diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..e4ca99992c7e4c6def4720465534dcb623897738
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,29 @@
+# ── Python ────────────────────────────────────────────────────────────────────
+__pycache__/
+*.py[cod]
+*.pyo
+.venv/
+*.egg-info/
+.pytest_cache/
+.mypy_cache/
+dist/
+
+# ── Node / Frontend ───────────────────────────────────────────────────────────
+frontend/node_modules/
+frontend/dist/
+frontend/.env.local
+
+# ── Git / Editor ──────────────────────────────────────────────────────────────
+.git/
+.gitignore
+.vscode/
+*.md
+TASKS.md
+
+# ── OS ────────────────────────────────────────────────────────────────────────
+.DS_Store
+Thumbs.db
+
+# ── Model binary variants (keep only .pkl, not duplicate .pth) ───────────────
+# Both extensions are identical — Docker only needs .pkl
+**/*.pth
diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..bca5bb0a4e7b23ba6b0c8ca778633c20e429a6cb
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,2 @@
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
diff --git a/.github/workflows/deploy-backend.yml b/.github/workflows/deploy-backend.yml
new file mode 100644
index 0000000000000000000000000000000000000000..2845792e5fa003c26c740417ba2b687d313932d3
--- /dev/null
+++ b/.github/workflows/deploy-backend.yml
@@ -0,0 +1,61 @@
+name: Deploy Backend → Fly.io
+
+on:
+  push:
+    branches: [main]
+    paths:
+      - 'backend/**'
+      - 'Dockerfile'
+      - '.dockerignore'
+      - 'fly.toml'
+      - 'Mediapipe_XGBoost/**'
+      - 'CNN_Autoencoder_LightGBM/**'
+      - 'CNN_PreTrained/**'
+
+jobs:
+  test:
+    name: Run backend tests
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+          cache: pip
+          cache-dependency-path: backend/requirements.txt
+
+      - name: Install deps
+        run: |
+          pip install -r backend/requirements.txt
+          pip install -r backend/requirements-dev.txt
+
+      - name: Run tests
+        working-directory: backend
+        env:
+          KERAS_BACKEND: tensorflow
+          TF_CPP_MIN_LOG_LEVEL: "3"
+          CUDA_VISIBLE_DEVICES: ""
+          TF_ENABLE_ONEDNN_OPTS: "0"
+        run: pytest tests/ -v --tb=short -q
+        # Note: tests will be skipped automatically if model .pkl files are absent
+        # (model artefacts are gitignored). Add them as GitHub Actions artifacts
+        # or use DVC/GCS to restore them in CI if you want full test coverage.
+
+  deploy:
+    name: Deploy to Fly.io
+    needs: test
+    runs-on: ubuntu-latest
+    environment: production
+    concurrency:
+      group: fly-deploy
+      cancel-in-progress: true
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: superfly/flyctl-actions/setup-flyctl@master
+
+      - name: Deploy
+        run: flyctl deploy --remote-only
+        env:
+          FLY_API_TOKEN: ${{ secrets.FLY_API_TOKEN }}
diff --git a/.github/workflows/deploy-frontend.yml b/.github/workflows/deploy-frontend.yml
new file mode 100644
index 0000000000000000000000000000000000000000..9028fddef4abded7700393976e118561d5d0f046
--- /dev/null
+++ b/.github/workflows/deploy-frontend.yml
@@ -0,0 +1,45 @@
+name: Deploy Frontend → Vercel
+
+on:
+  push:
+    branches: [main]
+    paths:
+      - 'frontend/**'
+
+jobs:
+  build-and-deploy:
+    name: Build & Deploy
+    runs-on: ubuntu-latest
+    environment: production
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-node@v4
+        with:
+          node-version: '22'
+          cache: npm
+          cache-dependency-path: frontend/package-lock.json
+
+      - name: Install dependencies
+        working-directory: frontend
+        run: npm ci
+
+      - name: Type-check
+        working-directory: frontend
+        run: npx tsc --project tsconfig.app.json --noEmit
+
+      - name: Build
+        working-directory: frontend
+        env:
+          VITE_WS_URL:  ${{ vars.VITE_WS_URL }}
+          VITE_API_URL: ${{ vars.VITE_API_URL }}
+        run: npm run build
+
+      - name: Deploy to Vercel
+        uses: amondnet/vercel-action@v25
+        with:
+          vercel-token:   ${{ secrets.VERCEL_TOKEN }}
+          vercel-org-id:  ${{ secrets.VERCEL_ORG_ID }}
+          vercel-project-id: ${{ secrets.VERCEL_PROJECT_ID }}
+          working-directory: frontend
+          vercel-args: '--prod'
diff --git a/CNN_Autoencoder_LightGBM/autoencoder_model.pkl b/CNN_Autoencoder_LightGBM/autoencoder_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..187665d56106a08c70e7530ff9cf6ea0a18f9c5c
--- /dev/null
+++ b/CNN_Autoencoder_LightGBM/autoencoder_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5843688f059d26851774e553c4afddbc7c0f2f7fc048401b8447f290a63d2cbe
+size 92934
diff --git a/CNN_Autoencoder_LightGBM/autoencoder_model.pth b/CNN_Autoencoder_LightGBM/autoencoder_model.pth
new file mode 100644
index 0000000000000000000000000000000000000000..9d2087d04c172dc153c50533b4c101b020d98b51
--- /dev/null
+++ b/CNN_Autoencoder_LightGBM/autoencoder_model.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3593536edda0328121d5f92fd186a8e40c341799bd9bb703e0e2ad155b6e7aeb
+size 121321
diff --git a/CNN_Autoencoder_LightGBM/lgbm_model.pkl b/CNN_Autoencoder_LightGBM/lgbm_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..cb2d2867b68e9620a66689514a19a6f958d56124
--- /dev/null
+++ b/CNN_Autoencoder_LightGBM/lgbm_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e83d2bb3a18da0b3ccdd7afc5d044fa52c6e70c4e6090b312a622a866ee0008
+size 3623126
diff --git a/CNN_Autoencoder_LightGBM/lgbm_model.pth b/CNN_Autoencoder_LightGBM/lgbm_model.pth
new file mode 100644
index 0000000000000000000000000000000000000000..0534d3026a1e90107943adfeaa834eff72035ba2
--- /dev/null
+++ b/CNN_Autoencoder_LightGBM/lgbm_model.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a09f7b712da5f0e6b63e222e4ea938029567bd8cf496da7ad93752d54219b57
+size 3626367
diff --git a/CNN_PreTrained/cnn_model.pkl b/CNN_PreTrained/cnn_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..6adb94be239ba68afcc89f5d9da68f836b8dd2d8
--- /dev/null
+++ b/CNN_PreTrained/cnn_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:464df17407edea99db1b69c20e7ff718f6ceafb05f1bbeaacc889499e4cd920a
+size 97136794
diff --git a/CNN_PreTrained/cnn_model.pth b/CNN_PreTrained/cnn_model.pth
new file mode 100644
index 0000000000000000000000000000000000000000..0bc2e3ed93a88f248a2aa58e0d4558bc1e40609b
--- /dev/null
+++ b/CNN_PreTrained/cnn_model.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6eb8e3419763c47b5ba2480ccaf9907e8d748602b26fe59c009b6112fa840ae5
+size 146278905
diff --git a/CNN_PreTrained/svm_model.pkl b/CNN_PreTrained/svm_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..bce377664a6b217e15764a9e77150678d635609e
--- /dev/null
+++ b/CNN_PreTrained/svm_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf21a17c1340b84359c3431fc4ae8eb05239e4e1ef58dd34ab775f53b9bc7f53
+size 929927
diff --git a/CNN_PreTrained/svm_model.pth b/CNN_PreTrained/svm_model.pth
new file mode 100644
index 0000000000000000000000000000000000000000..259ea84b1227e68cb399d4209d6cd7845b624483
--- /dev/null
+++ b/CNN_PreTrained/svm_model.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e706ebf4588c580d0e6ac6f1554f9fd2eaef5564ee02f8022e3ca5f13bb8985b
+size 1079865
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..7168a5adbb77fc546be24fdbdcd88aecd76658a5
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,48 @@
+# ─────────────────────────────────────────────────────────────────────────────
+# SanketSetu Backend — Dockerfile
+# Build context: repo root (SanketSetu/)
+#
+#   docker build -t sanketsetu-backend .
