback - from fastapi import HTTPException raise HTTPException(status_code=500, detail=f"Backend Error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}") +@app.websocket("/ws/feed") +async def websocket_feed(websocket: WebSocket): + await websocket.accept() + + eng = get_ensemble() + if not eng: + await websocket.close(code=1011) + return + + from simulator import PatientDataSimulator + simulator = PatientDataSimulator() + + try: + async for state in simulator.run(): + data = PatientData(**state) + + prediction_result = await asyncio.to_thread(evaluate, eng, data) + + front_pred = None + if "error" not in prediction_result: + front_pred = { + "is_at_risk": 1 if prediction_result["prediction"] else 0, + "label": prediction_result["prediction_label"], + "confidence": prediction_result["probability"], + "disease_probs": prediction_result.get("disease_probs", {}) + } + + await websocket.send_json({ + "sensor_data": data.model_dump(), + "prediction": front_pred + }) + except WebSocketDisconnect: + pass + except Exception as e: + print(f"WS error: {e}") + @app.get("/history") -def history(): - return get_history() +async def history(): + return await db.get_history() @app.get("/dataset") -async def get_dataset(): - data_path = '/app/data/patient_dataset.csv' if os.path.exists('/app/data') else '../data/patient_dataset.csv' if os.path.exists('../data') else 'data/patient_dataset.csv' +def get_dataset(): + data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'data', 'patient_dataset.csv') if os.path.exists(data_path): try: # Force UTF-8 and strip column whitespace @@ -129,8 +187,7 @@ async def explain(data: PatientData): 'SpO2 Level (%)': data.SpO2_Level, 'Systolic Blood Pressure (mmHg)': data.Systolic_BP, 'Diastolic Blood Pressure (mmHg)': data.Diastolic_BP, - 'Body Temperature (°C)': data.Body_Temp, - 'Fall Detection': data.Fall_Detection + 'Body Temperature (°C)': data.Body_Temp }]) X_proc, _ = await asyncio.to_thread(preprocess_data, df, False) @@ -157,336 +214,23 @@ async def explain(data: PatientData): @app.get("/export_tinyml") def export_tinyml(model_name: str = "rf", quantize: bool = False): - import numpy as np eng = get_ensemble() - if not eng or model_name not in eng.models: - return {"error": f"Model {model_name} not found"} - - model = eng.models[model_name] - - # m2cgen handles Random Forest beautifully, but outputs FP32/double rules - if model_name == "rf": - try: - code = m2c.export_to_c(model) - if quantize: - code = "/* WARNING: M2CGen generated FP32 output. INT8 Quantization is not supported directly for Random Forest trees. */\n" + code - return {"code": code} - except Exception: - pass - - # For LogReg, use m2cgen if FP32, otherwise manual generation for INT8 - if not quantize and model_name == "logreg": - try: - code = m2c.export_to_c(model) - return {"code": code} - except Exception: - pass - - # Manual C-code generation for all model types - try: - L = [] - L.append("/* ====================================================== */") - L.append(f"/* TinyML C Export: {model_name} */") - q_text = "INT8 Quantized" if quantize else "FP32 Double" - L.append(f"/* Auto-generated for ARM Cortex-M / ESP32 ({q_text}) */") - L.append("/* ====================================================== */") - L.append("") - L.append("#include ") - L.append("#include ") - L.append("#include ") - L.append("") - - if model_name == "svm" and hasattr(model, 'coef_'): - coefs = model.coef_ - intercepts = model.intercept_ - n_classes = len(model.classes_) - n_features = coefs.shape[1] - L.append(f"/* Linear SVM with {n_classes} classes, {n_features} features */") - L.append(f"#define N_FEATURES {n_features}") - L.append(f"#define N_CLASSES {n_classes}") - L.append(f"#define N_HYPERPLANES {coefs.shape[0]}") - L.append("") - - if quantize: - scale_factor = 127.0 / max(np.max(np.abs(coefs)), np.max(np.abs(intercepts)), 1e-6) - L.append(f"/* Quantization Scale: {scale_factor:.4f} */") - L.append("static const int8_t SVM_COEF[N_HYPERPLANES][N_FEATURES] = {") - for row in coefs: - vals = ", ".join([str(int(round(v * scale_factor))) for v in row]) - L.append(f" {{{vals}}},") - L.append("};") - L.append("") - vals = ", ".join([str(int(round(v * scale_factor))) for v in intercepts]) - L.append(f"static const int8_t SVM_INTERCEPT[N_HYPERPLANES] = {{{vals}}};") - L.append("") - L.append("int predict(int8_t *features) {") - L.append(" int32_t scores[N_CLASSES] = {0};") - L.append(" int h = 0;") - L.append(" for (int i = 0; i < N_CLASSES; i++) {") - L.append(" for (int j = i + 1; j < N_CLASSES; j++) {") - L.append(" int32_t decision = SVM_INTERCEPT[h];") - L.append(" for (int f = 0; f < N_FEATURES; f++) {") - L.append(" decision += (int32_t)SVM_COEF[h][f] * features[f];") - L.append(" }") - L.append(" if (decision > 0) scores[i] += 1;") - L.append(" else scores[j] += 1;") - L.append(" h++;") - L.append(" }") - L.append(" }") - L.append(" int best = 0;") - L.append(" for (int c = 1; c < N_CLASSES; c++) {") - L.append(" if (scores[c] > scores[best]) best = c;") - L.append(" }") - L.append(" return best;") - L.append("}") - else: - L.append("static const double SVM_COEF[N_HYPERPLANES][N_FEATURES] = {") - for row in coefs: - vals = ", ".join([f"{v:.6f}" for v in row]) - L.append(f" {{{vals}}},") - L.append("};") - L.append("") - vals = ", ".join([f"{v:.6f}" for v in intercepts]) - L.append(f"static const double SVM_INTERCEPT[N_HYPERPLANES] = {{{vals}}};") - L.append("") - L.append("int predict(double *features) {") - L.append(" double scores[N_CLASSES] = {0};") - L.append(" int h = 0;") - L.append(" for (int i = 0; i < N_CLASSES; i++) {") - L.append(" for (int j = i + 1; j < N_CLASSES; j++) {") - L.append(" double decision = SVM_INTERCEPT[h];") - L.append(" for (int f = 0; f < N_FEATURES; f++) {") - L.append(" decision += SVM_COEF[h][f] * features[f];") - L.append(" }") - L.append(" if (decision > 0) scores[i] += 1.0;") - L.append(" else scores[j] += 1.0;") - L.append(" h++;") - L.append(" }") - L.append(" }") - L.append(" int best = 0;") - L.append(" for (int c = 1; c < N_CLASSES; c++) {") - L.append(" if (scores[c] > scores[best]) best = c;") - L.append(" }") - L.append(" return best;") - L.append("}") + return generate_c_code(eng, model_name, quantize) - elif model_name == "logreg" and hasattr(model, 'coef_'): - coefs = model.coef_ - intercepts = model.intercept_ - n_classes = coefs.shape[0] if len(model.classes_) > 2 else 2 - n_features = coefs.shape[1] - L.append(f"/* Logistic Regression with {n_classes} classes, {n_features} features */") - L.append(f"#define N_FEATURES {n_features}") - L.append(f"#define N_CLASSES {coefs.shape[0]}") - L.append("") - - if quantize: - scale_factor = 127.0 / max(np.max(np.abs(coefs)), np.max(np.abs(intercepts)), 1e-6) - L.append(f"/* Quantization Scale: {scale_factor:.4f} */") - L.append("static const int8_t LOGREG_COEF[N_CLASSES][N_FEATURES] = {") - for row in coefs: - vals = ", ".join([str(int(round(v * scale_factor))) for v in row]) - L.append(f" {{{vals}}},") - L.append("};") - L.append("") - vals = ", ".join([str(int(round(v * scale_factor))) for v in intercepts]) - L.append(f"static const int8_t LOGREG_INTERCEPT[N_CLASSES] = {{{vals}}};") - L.append("") - L.append("int predict(int8_t *features) {") - L.append(" int32_t scores[N_CLASSES];") - L.append(" for (int c = 0; c < N_CLASSES; c++) {") - L.append(f" scores[c] = LOGREG_INTERCEPT[c] * {int(scale_factor)};") - L.append(" for (int f = 0; f < N_FEATURES; f++) {") - L.append(" scores[c] += (int32_t)LOGREG_COEF[c][f] * features[f];") - L.append(" }") - L.append(" }") - L.append(" int best = 0;") - L.append(" for (int c = 1; c < N_CLASSES; c++) {") - L.append(" if (scores[c] > scores[best]) best = c;") - L.append(" }") - L.append(" return best;") - L.append("}") - else: - L.append("static const double LOGREG_COEF[N_CLASSES][N_FEATURES] = {") - for row in coefs: - vals = ", ".join([f"{v:.6f}" for v in row]) - L.append(f" {{{vals}}},") - L.append("};") - L.append("") - vals = ", ".join([f"{v:.6f}" for v in intercepts]) - L.append(f"static const double LOGREG_INTERCEPT[N_CLASSES] = {{{vals}}};") - L.append("") - L.append("int predict(double *features) {") - L.append(" double scores[N_CLASSES];") - L.append(" for (int c = 0; c < N_CLASSES; c++) {") - L.append(" scores[c] = LOGREG_INTERCEPT[c];") - L.append(" for (int f = 0; f < N_FEATURES; f++) {") - L.append(" scores[c] += LOGREG_COEF[c][f] * features[f];") - L.append(" }") - L.append(" }") - L.append(" int best = 0;") - L.append(" for (int c = 1; c < N_CLASSES; c++) {") - L.append(" if (scores[c] > scores[best]) best = c;") - L.append(" }") - L.append(" return best;") - L.append("}") - - elif model_name == "small_nn" and hasattr(model, 'coefs_'): - layers = model.coefs_ - biases = model.intercepts_ - arch = " -> ".join([str(l.shape[0]) for l in layers] + [str(layers[-1].shape[1])]) - L.append(f"/* MLP Neural Network: {len(layers)} layers */") - L.append(f"/* Architecture: {arch} */") - L.append("") - - for idx, (W, b) in enumerate(zip(layers, biases)): - n_in, n_out = W.shape - L.append(f"#define L{idx}_IN {n_in}") - L.append(f"#define L{idx}_OUT {n_out}") - L.append(f"static const double W{idx}[{n_in}][{n_out}] = {{") - for row in W: - vals = ", ".join([f"{v:.6f}" for v in row]) - L.append(f" {{{vals}}},") - L.append("};") - bvals = ", ".join([f"{v:.6f}" for v in b]) - L.append(f"static const double B{idx}[{n_out}] = {{{bvals}}};") - L.append("") - - L.append("static inline double relu(double x) { return x > 0 ? x : 0; }") - L.append("") - - if quantize: - # Calculate global max for int8 scaling - max_val = max([np.max(np.abs(w)) for w in layers] + [np.max(np.abs(b)) for b in biases] + [1e-6]) - scale_factor = 127.0 / max_val - L.append(f"/* INT8 Quantization Scale Factor: {scale_factor:.4f} */") - for idx, (W, b) in enumerate(zip(layers, biases)): - n_in, n_out = W.shape - L.append(f"static const int8_t W{idx}[{n_in}][{n_out}] = {{") - for row in W: - vals = ", ".join([str(int(round(v * scale_factor))) for v in row]) - L.append(f" {{{vals}}},") - L.append("};") - bvals = ", ".join([str(int(round(v * scale_factor))) for v in b]) - L.append(f"static const int8_t B{idx}[{n_out}] = {{{bvals}}};") - L.append("") - L.append("static inline int32_t relu_int(int32_t x) { return x > 0 ? x : 0; }") - L.append("") - L.append("int predict(int8_t *input) {") - for idx in range(len(layers)): - n_in = layers[idx].shape[0] - n_out = layers[idx].shape[1] - is_last = (idx == len(layers) - 1) - src = "input" if idx == 0 else f"a{idx-1}" - L.append(f" int32_t a{idx}[{n_out}];") - L.append(f" for (int j = 0; j < {n_out}; j++) {{") - L.append(f" a{idx}[j] = B{idx}[j] * {int(scale_factor)}; /* scale bias */") - L.append(f" for (int i = 0; i < {n_in}; i++) {{") - L.append(f" a{idx}[j] += (int32_t){src}[i] * W{idx}[i][j];") - L.append(f" }}") - if not is_last: - L.append(f" a{idx}[j] = relu_int(a{idx}[j]) / {int(scale_factor)}; /* rescale */") - L.append(f" }}") - last_idx = len(layers) - 1 - last_out = layers[-1].shape[1] - L.append(f" int best = 0;") - L.append(f" for (int c = 1; c < {last_out}; c++) {{") - L.append(f" if (a{last_idx}[c] > a{last_idx}[best]) best = c;") - L.append(f" }}") - L.append(f" return best;") - L.append("}") - else: - for idx, (W, b) in enumerate(zip(layers, biases)): - n_in, n_out = W.shape - L.append(f"#define L{idx}_IN {n_in}") - L.append(f"#define L{idx}_OUT {n_out}") - L.append(f"static const double W{idx}[{n_in}][{n_out}] = {{") - for row in W: - vals = ", ".join([f"{v:.6f}" for v in row]) - L.append(f" {{{vals}}},") - L.append("};") - bvals = ", ".join([f"{v:.6f}" for v in b]) - L.append(f"static const double B{idx}[{n_out}] = {{{bvals}}};") - L.append("") - - L.append("static inline double relu(double x) { return x > 0 ? x : 0; }") - L.append("") - L.append("int predict(double *input) {") - for idx in range(len(layers)): - n_in = layers[idx].shape[0] - n_out = layers[idx].shape[1] - is_last = (idx == len(layers) - 1) - src = "input" if idx == 0 else f"a{idx-1}" - L.append(f" double a{idx}[{n_out}];") - L.append(f" for (int j = 0; j < {n_out}; j++) {{") - L.append(f" a{idx}[j] = B{idx}[j];") - L.append(f" for (int i = 0; i < {n_in}; i++) {{") - L.append(f" a{idx}[j] += {src}[i] * W{idx}[i][j];") - L.append(f" }}") - if not is_last: - L.append(f" a{idx}[j] = relu(a{idx}[j]);") - L.append(f" }}") - - last_idx = len(layers) - 1 - last_out = layers[-1].shape[1] - L.append(f" int best = 0;") - L.append(f" for (int c = 1; c < {last_out}; c++) {{") - L.append(f" if (a{last_idx}[c] > a{last_idx}[best]) best = c;") - L.append(f" }}") - L.append(f" return best;") - L.append("}") - - elif model_name == "knn" and hasattr(model, '_fit_X'): - n_samples = min(model._fit_X.shape[0], 100) - n_feats = model._fit_X.shape[1] - L.append(f"/* KNN Lookup Table: {n_samples} reference samples */") - L.append(f"#define N_NEIGHBORS {model.n_neighbors}") - L.append(f"#define N_SAMPLES {n_samples}") - L.append(f"#define N_FEATURES {n_feats}") - L.append("") - L.append("static const double REF[N_SAMPLES][N_FEATURES] = {") - for row in model._fit_X[:n_samples]: - vals = ", ".join([f"{v:.4f}" for v in row]) - L.append(f" {{{vals}}},") - L.append("};") - L.append("") - labels_str = ", ".join([str(int(l)) for l in model._y[:n_samples]]) - L.append(f"static const int LABELS[N_SAMPLES] = {{{labels_str}}};") - L.append("") - L.append("int predict(double *features) {") - L.append(" double dists[N_SAMPLES];") - L.append(" for (int i = 0; i < N_SAMPLES; i++) {") - L.append(" dists[i] = 0.0;") - L.append(" for (int f = 0; f < N_FEATURES; f++) {") - L.append(" double d = features[f] - REF[i][f];") - L.append(" dists[i] += d * d;") - L.append(" }") - L.append(" }") - L.append(" int votes[10] = {0};") - L.append(" for (int k = 0; k < N_NEIGHBORS; k++) {") - L.append(" int mi = 0;") - L.append(" for (int i = 1; i < N_SAMPLES; i++) {") - L.append(" if (dists[i] < dists[mi]) mi = i;") - L.append(" }") - L.append(" votes[LABELS[mi]]++;") - L.append(" dists[mi] = 1e18;") - L.append(" }") - L.append(" int best = 0;") - L.append(" for (int i = 1; i < 10; i++) {") - L.append(" if (votes[i] > votes[best]) best = i;") - L.append(" }") - L.append(" return best;") - L.append("}") - else: - return {"error": f"Model {model_name} cannot be exported to C."} - - return {"code": "\n".join(L)} +def run_training_background(): + try: + from models import train_models + train_models() + # Force reload in lifespan is tricky, but we can do it via global + global ensemble_system + from ensemble import EnsembleModel + ensemble_system = EnsembleModel() except Exception as e: - return {"error": f"Export failed: {str(e)}"} + print(f"Background training failed: {e}") @app.post("/retrain") -async def retrain(file: UploadFile = File(...)): - data_path = '/app/data/patient_dataset.csv' if os.path.exists('/app/data') else '../data/patient_dataset.csv' if os.path.exists('../data') else 'data/patient_dataset.csv' +async def retrain(background_tasks: BackgroundTasks, file: UploadFile = File(...)): + data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'data', 'patient_dataset.csv') os.makedirs(os.path.dirname(data_path), exist_ok=True) try: @@ -496,7 +240,7 @@ async def retrain(file: UploadFile = File(...)): new_df.columns = [c.strip() for c in new_df.columns] if os.path.exists(data_path): - existing_df = pd.read_csv(data_path) + existing_df = pd.read_csv(data_path, encoding='utf-8') existing_df.columns = [c.strip() for c in existing_df.columns] # Validate schema @@ -518,12 +262,8 @@ async def retrain(file: UploadFile = File(...)): # Save validated and combined dataset combined_df.to_csv(data_path, index=False, encoding='utf-8') - from models import train_models - await asyncio.to_thread(train_models) - - global ensemble_system - ensemble_system = None + background_tasks.add_task(run_training_background) - return {"status": "success", "message": f"Dataset updated (now {len(combined_df)} records) and ensemble retrained successfully!"