+#   docker run -p 8000:8000 sanketsetu-backend
+# ─────────────────────────────────────────────────────────────────────────────
+
+FROM python:3.12-slim AS base
+
+# System libraries needed by OpenCV headless + Pillow
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        libgl1 libglib2.0-0 libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# ── Python dependencies (cached layer) ───────────────────────────────────────
+WORKDIR /app
+COPY backend/requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+# ── Application source ────────────────────────────────────────────────────────
+COPY backend/app/ ./app/
+
+# ── Model artefacts ───────────────────────────────────────────────────────────
+# Copied to /models so the container is fully self-contained.
+# Override at runtime with -e WEIGHTS_DIR=/mnt/models + bind-mount if preferred.
+COPY Mediapipe_XGBoost/       /models/Mediapipe_XGBoost/
+COPY CNN_Autoencoder_LightGBM/ /models/CNN_Autoencoder_LightGBM/
+COPY CNN_PreTrained/           /models/CNN_PreTrained/
+
+# ── Runtime environment ───────────────────────────────────────────────────────
+ENV WEIGHTS_DIR=/models \
+    KERAS_BACKEND=tensorflow \
+    TF_CPP_MIN_LOG_LEVEL=3 \
+    CUDA_VISIBLE_DEVICES="" \
+    TF_ENABLE_ONEDNN_OPTS=0 \
+    OMP_NUM_THREADS=4 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+EXPOSE 8000
+
+# ── Health-check ──────────────────────────────────────────────────────────────
+# Wait up to 3 minutes for models to load before marking the container healthy.
+HEALTHCHECK --interval=30s --timeout=10s --start-period=180s --retries=3 \
+    CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health', timeout=5)"
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
diff --git a/Mediapipe_XGBoost/model.pkl b/Mediapipe_XGBoost/model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..5b963393836905c736c041f9e3a43471dc2c070c
--- /dev/null
+++ b/Mediapipe_XGBoost/model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a736b83df3e56b69b0f1c11f018257760746969d6598d90ea2a60c78f8305883
+size 1711525
diff --git a/Mediapipe_XGBoost/model.pth b/Mediapipe_XGBoost/model.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f2547200fcf8a63e8f02717877c16e5926ed8e0b
--- /dev/null
+++ b/Mediapipe_XGBoost/model.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ff5f1cbc121be57f2a7fe04b38925ea740fe79602a6205ca09a748cb0f20b81
+size 1895969
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c27590d088ed7bf0275991d407a54da55c1eea3e
--- /dev/null
+++ b/README.md
@@ -0,0 +1,108 @@
+# SanketSetu
+
+A real-time sign language recognition system using machine learning and computer vision.
+
+## Overview
+
+SanketSetu is an intelligent sign language interpretation system that provides real-time recognition and translation of sign language gestures using advanced machine learning models and MediaPipe hand tracking.
+
+## Project Structure
+
+```
+├── backend/              # FastAPI backend server
+│   ├── app/             # Main application code
+│   │   ├── inference/   # ML inference pipelines
+│   │   └── models/      # Model loading and management
+│   └── tests/           # Backend tests
+├── frontend/            # React + TypeScript frontend
+│   └── src/
+│       ├── components/  # React components
+│       ├── hooks/       # Custom React hooks
+│       └── lib/         # Utility libraries
+├── CNN_Autoencoder_LightGBM/  # CNN Autoencoder + LightGBM model
+├── CNN_PreTrained/              # CNN + SVM model
+└── Mediapipe_XGBoost/          # MediaPipe + XGBoost model
+```
+
+## Features
+
+- Real-time sign language gesture recognition
+- Multiple ML model ensemble approach
+- WebSocket-based real-time communication
+- MediaPipe hand landmark tracking
+- Interactive webcam feed with visual feedback
+- Prediction confidence display
+
+## Tech Stack
+
+### Backend
+- FastAPI
+- Python 3.x
+- PyTorch
+- LightGBM
+- XGBoost
+- MediaPipe
+
+### Frontend
+- React
+- TypeScript
+- Vite
+- TailwindCSS
+
+## Getting Started
+
+### Prerequisites
+- Python 3.8+
+- Node.js 16+
+- npm or yarn
+
+### Backend Setup
+
+```bash
+cd backend
+pip install -r requirements.txt
+python -m app.main
+```
+
+### Frontend Setup
+
+```bash
+cd frontend
+npm install
+npm run dev
+```
+
+## Development
+
+Run the development servers:
+
+```bash
+# Start both frontend and backend
+.\start.ps1
+```
+
+## Docker
+
+Build and run using Docker:
+
+```bash
+docker build -t sanketsetu .
+docker run -p 8000:8000 sanketsetu
+```
+
+## Testing
+
+Run backend tests:
+
+```bash
+cd backend
+pytest
+```
+
+## License
+
+All rights reserved.
+
+## Author
+
+Devrajsinh Gohil (devrajsinh2012)
diff --git a/SanketSetu_ Production-Grade Implementation Plan.md b/SanketSetu_ Production-Grade Implementation Plan.md
new file mode 100644
index 0000000000000000000000000000000000000000..e877d0d19dac08ead1126465f3819bedf184bef4
--- /dev/null
+++ b/SanketSetu_ Production-Grade Implementation Plan.md	
@@ -0,0 +1,99 @@
+# SanketSetu: Production-Grade Implementation Plan
+
+## 1. Executive Summary
+**SanketSetu** (Bridge of Signs) is a high-performance, real-time Gujarati Sign Language (GSL) recognition system. This document outlines a production-ready architecture designed to run entirely on **free-tier cloud services**. The system leverages a decoupled architecture with a React-based interactive frontend and a FastAPI backend, ensuring low-latency inference and a seamless user experience.
+
+---
+
+## 2. High-Level System Architecture
+The system follows a modern microservices-inspired pattern to ensure scalability and ease of updates.
+
+| Component | Technology | Role | Hosting (Free Tier) |
+| :--- | :--- | :--- | :--- |
+| **Frontend** | React + Vite + TS | User interface, webcam capture, real-time feedback | **Vercel** |
+| **Backend API** | FastAPI (Python) | WebSocket management, API gateway, logic | **Fly.io** |
+| **Inference Engine** | ONNX Runtime / XGBoost | High-speed model execution | **Fly.io** (Internal) |
+| **Storage** | Cloudflare R2 | S3-compatible storage for model weights | **Cloudflare** |
+| **Real-time** | WebSockets (WSS) | Low-latency frame-by-frame data transfer | N/A |
+
+---
+
+## 3. Backend Implementation Details
+
+### 3.1 API Design (FastAPI)
+The backend is built for speed. It handles binary data from WebSockets to minimize overhead.
+
+*   **WebSocket Protocol**: The client sends a stream of normalized hand landmark coordinates (63 points per frame) extracted locally via MediaPipe. This reduces bandwidth significantly compared to sending raw video frames.
+*   **Concurrency**: Uses `asyncio` to handle multiple simultaneous user connections without blocking the event loop.
+*   **Model Loading**: Models are loaded into memory at startup using a Singleton pattern to ensure zero-latency on the first request.
+
+### 3.2 Model Serving Strategy
+1.  **Primary Model**: The **XGBoost** model is used as the default due to its sub-millisecond inference time.
+2.  **Backup/Ensemble**: The system can optionally query the **CNN+SVM** or **LGBM** models for high-confidence verification if the XGBoost score is below a certain threshold.
+3.  **Optimization**: Models are converted to **ONNX** format to leverage the ONNX Runtime's hardware-specific optimizations, even on free-tier CPU instances.
+
+---
+
+## 4. Frontend & Interactive UI/UX
+
+The frontend is designed to be "cool," responsive, and highly interactive, providing users with a "futuristic" feel.
+
+### 4.1 Tech Stack
+*   **Styling**: Tailwind CSS for rapid, modern UI development.
+*   **Animations**: Framer Motion for smooth transitions, layout changes, and interactive elements.
+*   **Icons**: Lucide React for a clean, consistent icon set.
+
+### 4.2 Key UI Features
+*   **Glassmorphism Design**: Use of semi-transparent backgrounds with blur effects for a modern look.
+*   **Interactive Landmark Overlay**: A canvas overlay on the webcam feed that draws the 21 hand landmarks in real-time. Landmarks will "glow" when a sign is successfully recognized.
+*   **Dynamic Prediction HUD**: A Head-Up Display (HUD) style interface that shows the current prediction, confidence level, and a history of recently detected signs.
+*   **Responsive Layout**: Fully functional on mobile and desktop, with optimized camera controls for both.
+
+### 4.3 User Experience Flow
+1.  **Onboarding**: A quick, animated guide on how to position the hand for best results.
+2.  **Calibration**: A brief "Ready?" state that ensures the lighting and hand distance are optimal.
+3.  **Real-time Translation**: Instant feedback as the user signs, with the translated Gujarati text appearing in a stylized "speech bubble" or text box.
+
+---
+
+## 4. Deployment & DevOps
+
+### 4.1 Continuous Integration/Deployment (CI/CD)
+Using **GitHub Actions**, the project will follow a strict deployment pipeline:
+1.  **Lint & Test**: Ensure code quality and run unit tests for ML logic.
+2.  **Build**: Create optimized production builds for the React app and Dockerize the FastAPI backend.
+3.  **Deploy**: 
+    *   Frontend automatically pushes to **Vercel**.
+    *   Backend pushes to **Fly.io** using `flyctl`.
+
+### 4.2 Scalability & Cost Management
+*   **Scale-to-Zero**: The backend on Fly.io can be configured to sleep when not in use to preserve free-tier resources.
+*   **CDN Caching**: Vercel's Edge Network will cache all static assets, ensuring fast load times globally.
+
+---
+
+## 5. Implementation Roadmap
+
+### Phase 1: Core Backend & ML Integration
+- [ ] Set up FastAPI project structure.
+- [ ] Implement WebSocket handler for landmark data.
+- [ ] Integrate the trained XGBoost model for real-time inference.
+
+### Phase 2: Advanced Frontend Development
+- [ ] Initialize Vite + React project with Tailwind.
+- [ ] Implement webcam capture and MediaPipe landmark extraction (client-side).
+- [ ] Create the interactive HUD and glassmorphism UI.
+
+### Phase 3: Production Hardening
+- [ ] Set up GitHub Actions for automated deployment.
+- [ ] Implement error handling for low-bandwidth scenarios.
+- [ ] Finalize documentation and user guide.
+
+---
+
+## 6. References
+[1] [FastAPI Documentation](https://fastapi.tiangolo.com/) - High-performance web framework for building APIs.
+[2] [MediaPipe Hands](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker) - Real-time hand landmark detection.
+[3] [Framer Motion](https://www.framer.com/motion/) - A production-ready motion library for React.
+[4] [Fly.io Free Tier](https://fly.io/docs/about/pricing/) - Details on free-tier resource allocation.
+[5] [Vercel Deployment](https://vercel.com/docs/deployments/overview) - Global CDN and hosting for frontend applications.
diff --git a/TASKS.md b/TASKS.md
new file mode 100644
index 0000000000000000000000000000000000000000..2a397bc7610da3482b311851bcfa1d850754f355
--- /dev/null
+++ b/TASKS.md
@@ -0,0 +1,284 @@
+# SanketSetu — Execution TODO & Implementation Tracker
+
+## Model Analysis (Reviewed 2026-03-02)
+
+All 5 model files inspected. Three distinct inference pipelines exist:
+
+| Pipeline | Files | Input | Process | Output |
+|---|---|---|---|---|
+| **A — Primary (Fastest)** | `Mediapipe_XGBoost/model.pkl` | 63 MediaPipe coords (21 landmarks × x,y,z) | XGBClassifier (50 trees) | 34-class probability |
+| **B — Autoencoder + LGBM** | `CNN_Autoencoder_LightGBM/autoencoder_model.pkl` + `lgbm_model.pkl` | 63 MediaPipe coords | Encoder (63→32→**16** bottleneck) + LGBMClassifier | 34-class probability |
+| **C — Vision CNN + SVM** | `CNN_PreTrained/cnn_model.pkl` + `svm_model.pkl` | 128×128×3 RGB image | ResNet50-based CNN (179 layers) → 256 features + SVC(C=10) | 34-class probability w/ probability=True |
+
+### Key Architecture Facts
+- **34 classes** (Gujarati Sign Language alphabet + digits, labels 0–33)
+- **Pipeline A** input: 63 floats — directly from MediaPipe `hand_landmarks` (x, y, z per landmark, flattened)
+- **Pipeline B** input: same 63 floats → takes only the encoder half (first 3 Dense layers, output of `dense_1` layer = 16 features) 
+- **Pipeline C** input: 128×128 BGR/RGB cropped hand image, normalized to [0,1]
+- All `.pth` files are identical copies of the `.pkl` files (same objects, different extension)
+- Model quality strategy: A is primary (sub-ms); if confidence < threshold, query B or C for ensemble
+
+---
+
+## Project Folder Structure to Create
+
+```
+SanketSetu/
+├── backend/                    ← FastAPI server
+│   ├── app/
+│   │   ├── main.py             ← FastAPI entry, WebSocket + REST
+│   │   ├── models/
+│   │   │   ├── loader.py       ← Singleton model loader
+│   │   │   └── label_map.py    ← 0–33 → Gujarati sign name mapping
+│   │   ├── inference/
+│   │   │   ├── pipeline_a.py   ← XGBoost inference (63 landmarks)
+│   │   │   ├── pipeline_b.py   ← Autoencoder encoder + LightGBM
+│   │   │   ├── pipeline_c.py   ← ResNet CNN + SVM (image-based)
+│   │   │   └── ensemble.py     ← Confidence-weighted ensemble logic
+│   │   ├── schemas.py          ← Pydantic request/response models
+│   │   └── config.py           ← Settings (confidence threshold, etc.)