} + return {"status": "success", "message": f"Dataset updated (now {len(combined_df)} records) and ensemble retrain started in background!"} except Exception as e: return {"error": f"Retraining failed: {str(e)}"} diff --git a/backend/models.py b/backend/models.py index 919b20e7fd8725879d3347df94b94245d388b427..7738da76906c5410afd0d300e852689c60777863 100644 --- a/backend/models.py +++ b/backend/models.py @@ -12,12 +12,12 @@ sys.path.append(os.path.dirname(os.path.abspath(__file__))) from preprocessing import get_train_test_split, resolve_model_dir def train_models(): - data_path = '/app/data/patient_dataset.csv' if os.path.exists('/app/data') else '../data/patient_dataset.csv' if os.path.exists('../data') else 'data/patient_dataset.csv' + data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'data', 'patient_dataset.csv') model_dir = resolve_model_dir() os.makedirs(model_dir, exist_ok=True) print("Loading data...") - df = pd.read_csv(data_path) + df = pd.read_csv(data_path, encoding='utf-8') X_train, X_test, y_train, y_test = get_train_test_split(df) models = { diff --git a/backend/patient_history.db b/backend/patient_history.db index 515864ac4e2600a653efe3e263c87753b32f04ee..3d3a458d1ddb4d3ea42f1b2dd39b8d8ddd5e6831 100644 Binary files a/backend/patient_history.db and b/backend/patient_history.db differ diff --git a/backend/preprocessing.py b/backend/preprocessing.py index 40fb9ace496b02fbfe138d95033e422af15f78f0..90be8393be77e087756ff7ebe9c6aa3baf13f3d3 100644 --- a/backend/preprocessing.py +++ b/backend/preprocessing.py @@ -4,17 +4,18 @@ import os import joblib from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler, LabelEncoder +from sklearn.impute import SimpleImputer def resolve_model_dir(): - # Resolve relative paths universally whether Docker or local execution - return '/app/model' if os.path.exists('/app/model') else '../model' if os.path.exists('../model') else 'model' + return os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'model') def preprocess_data(df, is_training=True): model_dir = resolve_model_dir() scaler_path = f'{model_dir}/scaler.pkl' label_encoder_path = f'{model_dir}/label_encoder.pkl' + imputer_path = f'{model_dir}/imputer.pkl' - columns_to_drop = ['Patient Number', 'Data Accuracy (%)', 'Heart Rate Alert', 'SpO2 Level Alert', 'Blood Pressure Alert', 'Temperature Alert'] + columns_to_drop = ['Patient Number', 'Data Accuracy (%)', 'Heart Rate Alert', 'SpO2 Level Alert', 'Blood Pressure Alert', 'Temperature Alert', 'Fall Detection'] cols_drop = [c for c in columns_to_drop if c in df.columns] X_raw = df.drop(columns=cols_drop) @@ -36,21 +37,30 @@ def preprocess_data(df, is_training=True): else: y = y_raw + # Fix broken encoding column names safely + X_raw.rename(columns=lambda x: x.replace('', '°') if isinstance(x, str) else x, inplace=True) + if 'Fall Detection' in X_raw.columns: X_raw['Fall Detection'] = X_raw['Fall Detection'].map({'Yes': 1, 'No': 0}).fillna(0) - X_raw.fillna(X_raw.mean(), inplace=True) - # Engineered Feature X_raw['Risk_Severity'] = (X_raw['Heart Rate (bpm)'] > 105).astype(int) + (X_raw['SpO2 Level (%)'] < 94).astype(int) continuous_features = ['Heart Rate (bpm)', 'SpO2 Level (%)', 'Systolic Blood Pressure (mmHg)', 'Diastolic Blood Pressure (mmHg)', 'Body Temperature (°C)', 'Risk_Severity'] if is_training: + imputer = SimpleImputer(strategy='mean') + X_raw[continuous_features] = imputer.fit_transform(X_raw[continuous_features]) + joblib.dump(imputer, imputer_path) + scaler = StandardScaler() X_raw[continuous_features] = scaler.fit_transform(X_raw[continuous_features]) joblib.dump(scaler, scaler_path) else: + if os.path.exists(imputer_path): + imputer = joblib.load(imputer_path) + X_raw[continuous_features] = imputer.transform(X_raw[continuous_features]) + if os.path.exists(scaler_path): scaler = joblib.load(scaler_path) X_raw[continuous_features] = scaler.transform(X_raw[continuous_features]) diff --git a/backend/requirements.txt b/backend/requirements.txt index 4eb44e3cd2e868e7d3cc7dafdb5a4628c41365bc..8307feb61f9d3373c4061b8a4fd522b85a7f752f 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -1,9 +1,12 @@ -fastapi==0.103.1 -uvicorn==0.23.2 -pydantic==2.4.2 -scikit-learn==1.3.0 -pandas==2.1.0 -numpy==1.25.2 -shap==0.42.1 -m2cgen==0.10.0 -sqlalchemy +fastapi +uvicorn +pydantic +scikit-learn==1.8.0 +pandas +numpy +shap +m2cgen +huggingface-hub +websockets +aiosqlite +python-multipart diff --git a/backend/simulator.py b/backend/simulator.py new file mode 100644 index 0000000000000000000000000000000000000000..d4cf3149b830956901515d84aac4dd93fd058bea --- /dev/null +++ b/backend/simulator.py @@ -0,0 +1,57 @@ +import asyncio +import random +from typing import AsyncGenerator + +class PatientDataSimulator: + def __init__(self, initial_hr=75.0, initial_spo2=98.0, initial_sys=120.0, initial_dia=80.0, initial_temp=37.0): + self.hr = initial_hr + self.spo2 = initial_spo2 + self.sys_bp = initial_sys + self.dia_bp = initial_dia + self.temp = initial_temp + + self.target_hr = self.hr + self.target_spo2 = self.spo2 + self.target_sys = self.sys_bp + self.target_dia = self.dia_bp + + def _shift_state(self): + # 5% chance every second to shift patient state + if random.random() < 0.05: + state = random.choice(["normal", "normal", "normal", "asthma", "hypertension", "heart_disease", "diabetes"]) + if state == "normal": + self.target_hr, self.target_spo2, self.target_sys, self.target_dia = 75.0, 98.0, 120.0, 80.0 + elif state == "asthma": + self.target_hr, self.target_spo2, self.target_sys, self.target_dia = 115.0, 88.0, 135.0, 85.0 + elif state == "hypertension": + self.target_hr, self.target_spo2, self.target_sys, self.target_dia = 90.0, 97.0, 175.0, 105.0 + elif state == "heart_disease": + self.target_hr, self.target_spo2, self.target_sys, self.target_dia = 135.0, 91.0, 150.0, 95.0 + elif state == "diabetes": + self.target_hr, self.target_spo2, self.target_sys, self.target_dia = 85.0, 96.0, 140.0, 90.0 + + def _step_physics(self): + # Interpolate towards target with noise + self.hr += (self.target_hr - self.hr) * 0.1 + random.uniform(-2, 2) + self.spo2 += (self.target_spo2 - self.spo2) * 0.1 + random.uniform(-0.5, 0.5) + self.sys_bp += (self.target_sys - self.sys_bp) * 0.1 + random.uniform(-1, 1) + self.dia_bp += (self.target_dia - self.dia_bp) * 0.1 + random.uniform(-1, 1) + self.temp = max(36.0, min(39.0, self.temp + random.uniform(-0.1, 0.1))) + + # Clamp values + self.hr = max(50.0, min(180.0, self.hr)) + self.spo2 = max(80.0, min(100.0, self.spo2)) + + async def run(self) -> AsyncGenerator[dict, None]: + while True: + self._shift_state() + self._step_physics() + + yield { + "Heart_Rate": round(self.hr, 1), + "SpO2_Level": round(self.spo2, 1), + "Systolic_BP": round(self.sys_bp, 1), + "Diastolic_BP": round(self.dia_bp, 1), + "Body_Temp": round(self.temp, 1) + } + await asyncio.sleep(1.0) diff --git a/backend/tests/test_api.py b/backend/tests/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..56755a9bc07e340d8672394c8b272e359519d2e2 --- /dev/null +++ b/backend/tests/test_api.py @@ -0,0 +1,29 @@ +import pytest +from fastapi.testclient import