+│   ├── weights/                ← Symlink or copy of model pkl files
+│   ├── requirements.txt
+│   ├── Dockerfile
+│   └── fly.toml
+│
+├── frontend/                   ← Vite + React + TS
+│   ├── src/
+│   │   ├── components/
+│   │   │   ├── WebcamFeed.tsx       ← Webcam + canvas landmark overlay
+│   │   │   ├── LandmarkCanvas.tsx   ← Draws 21 hand points + connections
+│   │   │   ├── PredictionHUD.tsx    ← Live sign, confidence bar, history
+│   │   │   ├── OnboardingGuide.tsx  ← Animated intro wizard
+│   │   │   └── Calibration.tsx      ← Lighting/distance check UI
+│   │   ├── hooks/
+│   │   │   ├── useWebSocket.ts      ← WS connection, send/receive
+│   │   │   ├── useMediaPipe.ts      ← MediaPipe Hands JS integration
+│   │   │   └── useWebcam.ts         ← Camera permissions + stream
+│   │   ├── lib/
+│   │   │   └── landmarkUtils.ts     ← Landmark normalization (mirror XGBoost preprocessing)
+│   │   ├── App.tsx
+│   │   └── main.tsx
+│   ├── public/
+│   ├── index.html
+│   ├── tailwind.config.ts
+│   ├── vite.config.ts
+│   └── package.json
+│
+├── CNN_Autoencoder_LightGBM/   ← (existing)
+├── CNN_PreTrained/             ← (existing)
+├── Mediapipe_XGBoost/          ← (existing)
+└── .github/
+    └── workflows/
+        ├── deploy-backend.yml
+        └── deploy-frontend.yml
+```
+
+---
+
+## Phase 1 — Backend Core (FastAPI + Model Integration)
+
+### 1.1 Project Bootstrap
+- [x] Create `backend/` folder and `app/` package structure
+- [x] Create `backend/requirements.txt` with: `fastapi`, `uvicorn[standard]`, `websockets`, `xgboost`, `lightgbm`, `scikit-learn`, `keras==3.13.2`, `tensorflow-cpu`, `numpy`, `opencv-python-headless`, `pillow`, `python-dotenv`
+- [x] Create `backend/app/config.py` — confidence threshold (default 0.7), WebSocket max connections, pipeline mode (A/B/C/ensemble)
+- [x] Create `backend/app/models/label_map.py` — map class indices 0–33 to Gujarati sign names
+
+### 1.2 Model Loader (Singleton)
+- [x] Create `backend/app/models/loader.py`
+  - Load `model.pkl` (XGBoost) at startup
+  - Load `autoencoder_model.pkl` (extract encoder layers only: input → dense → dense_1) and `lgbm_model.pkl`
+  - Load `cnn_model.pkl` (full ResNet50 feature extractor, strip any classification head) and `svm_model.pkl`
+  - Expose `ModelStore` singleton accessed via `get_model_store()` dependency
+  - Log load times for each model
+
+### 1.3 Pipeline A — XGBoost (Primary, Landmarks)
+- [x] Create `backend/app/inference/pipeline_a.py`
+  - Input: `List[float]` of length 63 (x,y,z per landmark, already normalized by MediaPipe)
+  - Output: `{"sign": str, "confidence": float, "probabilities": List[float]}`
+  - Use `model.predict_proba(np.array(landmarks).reshape(1,-1))[0]`
+  - Return `classes_[argmax]` and `max(probabilities)` as confidence
+
+### 1.4 Pipeline B — Autoencoder Encoder + LightGBM
+- [x] Create `backend/app/inference/pipeline_b.py`
+  - Build encoder-only submodel: `encoder = keras.Model(inputs=model.input, outputs=model.layers[2].output)` (output of `dense_1`, the 16-D bottleneck)
+  - Input: 63 MediaPipe coords
+  - Encode: `features = encoder.predict(np.array(landmarks).reshape(1,-1))[0]`  → shape (16,)
+  - Classify: `lgbm.predict_proba(features.reshape(1,-1))[0]`
+
+### 1.5 Pipeline C — CNN + SVM (Image-based)
+- [x] Create `backend/app/inference/pipeline_c.py`
+  - Input: base64-encoded JPEG or raw bytes of the cropped hand region (128×128 px)
+  - Decode → numpy array (128,128,3) uint8 → normalize to float32 [0,1]
+  - `features = cnn_model.predict(img[np.newaxis])[0]`  → shape (256,)
+  - `proba = svm.predict_proba(features.reshape(1,-1))[0]`
+  - Note: CNN inference is slower (~50–200ms on CPU); only call when Pipeline A confidence < threshold
+
+### 1.6 Ensemble Logic
+- [x] Create `backend/app/inference/ensemble.py`
+  - Call Pipeline A first
+  - If `confidence < config.THRESHOLD` (default 0.7), call Pipeline B
+  - If still below threshold and image data available, call Pipeline C
+  - Final result: weighted average of probabilities from each pipeline that was called
+  - Return the top predicted class and ensemble confidence score
+
+### 1.7 WebSocket Handler
+- [x] Create `backend/app/main.py` with FastAPI app
+- [x] Implement `GET /health` — returns `{"status": "ok", "models_loaded": true}`
+- [x] Implement `WS /ws/landmarks` — primary endpoint
+  - Client sends JSON: `{"landmarks": [63 floats], "session_id": "..."}`
+  - Server responds: `{"sign": "...", "confidence": 0.95, "pipeline": "A", "label_index": 12}`
+  - Handle disconnect gracefully
+- [x] Implement `WS /ws/image` — optional image-based endpoint for Pipeline C
+  - Client sends JSON: `{"image_b64": "...", "session_id": "..."}`
+- [x] Implement `POST /api/predict` — REST fallback for non-WS clients
+  - Body: `{"landmarks": [63 floats]}`
+  - Returns same response schema as WS
+
+### 1.8 Schemas & Validation
+- [x] Create `backend/app/schemas.py`
+  - `LandmarkMessage(BaseModel)`: `landmarks: List[float]` (must be length 63), `session_id: str`
+  - `ImageMessage(BaseModel)`: `image_b64: str`, `session_id: str`
+  - `PredictionResponse(BaseModel)`: `sign: str`, `confidence: float`, `pipeline: str`, `label_index: int`, `probabilities: Optional[List[float]]`
+
+### 1.9 CORS & Middleware
+- [x] Configure CORS for Vercel frontend domain + localhost:5173
+- [x] Add request logging middleware (log session_id, pipeline used, latency ms)
+- [x] Add global exception handler returning proper JSON errors
+
+---
+
+## Phase 2 — Frontend (React + Vite + Tailwind + Framer Motion)
+
+### 2.1 Project Bootstrap
+- [x] Run `npm create vite@latest frontend -- --template react-ts` inside `SanketSetu/`
+- [x] Install deps: `tailwindcss`, `framer-motion`, `lucide-react`, `@mediapipe/tasks-vision`
+- [x] Configure Tailwind with custom palette (dark neon-cyan glassmorphism theme)
+- [x] Set up `vite.config.ts` proxy: `/api` → backend URL, `/ws` → backend WS URL
+
+### 2.2 Webcam Hook (`useWebcam.ts`)
+- [x] Request `getUserMedia({ video: { width: 1280, height: 720 } })`
+- [x] Expose `videoRef`, `isReady`, `error`, `switchCamera()` (for mobile front/back toggle)
+- [x] Handle permission denied state with instructional UI
+
+### 2.3 MediaPipe Hook (`useMediaPipe.ts`)
+- [x] Initialize `HandLandmarker` from `@mediapipe/tasks-vision` (WASM backend)
+- [x] Process video frames at target 30fps using `requestAnimationFrame`