TestClient +import sys +import os + +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from main import app + +client = TestClient(app) + +def test_history_endpoint(): + response = client.get("/history") + assert response.status_code == 200 + assert isinstance(response.json(), list) + +def test_dataset_endpoint(): + response = client.get("/dataset") + assert response.status_code == 200 + assert isinstance(response.json(), list) + +def test_export_tinyml_random_forest(): + response = client.get("/export_tinyml?model_name=rf") + + if response.status_code == 200: + data = response.json() + if "error" in data: + assert "not found" in data["error"].lower() + else: + assert "code" in data or "c_code" in data diff --git a/docker-compose.yml b/docker-compose.yml index a25242dc99b8e7f8ef3232ec81acab6399d04eb0..9f0391718eb4fbdb6e81604dfceff389935543b8 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -2,22 +2,28 @@ version: '3.8' services: backend: - build: - context: . - dockerfile: backend/Dockerfile + build: + context: ./backend + dockerfile: Dockerfile ports: - "8000:8000" + environment: + - HF_TOKEN=${HF_TOKEN:-} volumes: - - ./model:/app/model - - ./data:/app/data + # Mount DB for persistence (in production use a volume, here bind-mount is okay for dev) + - backend_data:/app frontend: build: - context: . - dockerfile: frontend/Dockerfile + context: ./frontend + dockerfile: Dockerfile ports: - - "8501:8501" + - "3000:3000" environment: - - API_URL=http://backend:8000 + # Next.js will build this at build-time, but here it is for client-side API requests + - NEXT_PUBLIC_API_URL=http://localhost:8000 depends_on: - backend + +volumes: + backend_data: diff --git a/frontend/Dockerfile b/frontend/Dockerfile index 4961c1ad0afd1f9e3ae732c9eddf3b7a5d6eb02c..f3c472cfb229344d720341d59bfd5f796dc2c177 100644 --- a/frontend/Dockerfile +++ b/frontend/Dockerfile @@ -1,11 +1,20 @@ -FROM python:3.11-slim +FROM node:20-alpine AS deps WORKDIR /app +COPY package.json package-lock.json* ./ +RUN npm ci -RUN apt-get update && apt-get install -y graphviz && rm -rf /var/lib/apt/lists/* - -COPY frontend/requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt +FROM node:20-alpine AS builder +WORKDIR /app +COPY --from=deps /app/node_modules ./node_modules COPY . . -WORKDIR /app/frontend -EXPOSE 8501 -CMD sh -c "streamlit run Home.py --server.port=${PORT:-8501} --server.address=0.0.0.0" +RUN npm run build + +FROM node:20-alpine AS runner +WORKDIR /app +ENV NODE_ENV=production +COPY --from=builder /app/public ./public +COPY --from=builder /app/.next/standalone ./ +COPY --from=builder /app/.next/static ./.next/static +EXPOSE 3000 +ENV PORT=3000 +CMD ["node", "server.js"] diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e215bc4ccf138bbc38ad58ad57e92135484b3c0f --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/frontend/eslint.config.mjs b/frontend/eslint.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..05e726d1b4201bc8c7716d2b058279676582e8c0 --- /dev/null +++ b/frontend/eslint.config.mjs @@ -0,0 +1,18 @@ +import { defineConfig, globalIgnores } from "eslint/config"; +import nextVitals from "eslint-config-next/core-web-vitals"; +import nextTs from "eslint-config-next/typescript"; + +const eslintConfig = defineConfig([ + ...nextVitals, + ...nextTs, + // Override default ignores of eslint-config-next. + globalIgnores([ + // Default ignores of eslint-config-next: + ".next/**", + "out/**", + "build/**", + "next-env.d.ts", + ]), +]); + +export default eslintConfig; diff --git a/frontend/next.config.ts b/frontend/next.config.ts new file mode 100644 index 0000000000000000000000000000000000000000..39ad79f8cf4f875723ce5436724c034718aba090 --- /dev/null +++ b/frontend/next.config.ts @@ -0,0 +1,11 @@ +import type { NextConfig } from "next"; + +const nextConfig: NextConfig = { + output: "export", + // basePath: "/TinyML-Heart-Health-Monitoring-Dashboard", // Uncomment if hosted on a subpath in GitHub Pages + images: { + unoptimized: true, // Required for static export + } +}; + +export default nextConfig; diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..10ffe841c59f5b428be5dceb8143b9187225bd7f --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,6944 @@ +{ + "name": "web", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "web", + "version": "0.1.0", + "dependencies": { + "framer-motion": "^13.4.4", + "lucide-react": "^1.48.0", + "next": "16.3.6", + "react": "19.2.8", + "react-dom": "19.2.8" + }, + "devDependencies": { + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", 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"version": "0.1.0", + "private": true, + "scripts": { + "dev": "next dev", + "build": "next build", + "start": "next start", + "lint": "eslint" + }, + "dependencies": { + "framer-motion": "^13.4.4", + "lucide-react": "^1.48.0", + "next": "16.3.6", + "react": "19.2.8", + "react-dom": "19.2.8" + }, + "devDependencies": { + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", + "@types/react-dom": "^19", + "eslint": "^9", + "eslint-config-next": "16.3.6", + "tailwindcss": "^4", + "typescript": "^5" + } +} diff --git a/frontend/postcss.config.mjs b/frontend/postcss.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..61e36849cf7cfa9f1f71b4a3964a4953e3e243d3 --- /dev/null +++ b/frontend/postcss.config.mjs @@ -0,0 +1,7 @@ +const config = { + plugins: { + "@tailwindcss/postcss": {}, + }, +}; + +export default config; diff --git a/frontend/public/file.svg b/frontend/public/file.svg new file mode 100644 index 0000000000000000000000000000000000000000..004145cddf3f9db91b57b9cb596683c8eb420862 --- /dev/null +++ b/frontend/public/file.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/globe.svg b/frontend/public/globe.svg new file mode 100644 index 0000000000000000000000000000000000000000..567f17b0d7c7fb662c16d4357dd74830caf2dccb --- /dev/null +++ b/frontend/public/globe.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/next.svg b/frontend/public/next.svg new file mode 100644 index 0000000000000000000000000000000000000000..5174b28c565c285e3e312ec5178be64fbeca8398 --- /dev/null +++ b/frontend/public/next.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/vercel.svg b/frontend/public/vercel.svg new file mode 100644 index 0000000000000000000000000000000000000000..77053960334e2e34dc584dea8019925c3b4ccca9 --- /dev/null +++ b/frontend/public/vercel.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/window.svg b/frontend/public/window.svg new file mode 100644 index 0000000000000000000000000000000000000000..b2b2a44f6ebc70c450043c05a002e7a93ba5d651 --- /dev/null +++ b/frontend/public/window.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/app/analytics/page.tsx b/frontend/src/app/analytics/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..bfd8170da8e848999ce5cf02545b9ac92704ad0b --- /dev/null +++ b/frontend/src/app/analytics/page.tsx @@ -0,0 +1,135 @@ +"use client"; + +import { motion } from "framer-motion"; +import { Database, LineChart, Table2, Layers } from "lucide-react"; +import Link from "next/link"; +import { useEffect, useState } from "react"; + +export default function Analytics() { + const [dataset, setDataset] = useState([]); + const [loading, setLoading] = useState(true); + + useEffect(() => { + fetch((process.env.NEXT_PUBLIC_API_URL || "http://localhost:8000") + "/dataset") + .then((res) => res.json()) + .then((data) => { + setDataset(data); + setLoading(false); + }) + .catch((err) => { + console.error(err); + setLoading(false); + }); + }, []); + + const spring = { + type: "spring" as const, + stiffness: 100, + damping: 20, + }; + + const columns = dataset.length > 0 ? Object.keys(dataset[0]) : []; + + return ( +
+ + +
+
+ + +
+