+- [x] Extract `landmarks[0]` (first hand) → flatten to 63 floats `[x0,y0,z0, x1,y1,z1, ...]`
+- [x] Normalize: subtract wrist (landmark 0) position to make translation-invariant — **must match training preprocessing**
+- [x] Expose `landmarks: number[] | null`, `handedness: string`, `isDetecting: boolean`
+
+### 2.4 WebSocket Hook (`useWebSocket.ts`)
+- [x] Connect to `wss://backend-url/ws/landmarks` on mount
+- [x] Auto-reconnect with exponential backoff on disconnect
+- [x] `sendLandmarks(landmarks: number[])` — throttled to max 15 sends/sec
+- [x] Expose `lastPrediction: PredictionResponse | null`, `isConnected: boolean`, `latency: number`
+
+### 2.5 Landmark Canvas (`LandmarkCanvas.tsx`)
+- [x] Overlay `<canvas>` on top of `<video>` with `position: absolute`
+- [x] Draw 21 hand landmark dots (cyan glow: `shadowBlur`, `shadowColor`)
+- [x] Draw 21 bone connections following MediaPipe hand topology (finger segments)
+- [x] On successful prediction: animate landmarks to pulse/glow with Framer Motion spring
+- [x] Use `requestAnimationFrame` for smooth 60fps rendering
+
+### 2.6 Prediction HUD (`PredictionHUD.tsx`)
+- [x] Glassmorphism card: `backdrop-blur`, `bg-white/10`, `border-white/20`
+- [x] Large Gujarati sign name (mapped from label index)
+- [x] Confidence bar: animated width transition via Framer Motion `animate={{ width: confidence% }}`
+- [x] Color coding: green (>85%), yellow (60–85%), red (<60%)
+- [x] Rolling history list: last 10 recognized signs (Framer Motion `AnimatePresence` for enter/exit)
+- [x] Pipeline badge: shows which pipeline (A/B/C) produced the result
+- [x] Latency display: shows WS round-trip time in ms
+
+### 2.7 Onboarding Guide (`OnboardingGuide.tsx`)
+- [x] 3-step animated wizard using Framer Motion page transitions
+  1. "Position your hand 30–60cm from camera"
+  2. "Ensure good lighting, avoid dark backgrounds"
+  3. "Show signs clearly — palm facing camera"
+- [x] Skip button + "Don't show again" (localStorage)
+
+### 2.8 Calibration Screen (`Calibration.tsx`)
+- [x] Brief 2-second "Ready?" screen after onboarding
+- [x] Check: hand detected by MediaPipe → show green checkmark animation
+- [x] Auto-transitions to main translation view when hand is stable for 1 second
+
+### 2.9 Main App Layout (`App.tsx`)
+- [x] Full-screen dark background with subtle animated gradient
+- [x] Three-panel layout (desktop): webcam | HUD | history
+- [x] Mobile: stacked layout with webcam top, HUD bottom
+- [x] Header: "SanketSetu | સંકેત-સેતુ" with glowing text effect
+- [x] Settings gear icon → modal for pipeline selection (A / B / C / Ensemble), confidence threshold slider
+
+---
+
+## Phase 3 — Dockerization & Deployment
+
+### 3.1 Backend Dockerfile
+- [x] Create `Dockerfile` (repo root, build context includes models)
+- [x] Add `.dockerignore` (excludes `.venv`, `node_modules`, `*.pth`, tests)
+- [ ] Test locally: `docker build -t sanketsetu-backend . && docker run -p 8000:8000 sanketsetu-backend`
+
+### 3.2 Fly.io Configuration
+- [x] Create `fly.toml` (repo root, region=maa, port 8000, shared-cpu-2x)
+- [x] Note: Keras/TF will increase Docker image size — use `tensorflow-cpu` to keep slim
+- [ ] Set secrets via `flyctl secrets set` for any API keys
+- [ ] Run: `flyctl deploy --dockerfile Dockerfile`
+
+### 3.3 Vercel Frontend Deployment
+- [x] Create `frontend/vercel.json` with SPA rewrite + WASM Content-Type header
+- [x] Add `VITE_WS_URL` and `VITE_API_URL` to Vercel environment variables (via CI vars)
+- [ ] Ensure `@mediapipe/tasks-vision` WASM files are served correctly (add to `public/`)
+
+### 3.4 GitHub Actions CI/CD
+- [x] Create `.github/workflows/deploy-backend.yml`
+  - Triggers on push to `main` when `backend/**` changes
+  - Steps: checkout → setup Python → run tests → `flyctl deploy`
+- [x] Create `.github/workflows/deploy-frontend.yml`
+  - Triggers on push to `main` when `frontend/**` changes
+  - Steps: checkout → `npm ci` → tsc → `npm run build` → Vercel CLI deploy
+
+---
+
+## Phase 4 — Testing & Hardening
+
+### 4.1 Backend Tests
+- [x] `tests/test_pipeline_a.py` — 8 unit tests, XGBoost inference (4s)
+- [x] `tests/test_pipeline_b.py` — 6 unit tests, encoder + LightGBM (49s)
+- [x] `tests/test_pipeline_c.py` — 7 unit tests, CNN + SVM with real 128×128 images (14s)
+- [x] `tests/test_websocket.py` — 7 integration tests, health + REST + WS round-trip
+
+### 4.2 Frontend Error Handling
+- [ ] No-camera fallback UI (file upload for image mode)
+- [x] WS reconnecting banner (red banner when `!isConnected && stage === 'running'`)
+- [x] Low-bandwidth mode: reduce send rate to 5fps if latency > 500ms + yellow "LB" badge in HUD
+- [x] MediaPipe WASM load failure fallback message (shown in header via `mpError`)
+
+### 4.3 Label Map (Critical)
+- [ ] Create `backend/app/models/label_map.py` mapping classes 0–33 to actual Gujarati signs
+  - You need to confirm the exact mapping used during training (check your original dataset/notebook)
+  - Placeholder: `LABEL_MAP = { 0: "ક", 1: "ખ", ... , 33: "?" }`
+  - This file must exactly mirror what was used in training
+
+---
+
+## Execution Order (Start Here)
+
+```
+Week 1: Phase 1.1 → 1.3 → 1.7 (get WS working with Pipeline A alone, test in browser)
+Week 2: Phase 1.4 → 1.5 → 1.6 (add other pipelines + ensemble)
+Week 3: Phase 2.1 → 2.2 → 2.3 → 2.4 (React skeleton + WS connected)
+Week 4: Phase 2.5 → 2.6 → 2.7 → 2.8 → 2.9 (full UI)
+Week 5: Phase 3 + 4 (deploy + tests)
+```
+
+---
+
+## Critical Decision Points
+
+| Decision | Default | Notes |
+|---|---|---|
+| Primary pipeline | **A (XGBoost)** | Sub-ms inference, uses MediaPipe landmarks already extracted client-side |
+| Confidence threshold for fallback | **0.70** | Tune after testing - if XGBoost < 70%, call Pipeline B |
+| Enable Pipeline C (CNN) | **Optional / off by default** | Adds ~150ms latency and requires image upload, not just landmarks |
+| MediaPipe model variant | **lite** | Use `hand_landmarker_lite.task` for mobile performance |
+| WebSocket frame rate | **15fps** | Sufficient for sign recognition, avoids server overload |
+| Gujarati label map | **CONFIRM WITH DATASET** | Classes 0–33 must match training data exactly |
diff --git a/backend/.env.example b/backend/.env.example
new file mode 100644
index 0000000000000000000000000000000000000000..c2f4e994696574f855f6f7ff03fda64c7ee1109b
--- /dev/null
+++ b/backend/.env.example
@@ -0,0 +1,32 @@
+# SanketSetu Backend — environment variables
+# Copy this file to .env and edit as needed.
+# All values below are the defaults; remove a line to keep the default.
+
+# ── Model paths (default: resolved from repo root) ─────────────────────────
+# WEIGHTS_DIR=/absolute/path/to/model/dir
+
+# ── Inference ───────────────────────────────────────────────────────────────
+# Pipeline A confidence below this → also run Pipeline B
+CONFIDENCE_THRESHOLD=0.70
+
+# Pipeline A+B ensemble confidence below this → also run Pipeline C (if image)
+SECONDARY_THRESHOLD=0.60
+
+# Which pipeline to run: A | B | C | ensemble
+PIPELINE_MODE=ensemble
+
+# ── Server ──────────────────────────────────────────────────────────────────
+MAX_WS_CONNECTIONS=100
+
+# Comma-separated list of allowed CORS origins
+CORS_ORIGINS=http://localhost:5173,http://localhost:3000
+
+# ── TensorFlow / Keras ──────────────────────────────────────────────────────
+KERAS_BACKEND=tensorflow
+TF_CPP_MIN_LOG_LEVEL=3
+CUDA_VISIBLE_DEVICES=         # empty = CPU-only, skip GPU scan (faster startup)
+TF_ENABLE_ONEDNN_OPTS=0
+OMP_NUM_THREADS=4
+
+# ── Logging ─────────────────────────────────────────────────────────────────
+LOG_LEVEL=INFO
diff --git a/backend/app/__init__.py b/backend/app/__init__.py
new file mode 100644
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+++ b/backend/app/__init__.py
@@ -0,0 +1 @@
+# backend/app/__init__.py
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+++ b/backend/app/config.py
@@ -0,0 +1,64 @@
+"""
+Application-wide settings for SanketSetu backend.
+Override any value by setting the corresponding environment variable.
+"""
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+# ---------------------------------------------------------------------------
+# TensorFlow / Keras startup optimisations
+# Set these BEFORE any import that might pull in tensorflow.
+# ---------------------------------------------------------------------------
+os.environ.setdefault("KERAS_BACKEND",          "tensorflow")
+os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL",   "3")          # silence C++ TF logs
+os.environ.setdefault("CUDA_VISIBLE_DEVICES",   "")           # CPU-only: skip GPU scan
+os.environ.setdefault("TF_ENABLE_ONEDNN_OPTS",  "0")          # disable oneDNN init check
+os.environ.setdefault("OMP_NUM_THREADS",        "4")          # cap CPU thread pool
+
+# ---------------------------------------------------------------------------
+# Paths
+# ---------------------------------------------------------------------------
+BASE_DIR = Path(__file__).resolve().parent.parent.parent  # repo root
+WEIGHTS_DIR = os.getenv("WEIGHTS_DIR", str(BASE_DIR))
+
+# Individual model paths (relative to repo root)
+PIPELINE_A_MODEL   = os.path.join(WEIGHTS_DIR, "Mediapipe_XGBoost",       "model.pkl")
+PIPELINE_B_AE      = os.path.join(WEIGHTS_DIR, "CNN_Autoencoder_LightGBM", "autoencoder_model.pkl")
+PIPELINE_B_LGBM    = os.path.join(WEIGHTS_DIR, "CNN_Autoencoder_LightGBM", "lgbm_model.pkl")
+PIPELINE_C_CNN     = os.path.join(WEIGHTS_DIR, "CNN_PreTrained",            "cnn_model.pkl")
+PIPELINE_C_SVM     = os.path.join(WEIGHTS_DIR, "CNN_PreTrained",            "svm_model.pkl")
+
+# ---------------------------------------------------------------------------
+# Inference thresholds
+# ---------------------------------------------------------------------------
+# If Pipeline A confidence falls below this, Pipeline B is also called.
+CONFIDENCE_THRESHOLD: float = float(os.getenv("CONFIDENCE_THRESHOLD", "0.70"))
+
+# If ensemble after B still below this, Pipeline C is attempted (if image provided).
+SECONDARY_THRESHOLD: float = float(os.getenv("SECONDARY_THRESHOLD", "0.60"))
+
+# ---------------------------------------------------------------------------
+# Pipeline mode
+# ---------------------------------------------------------------------------
+# "A"        → only XGBoost (fastest)
+# "B"        → only Autoencoder + LGBM
+# "C"        → only CNN + SVM (image required)
+# "ensemble" → A first, fallback to B, then C
+PIPELINE_MODE: str = os.getenv("PIPELINE_MODE", "ensemble")
+
+# ---------------------------------------------------------------------------
+# WebSocket / server
+# ---------------------------------------------------------------------------
+MAX_WS_CONNECTIONS: int = int(os.getenv("MAX_WS_CONNECTIONS", "100"))
+WS_SEND_RATE_LIMIT: int = int(os.getenv("WS_SEND_RATE_LIMIT", "15"))  # max frames/sec per client
+
+# Allowed CORS origins (comma-separated list in env var)
+_cors_env = os.getenv("CORS_ORIGINS", "http://localhost:5173,http://localhost:3000")
+CORS_ORIGINS: list[str] = [o.strip() for o in _cors_env.split(",") if o.strip()]
+
+# ---------------------------------------------------------------------------
+# Logging
+# ---------------------------------------------------------------------------
+LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
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@@ -0,0 +1 @@
+# backend/app/inference/__init__.py
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diff --git a/backend/app/inference/ensemble.py b/backend/app/inference/ensemble.py
new file mode 100644
index 0000000000000000000000000000000000000000..6eebe1d60a41785e786fd2b0f7e35b1c5d02586b
--- /dev/null
+++ b/backend/app/inference/ensemble.py
@@ -0,0 +1,138 @@
+"""
+Ensemble logic — orchestrates Pipelines A → B → C with confidence-based fallback.
+
+Strategy
+--------
+1. Always run Pipeline A (XGBoost, sub-ms).
+2. If confidence < CONFIDENCE_THRESHOLD, also run Pipeline B (Autoencoder+LGBM).
+3. Average the probability vectors from the pipelines that were run.
+4. If ensemble confidence still < SECONDARY_THRESHOLD AND image data is supplied,
+   also run Pipeline C (CNN+SVM) and include it in the average.
+5. Return the class with the highest averaged probability.
+
+The caller can also force a specific pipeline via the PIPELINE_MODE config.
+"""
+from __future__ import annotations
+
+import logging
+import time
+from typing import List, Optional, Any
+
+import numpy as np
+
+from app import config
+from app.models.label_map import get_sign
+from app.inference.pipeline_a import PredictionResult
+import app.inference.pipeline_a as _pa
+import app.inference.pipeline_b as _pb
+import app.inference.pipeline_c as _pc
+
+logger = logging.getLogger(__name__)
+
+
+def run(
+    landmarks: List[float],
+    *,
+    image_input: Optional[str] = None,
+    xgb_model: Any,
+    encoder_model: Any,
+    lgbm_model: Any,
+    cnn_model: Any,
+    svm_model: Any,
+    pipeline_mode: str = "ensemble",
+    confidence_threshold: float = 0.70,
+    secondary_threshold: float = 0.60,
+) -> PredictionResult:
+    """
+    Run one or more inference pipelines and return a consolidated PredictionResult.