+ Dataset Explorer +

+

+ Preview the current ground-truth telemetry used for training the TinyML models. +

+
+
+
+ + {columns.length} Features +
+
+ + {dataset.length} Rows +
+
+ + + +
+

+ Raw Tabular View +

+
+
+ + + + {columns.map(col => ( + + ))} + + + + {loading ? ( + + + + ) : dataset.length === 0 ? ( + + + + ) : ( + dataset.slice(0, 100).map((row, i) => ( + + {columns.map(col => ( + + ))} + + )) + )} + +
+ {col} +
+ Loading dataset... +
+ No dataset available. Upload one in MLOps. +
+ {typeof row[col] === 'number' && !Number.isInteger(row[col]) + ? row[col].toFixed(2) + : String(row[col])} +
+ {!loading && dataset.length > 100 && ( +
+ Showing first 100 rows. +
+ )} +
+ +
+
+ ); +} diff --git a/frontend/src/app/architecture/page.tsx b/frontend/src/app/architecture/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..cb664913135f1233c414122818a0af898549620d --- /dev/null +++ b/frontend/src/app/architecture/page.tsx @@ -0,0 +1,87 @@ +"use client"; + +import Link from "next/link"; + +export default function Architecture() { + return ( +
+ {/* Top Nav (Mechanical) */} + + +
+
+
+
+ /// DOCS_AND_SPECS +
+

+ SYSTEM
ARCHITECTURE +

+
+
+ +
+

+ [ 01 ] Client Plane (Frontend) +

+

+ Engineered using Next.js 16 (App Router) and React 19. The UI adheres strictly to an Industrial Brutalist aesthetic utilizing hard grids, monochromatic blueprints, and high-contrast hazard states. Native WebSockets handle high-frequency telemetry without HTTP overhead, rendered via hardware-accelerated SVG paths in framer-motion. +

+
+ +
+

+ [ 02 ] Core Plane (Backend) +

+

+ Built on FastAPI (Python 3.10+) maximizing async throughput. A state-machine driven data simulator generates correlated, volatile telemetry streams representing clinical episodes. Storage employs aiosqlite for non-blocking database writes to track historical classifications without stalling the event loop. +

+
+ +
+

+ [ 03 ] Intelligence Pipeline (MLOps) +

+

+ Live patient data is standardized and imputed using a persistent SimpleImputer and StandardScaler. Clinical precision is achieved via a 5-model soft-voting ensemble. The `/retrain` pipeline allows dynamic CSV uploads, imputes missing data, trains models in background threads, and hot-swaps the EnsembleModel globally with zero downtime. +

+
+ +
+

+ [ 04 ] Edge Compilation (TinyML) +

+

+ The backend transpiles trained SciKit-Learn tree models directly into standalone zero-dependency C-headers. Floating-point thresholds are mathematically quantized to INT8, effectively shrinking the flashed payload by ~75%. Designed to execute directly on MCU SRAM without dynamic memory allocation. +

+
+
+ +
+
+
+ © 2026 HEARTFLOW_OS. TELEMETRY_SYS. +
+
+ ARCHITECTURE.MD + API_DOCS + GITHUB_REPO +
+
+
+
+ ); +} diff --git a/frontend/src/app/dashboard/page.tsx b/frontend/src/app/dashboard/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..0428ae953e14e545b6c20cb1b6c2a6319c6c91ce --- /dev/null +++ b/frontend/src/app/dashboard/page.tsx @@ -0,0 +1,218 @@ +"use client"; + +import { motion } from "framer-motion"; +import Link from "next/link"; +import { useTelemetry } from "@/context/TelemetryContext"; + +export default function Dashboard() { + const { data, stream } = useTelemetry(); + + return ( +
+ {/* Top Nav (Mechanical) */} + + +
+
+
+
+ /// TELEMETRY_STREAM_ACTIVE +
+

+ PATIENT
TELEMETRY +

+
+
+
+ STATUS: {data ? "CONNECTED" : "AWAITING SIGNAL"} +
+
+
+ + {/* Dashboard Grid - Brutalist Tables */} +
+ + {/* Main Status Panel */} +
+
+

{'< LIVE_VITALS >'}

+ {data?.prediction ? ( + (() => { + const getStatusStyles = (label: string) => { + const normalized = (label || "").toLowerCase(); + if (normalized === 'healthy' || normalized === 'normal') return "bg-[#d1fae5] text-[#065f46] border-[#065f46]"; + if (normalized.includes('heart')) return "bg-[#fef08a] text-[#854d0e] border-[#854d0e]"; + if (normalized.includes('asthma')) return "bg-[#e0f2fe] text-[#075985] border-[#075985]"; + if (normalized.includes('hypertension')) return "bg-[#f3e8ff] text-[#6b21a8] border-[#6b21a8]"; + if (normalized.includes('diabetes')) return "bg-[#ffedd5] text-[#9a3412] border-[#9a3412]"; + return "bg-hazard text-white border-hazard"; + }; + return ( +
+ {data.prediction.is_at_risk ? '[!]' : '[OK]'} {data.prediction.label} ({(data.prediction.confidence * 100).toFixed(1)}%) +
+ ); + })() + ) : ( +
+ INITIALIZING... +
+ )} +
+ +
+ + + + +
+ + {/* 2-Column Mini Graphs */} +
+
HR_STREAM
+
TEMP_STREAM
+
+ + {/* Diagnostic Probabilities */} +
+
+

{'< DIAGNOSTIC_PROBABILITIES >'}

+
CLASS / LIKELIHOOD
+
+
+ {data?.prediction?.disease_probs ? ( + Object.entries(data.prediction.disease_probs) + .sort((a, b) => (b[1] as number) - (a[1] as number)) + .map(([disease, prob]) => { + const pct = ((prob as number) * 100).toFixed(1); + const isRisk = (prob as number) > 0.4 && disease.toLowerCase() !== 'normal' && disease.toLowerCase() !== 'healthy'; + return ( +
+
+ {disease} + {pct}% +
+
+ {/* Blueprint tick marks */} +
+ +
+
+ ) + }) + ) : ( +
+ AWAITING CLASSIFICATION DATA... +
+ )} +
+
+ +
+
+
+
+ ); +} + +function VitalCard({ label, value, unit }: { label: string, value: string | number | null | undefined, unit: string }) { + return ( +
+
+ {label} +
+
+
+ {value !== null && value !== undefined ? value : "---"} +
+
+ [{unit}] +
+
+
+ ); +} + +function TelemetryGraph({ stream, min, max, color, fill }: { stream: number[], min: number, max: number, color: string, fill: string }) { + const range = max - min; + + const points = stream.map((val, i) => { + const x = (i / (stream.length - 1)) * 100; + const y = Math.max(0, Math.min(100, 100 - (((val - min) / range) * 100))); + return `${x},${y}`; + }); + + const firstY = points[0] ? points[0].split(',')[1] : 100; + const pathD = `M 0,100 L 0,${firstY} L ${points.join(" L ")} L 100,100 Z`; + const lineD = `M 0,${firstY} L ${points.join(" L ")}`; + const lastPoint = points[points.length - 1]; + const lastY = lastPoint ? lastPoint.split(',')[1] : 100; + + return ( +
+ {/* Blueprint Grid lines */} +
+ + {/* Signal trace */} +
+ + {/* Fill Area */} + + {/* Stroke Line (Glow) */} + + {/* Leading Dot */} + + +
+ + {/* Sweeping Radar Line */} + + + {/* Terminal Scanline Overlay */} +
+
+ ); +} diff --git a/frontend/src/app/edge/page.tsx b/frontend/src/app/edge/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..ebb610da31a5bf1fcb32ecabbfff0de6fc417d81 --- /dev/null +++ b/frontend/src/app/edge/page.tsx @@ -0,0 +1,181 @@ +"use client"; + +import Link from "next/link"; +import { useState } from "react"; + +export default function EdgeDeployment() { + const [model, setModel] = useState("rf"); + const [code, setCode] = useState(""); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(""); + + const handleExport = async () => { + setLoading(true); + setCode(""); + setError(""); + + try { + const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL || "http://localhost:8000"}/export_tinyml?model_name=${model}`); + if (!res.ok) throw new Error("Failed to export model"); + + const data = await res.json(); + if (data.error) throw new Error(data.error); + + setCode(data.code || data.c_code || "/* No C-code returned */"); + } catch (err: any) { + setError(err.message || "Unknown error occurred"); + } finally { + setLoading(false); + } + }; + + const handleDownload = () => { + if (!code) return; + const blob = new Blob([code], { type: "text/x-csrc" }); + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + a.download = `model_${model}.h`; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(url); + }; + + return ( +
+ {/* Top Nav (Mechanical) */} + + +
+
+
+
+ /// C_COMPILER_TOOLCHAIN +
+

+ EDGE
DEPLOYMENT +

+
+
+ +
+ {/* Config Panel */} +
+
+

{'< BUILD_CONFIG >'}

+

Select target architecture.