+
+    Parameters
+    ----------
+    landmarks            : flat 63-element MediaPipe landmark vector
+    image_input          : optional base-64 JPEG for Pipeline C
+    xgb_model            : Pipeline A model
+    encoder_model        : Pipeline B encoder (Keras sub-model)
+    lgbm_model           : Pipeline B classifier
+    cnn_model            : Pipeline C feature extractor
+    svm_model            : Pipeline C classifier
+    pipeline_mode        : "A" | "B" | "C" | "ensemble"
+    confidence_threshold : fallback to B when A confidence < this value
+    secondary_threshold  : fallback to C when ensemble(A+B) confidence < this value
+    """
+    t0 = time.perf_counter()
+
+    # -----------------------------------------------------------
+    # Forced single-pipeline modes
+    # -----------------------------------------------------------
+    if pipeline_mode == "A":
+        if xgb_model is None:
+            raise RuntimeError("Pipeline A model not loaded.")
+        return _pa.predict(landmarks, xgb_model)
+
+    if pipeline_mode == "B":
+        if encoder_model is None or lgbm_model is None:
+            raise RuntimeError("Pipeline B models not loaded.")
+        return _pb.predict(landmarks, encoder_model, lgbm_model)
+
+    if pipeline_mode == "C":
+        if cnn_model is None or svm_model is None:
+            raise RuntimeError("Pipeline C models not loaded.")
+        if image_input is None:
+            raise ValueError("Pipeline C requires image_input.")
+        return _pc.predict(image_input, cnn_model, svm_model)
+
+    # -----------------------------------------------------------
+    # Ensemble mode (default)
+    # -----------------------------------------------------------
+    results: list[PredictionResult] = []
+    proba_stack: list[list[float]] = []
+
+    # Step 1 — Pipeline A (always)
+    if xgb_model is not None:
+        res_a = _pa.predict(landmarks, xgb_model)
+        results.append(res_a)
+        proba_stack.append(res_a.probabilities)
+    else:
+        logger.warning("Pipeline A not available in ensemble mode.")
+        res_a = None
+
+    # Step 2 — Pipeline B if A confidence is low
+    current_conf = float(np.max(np.mean(proba_stack, axis=0))) if proba_stack else 0.0
+    if current_conf < confidence_threshold and encoder_model is not None and lgbm_model is not None:
+        res_b = _pb.predict(landmarks, encoder_model, lgbm_model)
+        results.append(res_b)
+        proba_stack.append(res_b.probabilities)
+
+    # Step 3 — Pipeline C if still low and image provided
+    current_conf = float(np.max(np.mean(proba_stack, axis=0))) if proba_stack else 0.0
+    if (
+        current_conf < secondary_threshold
+        and image_input is not None
+        and cnn_model is not None
+        and svm_model is not None
+    ):
+        res_c = _pc.predict(image_input, cnn_model, svm_model)
+        results.append(res_c)
+        proba_stack.append(res_c.probabilities)
+
+    # -----------------------------------------------------------
+    # Aggregate
+    # -----------------------------------------------------------
+    if not proba_stack:
+        raise RuntimeError("No inference pipeline could be executed.")
+
+    avg_proba = np.mean(proba_stack, axis=0)   # shape (34,)
+    idx  = int(np.argmax(avg_proba))
+    conf = float(avg_proba[idx])
+
+    pipeline_labels = "+".join(r.pipeline for r in results)
+    total_latency   = (time.perf_counter() - t0) * 1000
+
+    return PredictionResult(
+        sign=get_sign(idx),
+        confidence=conf,
+        label_index=idx,
+        probabilities=avg_proba.tolist(),
+        pipeline=pipeline_labels if len(results) > 1 else results[0].pipeline,
+        latency_ms=total_latency,
+    )
diff --git a/backend/app/inference/pipeline_a.py b/backend/app/inference/pipeline_a.py
new file mode 100644
index 0000000000000000000000000000000000000000..93d2bf1eb8a6d5bdc06d11dc125febc7d5dd8f75
--- /dev/null
+++ b/backend/app/inference/pipeline_a.py
@@ -0,0 +1,57 @@
+"""
+Pipeline A — XGBoost classifier on raw MediaPipe landmarks.
+
+Input  : 63 floats  [x0,y0,z0 … x20,y20,z20]  (already [0,1] normalised by MediaPipe)
+Output : PredictionResult
+"""
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass
+from typing import List
+
+import numpy as np
+
+from app.models.label_map import get_sign
+
+
+@dataclass
+class PredictionResult:
+    sign: str
+    confidence: float
+    label_index: int
+    probabilities: List[float]
+    pipeline: str
+    latency_ms: float
+
+
+def predict(landmarks: List[float], xgb_model) -> PredictionResult:
+    """
+    Run XGBoost inference on a flat 63-element landmark vector.
+
+    Parameters
+    ----------
+    landmarks : list of 63 floats
+    xgb_model : loaded XGBClassifier instance
+
+    Returns
+    -------
+    PredictionResult
+    """
+    t0 = time.perf_counter()
+
+    X = np.array(landmarks, dtype=np.float32).reshape(1, -1)   # shape (1, 63)
+    proba = xgb_model.predict_proba(X)[0]                       # shape (34,)
+    idx   = int(np.argmax(proba))
+    conf  = float(proba[idx])
+
+    latency = (time.perf_counter() - t0) * 1000
+
+    return PredictionResult(
+        sign=get_sign(idx),
+        confidence=conf,
+        label_index=idx,
+        probabilities=proba.tolist(),
+        pipeline="A",
+        latency_ms=latency,
+    )
diff --git a/backend/app/inference/pipeline_b.py b/backend/app/inference/pipeline_b.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ab2071f9dff68417de76f73c030b4d40509bdf2
--- /dev/null
+++ b/backend/app/inference/pipeline_b.py
@@ -0,0 +1,59 @@
+"""
+Pipeline B — Autoencoder encoder + LightGBM classifier.
+
+Input  : 63 floats  [x0,y0,z0 … x20,y20,z20]
+Process: Keras encoder compresses to 16-D bottleneck → LGBMClassifier
+Output : PredictionResult
+"""
+from __future__ import annotations
+
+import time
+import warnings
+from dataclasses import dataclass
+from typing import List, Any
+
+import numpy as np
+
+from app.models.label_map import get_sign
+from app.inference.pipeline_a import PredictionResult
+
+
+def predict(landmarks: List[float], encoder_model: Any, lgbm_model: Any) -> PredictionResult:
+    """
+    Run the autoencoder-encoder → LightGBM inference chain.
+
+    Parameters
+    ----------
+    landmarks     : list of 63 floats
+    encoder_model : Keras Model (input 63→output 16, bottleneck sub-model)
+    lgbm_model    : loaded LGBMClassifier instance
+
+    Returns
+    -------
+    PredictionResult
+    """
+    t0 = time.perf_counter()
+
+    X = np.array(landmarks, dtype=np.float32).reshape(1, -1)   # (1, 63)
+
+    # Encode to 16-D bottleneck (suppress verbose Keras progress bar)
+    features = encoder_model(X, training=False).numpy()         # (1, 16)
+
+    # LightGBM classify — suppress sklearn feature-name warning (model was
+    # fitted with a named DataFrame; numpy array input is perfectly valid)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", UserWarning)
+        proba = lgbm_model.predict_proba(features)[0]           # (34,)
+    idx   = int(np.argmax(proba))
+    conf  = float(proba[idx])
+
+    latency = (time.perf_counter() - t0) * 1000
+
+    return PredictionResult(
+        sign=get_sign(idx),
+        confidence=conf,
+        label_index=idx,
+        probabilities=proba.tolist(),
+        pipeline="B",
+        latency_ms=latency,
+    )
diff --git a/backend/app/inference/pipeline_c.py b/backend/app/inference/pipeline_c.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb09a692fae2a135b77d7d9a6991441449f51951
--- /dev/null
+++ b/backend/app/inference/pipeline_c.py
@@ -0,0 +1,86 @@
+"""
+Pipeline C — Pre-trained CNN (ResNet50) feature extractor + SVM classifier.