+
+ +
+
+ + +
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+
+ + {/* Output Panel */} +
+
+
+

+ {'< OUTPUT_BUFFER >'} +

+ {code && ( +
+ SIZE: {code.length} B ({(code.length / 1024).toFixed(2)} KB) + LINES: {code.split('\n').length} + MD5_CHK: 0x{Array.from(code).reduce((h, c) => Math.imul(31, h) + c.charCodeAt(0) | 0, 0).toString(16).toUpperCase().padStart(8, '0').slice(-8)} +
+ )} +
+ {code && ( + + )} +
+ +
+ {error ? ( +
+ [ERR] {error} +
+ ) : code ? ( + 
+                  {code}
+
+ ) : ( +
+ AWAITING_COMPILATION... +
+ )} +
+
+
+
+
+ ); +} diff --git a/frontend/src/app/favicon.ico b/frontend/src/app/favicon.ico new file mode 100644 index 0000000000000000000000000000000000000000..718d6fea4835ec2d246af9800eddb7ffb276240c Binary files /dev/null and b/frontend/src/app/favicon.ico differ diff --git a/frontend/src/app/globals.css b/frontend/src/app/globals.css new file mode 100644 index 0000000000000000000000000000000000000000..695791ae20340d849d6e14a3c372466a4303c5ac --- /dev/null +++ b/frontend/src/app/globals.css @@ -0,0 +1,60 @@ +@import "tailwindcss"; + +:root { + --canvas-cream: #FBFBFA; + --ink-black: #141413; + --slate-gray: #696969; + --hazard-red: #9F2F2D; + --white: #FFFFFF; + --whisper: #EAEAEA; + --risk-bg: #FDEBEC; + --healthy-bg: #EDF3EC; + --healthy-text: #346538; + --soft-bone: #F4F4F4; +} + +@theme inline { + --color-canvas-cream: var(--canvas-cream); + --color-ink: var(--ink-black); + --color-slate: var(--slate-gray); + --color-hazard: var(--hazard-red); + --color-whisper: var(--whisper); + --color-risk-bg: var(--risk-bg); + --color-healthy-bg: var(--healthy-bg); + --color-healthy-text: var(--healthy-text); + --color-soft-bone: var(--soft-bone); + + --font-sans: var(--font-sans); + --font-mono: var(--font-mono); +} + +* { + border-radius: 0 !important; +} + +body { + background-color: var(--canvas-cream); + color: var(--ink-black); + font-family: var(--font-sans); + font-weight: 500; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +/* Macro Typography Scaling */ +h1 { + font-family: var(--font-sans); + font-weight: 800; + color: var(--ink-black); + letter-spacing: -0.04em; + line-height: 0.9; + text-transform: uppercase; +} + +h2, h3, h4, h5, h6 { + font-family: var(--font-sans); + font-weight: 700; + color: var(--ink-black); + letter-spacing: -0.02em; + text-transform: uppercase; +} diff --git a/frontend/src/app/history/page.tsx b/frontend/src/app/history/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..79b1f5153508d40e5e4ad5beeb1616b5fd100e96 --- /dev/null +++ b/frontend/src/app/history/page.tsx @@ -0,0 +1,142 @@ +"use client"; + +import Link from "next/link"; +import { useEffect, useState } from "react"; + +interface HistoryRecord { + id: number; + timestamp: string; + heart_rate: number; + spo2: number; + sys_bp: number; + dia_bp: number; + body_temp?: number; + temp?: number; + prediction?: string; + prediction_label?: string; + confidence: number; +} + +export default function PatientHistory() { + const [records, setRecords] = useState([]); + const [loading, setLoading] = useState(true); + + useEffect(() => { + fetch((process.env.NEXT_PUBLIC_API_URL || "http://localhost:8000") + "/history") + .then((res) => res.json()) + .then((data) => { + setRecords(data); + setLoading(false); + }) + .catch((err) => { + console.error(err); + setLoading(false); + }); + }, []); + + return ( +
+ {/* Top Nav (Mechanical) */} + + +
+
+
+
+ /// HISTORICAL_RECORDS +
+

+ PREDICTION
LOG +

+
+
+
+ ROWS: {records.length} +
+
+
+ +
+
+ + + + + + + + + + + + + + {loading ? ( + + + + ) : records.length === 0 ? ( + + + + ) : ( + records.map((record) => { + const date = new Date(record.timestamp); + const predLabel = record.prediction_label || record.prediction || "UNKNOWN"; + const isRisk = predLabel.toLowerCase().includes("risk") || predLabel.toLowerCase() !== "healthy"; + const temperature = record.temp || record.body_temp || "—"; + + const getStatusStyles = (label: string) => { + const normalized = label.toLowerCase(); + if (normalized === 'healthy') return "bg-[#d1fae5] text-[#065f46]"; // Light green + if (normalized.includes('heart')) return "bg-[#fef08a] text-[#854d0e]"; // Light yellow + if (normalized.includes('asthma')) return "bg-[#e0f2fe] text-[#075985]"; // Light blue + if (normalized.includes('hypertension')) return "bg-[#f3e8ff] text-[#6b21a8]"; // Light purple + if (normalized.includes('diabetes')) return "bg-[#ffedd5] text-[#9a3412]"; // Light orange + return "bg-hazard/20 text-hazard"; // Default risk red + }; + + return ( + + + + + + + + + + ); + }) + )} + +
TIMESTAMPHRSPO2BPTEMPSTATUSCONFIDENCE
+ QUERYING_DATABASE... +
+ NO_RECORDS_FOUND. +
+ {date.toLocaleString()} + {record.heart_rate}{record.spo2}{record.sys_bp}/{record.dia_bp}{temperature} + {isRisk ? '[!] ' : '✓ '}{predLabel} + + {(record.confidence * 100).toFixed(1)}% +
+
+
+
+
+ ); +} diff --git a/frontend/src/app/layout.tsx b/frontend/src/app/layout.tsx new file mode 100644 index 0000000000000000000000000000000000000000..cc4f1075f8a6468497261ebeaf70e0edf1ba3a9a --- /dev/null +++ b/frontend/src/app/layout.tsx @@ -0,0 +1,34 @@ +import type { Metadata } from "next"; +import { Geist, Geist_Mono } from "next/font/google"; +import "./globals.css"; +import { TelemetryProvider } from "@/context/TelemetryContext"; + +const geistSans = Geist({ + variable: "--font-sans", + subsets: ["latin"], +}); + +const geistMono = Geist_Mono({ + variable: "--font-mono", + subsets: ["latin"], +}); + +export const metadata: Metadata = { + title: "TinyML Heart Health", + description: "Cardiovascular monitoring dashboard", +}; + +export default function RootLayout({ children }: { children: React.ReactNode }) { + return ( + + + + {children} + + + + ); +} diff --git a/frontend/src/app/mlops/page.tsx b/frontend/src/app/mlops/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..f2acada5c9ebcf374ce228f42e652257df2d9744 --- /dev/null +++ b/frontend/src/app/mlops/page.tsx @@ -0,0 +1,118 @@ +"use client"; + +import Link from "next/link"; +import { useState } from "react"; + +export default function MLOps() { + const [file, setFile] = useState(null); + const [status, setStatus] = useState<"idle" | "uploading" | "success" | "error">("idle"); + const [message, setMessage] = useState(""); + + const handleUpload = async () => { + if (!file) return; + setStatus("uploading"); + + const formData = new FormData(); + formData.append("file", file); + + try { + const res = await fetch((process.env.NEXT_PUBLIC_API_URL || "http://localhost:8000") + "/retrain", { + method: "POST", + body: formData, + }); + const data = await res.json(); + + if (data.error) { + setStatus("error"); + setMessage(data.error); + } else { + setStatus("success"); + setMessage(data.message); + } + } catch (err: any) { + setStatus("error"); + setMessage(err.message || "Upload failed"); + } + }; + + return ( +
+ {/* Top Nav (Mechanical) */} + + +
+
+
+
+ /// PIPELINE_CONTROL +
+

+ MODEL
OPERATIONS +

+
+
+ +
+
+

{'< BATCH_RETRAIN >'}

+

Append telemetry data and rebuild the edge ensemble.

+
+ +
+ + +
+
+ {status === "uploading" && PROCESSING...} + {status === "error" && ERR: {message}} + {status === "success" && SYS: {message}} +
+ + +
+
+
+
+
+ ); +} diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..7dc32548bef0f58063e0103be3d008f7984332b8 --- /dev/null +++ b/frontend/src/app/page.tsx @@ -0,0 +1,115 @@ +"use client"; + +import { motion } from "framer-motion"; +import Link from "next/link"; + +export default function Home() { + return ( +
+ {/* Top Nav (Mechanical) */} + + + {/* Hero Section */} +
+
+
+ /// MODEL_V2.0 + + INT8_QUANTIZED +
+ +

+ CLINICAL
+ INFERENCE
+ AT THE EDGE. +

+ +

+ An ensemble intelligence pipeline engineered for resource-constrained microcontrollers. Deploy robust cardiovascular telemetry analysis directly to the silicon. +

+ +
+ + [ INITIATE_STREAM ] + + + C_COMPILER >> + +
+
+ + {/* Feature Grid */} +
+
+
FEAT.01
+
+

+ INT8 Quantization +

+

+ Weights scaled to 8-bit integers. 75% payload reduction for ESP32 constraint matrices. +

+
+
+ +
+
FEAT.02
+
+

+ Ensemble Architecture +

+

+ RF, KNN, and SVM combined with a meta-classifier for clinical-grade precision. +

+
+
+ +
+
FEAT.03
+
+

+ Zero-Dep Export +

+

+ Compile trained models directly to standalone C headers. Zero dynamic allocation. +

+
+
+
+
+ + {/* Minimal Footer */} +
+
+
+ © 2026 HEARTFLOW_OS. TELEMETRY_SYS. +
+
+ ARCHITECTURE.MD + API_DOCS + GITHUB_REPO +
+
+
+
+ ); +} diff --git a/frontend/src/app/simulator/page.tsx b/frontend/src/app/simulator/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..7b361219d74218d64305b2afbba49258f5e75e8e --- /dev/null +++ b/frontend/src/app/simulator/page.tsx @@ -0,0 +1,246 @@ +"use client"; + +import { motion } from "framer-motion"; +import Link from "next/link"; +import { useState, useEffect } from "react"; +import ShapChart from "@/components/ShapChart"; + +export default function Analysis() { + const [formData, setFormData] = useState({ + Heart_Rate: 75, + SpO2_Level: 98, + Systolic_BP: 120, + Diastolic_BP: 80, + Body_Temp: 37.0 + }); + + useEffect(() => { + setFormData({ + Heart_Rate: Math.floor(Math.random() * (140 - 60 + 1)) + 60, + SpO2_Level: Math.floor(Math.random() * (100 - 90 + 1)) + 90, + Systolic_BP: Math.floor(Math.random() * (160 - 90 + 1)) + 90, + Diastolic_BP: Math.floor(Math.random() * (100 - 60 + 1)) + 60, + Body_Temp: Number((Math.random() * (39.5 - 36.0) + 36.0).toFixed(1)) + }); + }, []); + + const [submittedData, setSubmittedData] = useState(null); + const [prediction, setPrediction] = useState(null); + const [loading, setLoading] = useState(false); + + const handlePredict = async () => { + setLoading(true); + setPrediction(null); + setSubmittedData(null); + + try { + const res = await fetch((process.env.NEXT_PUBLIC_API_URL || "http://localhost:8000") + "/predict", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify(formData) + }); + const data = await res.json(); + setPrediction(data); + setSubmittedData(formData); + } catch (err) { + console.error(err); + } finally { + setLoading(false); + } + }; + + return ( +
+ {/* Top Nav (Mechanical) */} + + +
+
+
+
+ /// EXPLAINABLE_AI_MODULE +
+

+ MODEL
SIMULATOR +

+
+
+
+ MANUAL OVERRIDE. INPUT CUSTOM TELEMETRY TO TEST ENSEMBLE CLASSIFIER AND VIEW SHAPLEY FEATURE IMPORTANCE VECTORS. +
+
+
+ +
+ + {/* Input Panel */} +
+
+

{'< MANUAL_INPUT_MATRIX >'}

+
+ +
+ setFormData({...formData, Heart_Rate: v})} /> + setFormData({...formData, SpO2_Level: v})} /> + setFormData({...formData, Systolic_BP: v})} /> + setFormData({...formData, Diastolic_BP: v})} /> + setFormData({...formData, Body_Temp: v})} /> + + + + {/* Terminal Readout to fill empty space */} +
+ {/* CRT Scanline Overlay */} +
+ +
+ RAW_PAYLOAD_PREVIEW + {JSON.stringify(formData).length} BYTES // SYS_READY +
+ +
+ 
+{`{
+  "Heart_Rate": ${formData.Heart_Rate},
+  "SpO2_Level": ${formData.SpO2_Level},
+  "Systolic_BP": ${formData.Systolic_BP},
+  "Diastolic_BP": ${formData.Diastolic_BP},
+  "Body_Temp": ${formData.Body_Temp.toFixed(1)}
+}`}
+                    
+
+
+
+
+
+ + {/* Results Panel */} +
+
+

{'< INFERENCE_RESULTS_&_XAI >'}

+
+ +
+ {/* Blueprint Grid Background */} +
+ +
+ {prediction ? ( + <> +
+
MODEL_OUTPUT
+ + {(() => { + const getStatusStyles = (label: string) => { + const normalized = (label || "").toLowerCase(); + if (normalized === 'healthy' || normalized === 'normal') return "bg-[#d1fae5] text-[#065f46] border-[#065f46]"; + if (normalized.includes('heart')) return "bg-[#fef08a] text-[#854d0e] border-[#854d0e]"; + if (normalized.includes('asthma')) return "bg-[#e0f2fe] text-[#075985] border-[#075985]"; + if (normalized.includes('hypertension')) return "bg-[#f3e8ff] text-[#6b21a8] border-[#6b21a8]"; + if (normalized.includes('diabetes')) return "bg-[#ffedd5] text-[#9a3412] border-[#9a3412]"; + return "bg-hazard text-white border-hazard"; + }; + + return ( +
+
+ {prediction.prediction === 1 ? `[!]_${prediction.prediction_label.replace(" ", "_")}` : 'NORMAL'} +
+
+ CONFIDENCE: {(prediction.probability * 100).toFixed(1)}% +
+
+ ); + })()} + + {/* Ensemble Diagnostics */} + {prediction.model_outputs && ( +
+
+ {'< ENSEMBLE_STACK_DIAGNOSTICS >'} + WEIGHTED_VOTING +
+
+ {Object.keys(prediction.model_outputs).map(modelName => ( +
+
[{modelName}]
+
+ {prediction.model_outputs[modelName] !== 'Healthy' ? '! ' + prediction.model_outputs[modelName] : 'OK Healthy'} +
+
+ CONF: {(prediction.model_probs[modelName] * 100).toFixed(1)}% +
+
+ W: {prediction.weights[modelName].toFixed(2)} +
+
+ ))} +
+
+ )} +
+ +
+
SHAPLEY_ADDITIVE_EXPLANATIONS
+
+ +
+
+ + ) : ( +
+
+ AWAITING_INPUT... +
+
+ )} +
+
+
+ +
+
+
+ ); +} + +function InputControl({ label, value, min, max, step = 1, onChange }: any) { + return ( +
+
+ + {value} +
+
+ onChange(Number(e.target.value))} + className="w-full appearance-none h-4 bg-ink/10 outline-none [&::-webkit-slider-thumb]:appearance-none [&::-webkit-slider-thumb]:w-6 [&::-webkit-slider-thumb]:h-8 [&::-webkit-slider-thumb]:bg-ink [&::-webkit-slider-thumb]:cursor-pointer relative z-10 block" + /> +
+
+ ); +} diff --git a/frontend/src/components/ShapChart.tsx b/frontend/src/components/ShapChart.tsx new file mode 100644 index 0000000000000000000000000000000000000000..62d25dc0dd7e0d86e55a28c063bf17894a14e2d1 --- /dev/null +++ b/frontend/src/components/ShapChart.tsx @@ -0,0 +1,101 @@ +"use client"; + +import { motion } from "framer-motion"; +import { useEffect, useState } from "react"; +import { Loader2 } from "lucide-react"; + +export default function ShapChart({ patientData }: { patientData: any }) { + const [shapData, setShapData] = useState<{feature_names: string[], shap_values: number[]} | null>(null); + const [loading, setLoading] = useState(false); + + useEffect(() => { + if (!patientData) return; + + const fetchExplain = async () => { + setLoading(true); + try { + const res = await fetch((process.env.NEXT_PUBLIC_API_URL || "http://localhost:8000") + "/explain", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify(patientData) + }); + const data = await res.json(); + if (data.shap_values) { + setShapData(data); + } + } catch (err) { + console.error("SHAP fetch error", err); + } finally { + setLoading(false); + } + }; + + const timeout = setTimeout(fetchExplain, 2500); + return () => clearTimeout(timeout); + }, [patientData]); + + if (!shapData) { + return ( +
+ {loading ? ( + <> + + COMPUTING_SHAP_VECTORS... + + ) : ( + AWAITING_TELEMETRY... + )} +
+ ); + } + + // Sort by absolute magnitude of SHAP value + const items = shapData.feature_names.map((name, i) => ({ + name: name.toUpperCase(), + value: shapData.shap_values[i], + abs: Math.abs(shapData.shap_values[i]) + })).sort((a, b) => b.abs - a.abs); // show ALL features + + const maxAbs = Math.max(...items.map(i => i.abs), 0.1); + + return ( +
+ {items.map((item, idx) => { + const isPositive = item.value > 0; + const widthPercent = (item.abs / maxAbs) * 50; + + return ( +
+
+ {item.name} + + {item.value > 0 ? "+" : ""}{item.value.toFixed(3)} + +
+ + {/* Brutalist Chart Bar */} +
+ {/* Midline (zero axis) */} +
+ + {/* Animated Value Bar */} + +
+
+ ); + })} +
+ ); +} diff --git a/frontend/src/context/TelemetryContext.tsx b/frontend/src/context/TelemetryContext.tsx new file mode 100644 index 0000000000000000000000000000000000000000..f110986ee84c8c5dc2df1a10619fbc5e7adf4d9c --- /dev/null +++ b/frontend/src/context/TelemetryContext.tsx @@ -0,0 +1,52 @@ +"use client"; + +import { createContext, useContext, useEffect, useState } from "react"; + +type StreamState = { + hr: number[]; + temp: number[]; +}; + +type TelemetryContextType = { + data: any; + stream: StreamState; +}; + +const TelemetryContext = createContext(null); + +export function TelemetryProvider({ children }: { children: React.ReactNode }) { + const [data, setData] = useState(null); + const [stream, setStream] = useState({ + hr: Array(50).fill(75), + temp: Array(50).fill(37.0) + }); + + useEffect(() => { + const ws = new WebSocket(process.env.NEXT_PUBLIC_WS_URL || "ws://localhost:8000/ws/feed"); + ws.onmessage = (event) => { + const msg = JSON.parse(event.data); + setData(msg); + if (msg?.sensor_data) { + setStream(prev => ({ + hr: [...prev.hr.slice(1), msg.sensor_data.Heart_Rate], + temp: [...prev.temp.slice(1), msg.sensor_data.Body_Temp] + })); + } + }; + return () => ws.close(); + }, []); + + return ( + + {children} + + ); +} + +export function useTelemetry() { + const context = useContext(TelemetryContext); + if (!context) { + throw new Error("useTelemetry must be used within a TelemetryProvider"); + } + return context; +} diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json new file mode 100644 index 0000000000000000000000000000000000000000..cf9c65d3e0676a0169374d827f7abb97497789ef --- /dev/null +++ b/frontend/tsconfig.json @@ -0,0 +1,34 @@ +{ + "compilerOptions": { + "target": "ES2017", + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "react-jsx", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "@/*": ["./src/*"] + } + }, + "include": [ + "next-env.d.ts", + "**/*.ts", + "**/*.tsx", + ".next/types/**/*.ts", + ".next/dev/types/**/*.ts", + "**/*.mts" + ], + "exclude": ["node_modules"] +} diff --git a/model/classes.pkl b/model/classes.pkl index e752f71cc5e90411f2569611f54d3dcef4b38c61..d6bd01789c595a93cfb3f0a9581e55ae357ac86d 100644 --- a/model/classes.pkl +++ b/model/classes.pkl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a72e398999fbcd2495f31ec5f28f07e8325e903d836d2f2fbef872d6da2785bb +oid sha256:43241923778ab2ba4ee472ae79697c0730f7f325bf9d6607aea5d988fea6d7ab size 440 diff --git a/model/knn.pkl b/model/knn.pkl index dcfe01f8e08d18d9794bd5ea680f0f41a886010d..6458f1fc2c25c02400cae4576b5f5367b89cadfa 100644 --- a/model/knn.pkl +++ b/model/knn.pkl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0fd25eec3d294ae23ef7fcce62dad8c695f585a149935845bdcd4771b3271e61 -size 6440614 +oid sha256:57cf9edd90be13d04cd8a419486598f4b2ca2f2d417a584aa600d9fd7ce6647e +size 5639846 diff --git a/model/label_encoder.pkl b/model/label_encoder.pkl index b129ecfaba538379f937c96f7ace1171f18b3354..5a91801495a4421b833deedb5d7cff7ce38e15ca 100644 --- a/model/label_encoder.pkl +++ b/model/label_encoder.pkl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:da67513db74567d59333258b4d1982743cfdc80e54a59feb67922e7ea1bb92a9 +oid sha256:7d8b08e681c302b1d80cba57ab678d752a3608e7787fe7fcf82c4052c5eef91e size 543 diff --git a/model/logreg.pkl b/model/logreg.pkl index 157fc5a9f41111fc50e1b3089bc9b233c8ca8442..e02e59e68d5a1e7041b726fe9860e465519b8e1b 100644 --- a/model/logreg.pkl +++ b/model/logreg.pkl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f065ca55b8b3017459446e339341e947635c15b9919b7e033707e261d451662f -size 1615 +oid sha256:32715849e964b7e609d7b6b359975908a567a4da7d990f80f89a28a603895e24 +size 1567 diff --git a/model/rf.pkl b/model/rf.pkl index 1470328b6bcb27e166f17b0712ce3acdca1882d4..4adb980b3a161969aeab05f44cd399c9b2661608 100644 --- a/model/rf.pkl +++ b/model/rf.pkl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1e9d9d36ed1ae8016273c4c72d77043ea059db8e359059f29b05f40885441daa -size 171569 +oid sha256:65f4f5ffbd62038eff9fca041d0c907a53aa1e7deee7676ea5623ffdbae950ed +size 157857 diff --git a/model/scaler.pkl b/model/scaler.pkl index 808fdf1b9b5a753f5ec411bafa820f887d0af614..8cee812a73daf801aab73441d187850ad1947951 100644 --- a/model/scaler.pkl +++ b/model/scaler.pkl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d084c7de7a9421241c7eda590f17b42175ca4b042162377056f45351d0bc82f4 -size 1127 +oid sha256:2cecddcc89cecbd0b354f4f13f2a2f058d8b144a9729757438b561b36b46a8d2 +size 1159 diff --git a/model/small_nn.pkl b/model/small_nn.pkl index 3fda3657c61488c052630a19701f724ab6b27f4f..520569f856c8b91dc35c2871da7216659e402919 100644 --- a/model/small_nn.pkl +++ b/model/small_nn.pkl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:4aa30676f4f08388fa0bcb22823e4dfc0ef001ef834b56d0caafe6ced207c0f9 -size 22664 +oid sha256:1f60d7c98971af712b44ec6da288623f580ca9c9345f7c8cbee87f9fe12ce984 +size 22456 diff --git a/model/svm.pkl b/model/svm.pkl index fc6cd95ab3a8e2fd78ff43ea06c573be3d0be457..5ab990de86442e60e6012708d0cbb77740b5f333 100644 --- a/model/svm.pkl +++ b/model/svm.pkl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:bb1d7c34921c17009437b565526ed302366625bcf74a8068f8e6e2e71e2d9ce6 -size 899439 +oid sha256:e4b5db79c6bafe30b72c8f5e2ce41a29090345349db7a2ecea07557dd77d961f +size 842239 diff --git a/run.bat b/run.bat index 202756353361a3fc444ae6e2118b50ede95c5457..c5bfc5205d016cb3584d784e3c08eea2f766c3a9 100644 --- a/run.bat +++ b/run.bat @@ -4,15 +4,25 @@ echo TinyML Dashboard Launch Sequence echo ============================================== echo [1/3] Installing/Verifying Dependencies... -python -m pip install -r backend\requirements.txt -python -m pip install -r frontend\requirements.txt +echo Backend (uv)... +if not exist "backend\.venv" ( + uv venv --python 3.11 backend\.venv +) +call backend\.venv\Scripts\activate.bat +uv pip install -r backend\requirements.txt + +echo Frontend (npm)... +cd frontend +call npm install +cd .. echo [2/3] Spinning up FastAPI Backend... echo Clearing previous instances on port 8000 (if any)... for /f "tokens=5" %%a in ('netstat -aon ^| find ":8000" ^| find "LISTENING"') do taskkill /f /pid %%a >nul 2>&1 -start /B "TinyML Backend" cmd /c "cd backend && python -m uvicorn main:app --host 0.0.0.0 --port 8000" +start /B "TinyML Backend" cmd /c "cd backend && call .venv\Scripts\activate.bat && python -m uvicorn main:app --host 0.0.0.0 --port 8000" -echo [3/3] Launching Streamlit Frontend... +echo [3/3] Launching Next.js Frontend... +echo Clearing previous instances on port 3000 (if any)... +for /f "tokens=5" %%a in ('netstat -aon ^| find ":3000" ^| find "LISTENING"') do taskkill /f /pid %%a >nul 2>&1 cd frontend -python -m streamlit run Home.py - +npm run dev