+
+Input  : base-64 encoded JPEG string OR raw bytes of a 128×128 RGB hand-crop.
+Process: Decode → normalise → CNN (256-D features) → SVC.predict_proba
+Output : PredictionResult
+
+Note: This pipeline is significantly slower (~100–300 ms on CPU) and is only
+invoked as a fallback when landmark-based pipelines have low confidence.
+"""
+from __future__ import annotations
+
+import base64
+import io
+import time
+from dataclasses import dataclass
+from typing import Any, List, Union
+
+import numpy as np
+from PIL import Image
+
+from app.models.label_map import get_sign
+from app.inference.pipeline_a import PredictionResult
+
+# Target input size expected by the CNN (ResNet50 Functional model)
+CNN_IMG_SIZE: int = 128
+
+
+def _decode_image(image_input: Union[str, bytes]) -> np.ndarray:
+    """
+    Accept either:
+      - A base-64 encoded JPEG string  (from WebSocket JSON payload)
+      - Raw bytes                       (from HTTP multipart)
+    Returns a (128, 128, 3) float32 array normalised to [0, 1].
+    """
+    if isinstance(image_input, str):
+        raw = base64.b64decode(image_input)
+    else:
+        raw = image_input
+
+    img = Image.open(io.BytesIO(raw)).convert("RGB")
+    img = img.resize((CNN_IMG_SIZE, CNN_IMG_SIZE), Image.LANCZOS)
+    arr = np.array(img, dtype=np.float32) / 255.0
+    return arr   # (128, 128, 3)
+
+
+def predict(
+    image_input: Union[str, bytes],
+    cnn_model: Any,
+    svm_model: Any,
+) -> PredictionResult:
+    """
+    Run the CNN + SVM inference pipeline.
+
+    Parameters
+    ----------
+    image_input : base-64 JPEG string or raw bytes of the hand crop (any size; will be resized)
+    cnn_model   : Keras Functional model (ResNet50-based, output 256-D feature vector)
+    svm_model   : loaded SVC(C=10, probability=True) instance
+
+    Returns
+    -------
+    PredictionResult
+    """
+    t0 = time.perf_counter()
+
+    img = _decode_image(image_input)           # (128, 128, 3)
+    batch = img[np.newaxis]                    # (1, 128, 128, 3)
+
+    # CNN forward pass — directly call model (avoids Keras verbose logging)
+    features = cnn_model(batch, training=False).numpy()   # (1, 256)
+
+    proba = svm_model.predict_proba(features)[0]          # (34,)
+    idx   = int(np.argmax(proba))
+    conf  = float(proba[idx])
+
+    latency = (time.perf_counter() - t0) * 1000
+
+    return PredictionResult(
+        sign=get_sign(idx),
+        confidence=conf,
+        label_index=idx,
+        probabilities=proba.tolist(),
+        pipeline="C",
+        latency_ms=latency,
+    )
diff --git a/backend/app/main.py b/backend/app/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..22ab65a0143363c853a9dee6da4a01e670b318f4
--- /dev/null
+++ b/backend/app/main.py
@@ -0,0 +1,280 @@
+"""
+SanketSetu FastAPI backend — entry point.
+
+Endpoints
+---------
+GET  /health                    → HealthResponse
+WS   /ws/landmarks              → real-time sign recognition (landmark stream)
+WS   /ws/image                  → image-based sign recognition (Pipeline C)
+POST /api/predict               → REST fallback for landmark inference
+POST /api/predict/image         → REST fallback for image inference
+"""
+from __future__ import annotations
+
+import json
+import logging
+import os
+import time
+from contextlib import asynccontextmanager
+from pathlib import Path
+from typing import Any
+
+# Load .env if present (before config is imported so env vars are available)
+try:
+    from dotenv import load_dotenv
+    _env_file = Path(__file__).resolve().parent.parent / ".env"
+    if _env_file.exists():
+        load_dotenv(_env_file)
+except ImportError:
+    pass  # python-dotenv not installed; rely on shell env
+
+import numpy as np
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+
+from app import config
+from app.models.loader import load_models, get_model_store
+from app.schemas import (
+    LandmarkMessage,
+    ImageMessage,
+    EnsembleMessage,
+    PredictionResponse,
+    HealthResponse,
+    ErrorResponse,
+)
+import app.inference.ensemble as ensemble
+
+# ---------------------------------------------------------------------------
+# Logging
+# ---------------------------------------------------------------------------
+logging.basicConfig(
+    level=getattr(logging, config.LOG_LEVEL, logging.INFO),
+    format="%(asctime)s  %(levelname)-8s  %(name)s — %(message)s",
+)
+logger = logging.getLogger("sanketsetu")
+
+# Silence noisy TF / Keras output
+os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "3")
+os.environ.setdefault("KERAS_BACKEND", "tensorflow")
+logging.getLogger("tensorflow").setLevel(logging.ERROR)
+logging.getLogger("keras").setLevel(logging.ERROR)
+
+
+# ---------------------------------------------------------------------------
+# Lifespan — load models on startup
+# ---------------------------------------------------------------------------
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    logger.info("Starting SanketSetu backend …")
+    load_models()
+    logger.info("Models ready. Server accepting connections.")
+    yield
+    logger.info("Shutting down.")
+
+
+# ---------------------------------------------------------------------------
+# App
+# ---------------------------------------------------------------------------
+app = FastAPI(
+    title="SanketSetu API",
+    description="Real-time Gujarati Sign Language recognition backend",
+    version="1.0.0",
+    lifespan=lifespan,
+)
+
+# CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=config.CORS_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# ---------------------------------------------------------------------------
+# Global exception handler
+# ---------------------------------------------------------------------------
+@app.exception_handler(Exception)
+async def global_exception_handler(request: Request, exc: Exception):
+    logger.exception("Unhandled error: %s", exc)
+    return JSONResponse(
+        status_code=500,
+        content=ErrorResponse(error="Internal server error", detail=str(exc)).model_dump(),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _run_ensemble(
+    landmarks: list[float],
+    image_b64: str | None = None,
+) -> PredictionResponse:
+    store = get_model_store()
+    result = ensemble.run(
+        landmarks,
+        image_input=image_b64,
+        xgb_model=store.xgb_model,
+        encoder_model=store.encoder_model,
+        lgbm_model=store.lgbm_model,
+        cnn_model=store.cnn_model,
+        svm_model=store.svm_model,
+        pipeline_mode=config.PIPELINE_MODE,
+        confidence_threshold=config.CONFIDENCE_THRESHOLD,
+        secondary_threshold=config.SECONDARY_THRESHOLD,
+    )
+    return PredictionResponse(
+        sign=result.sign,
+        confidence=result.confidence,
+        pipeline=result.pipeline,
+        label_index=result.label_index,
+        probabilities=result.probabilities,
+        latency_ms=result.latency_ms,
+    )
+
+
+def _available_pipelines() -> list[str]:
+    try:
+        store = get_model_store()
+    except RuntimeError:
+        return []
+    pipelines = []
+    if store.xgb_model is not None:
+        pipelines.append("A")
+    if store.encoder_model is not None and store.lgbm_model is not None:
+        pipelines.append("B")
+    if store.cnn_model is not None and store.svm_model is not None:
+        pipelines.append("C")
+    return pipelines
+
+
+# ---------------------------------------------------------------------------
+# REST endpoints
+# ---------------------------------------------------------------------------
+
+@app.get("/health", response_model=HealthResponse)
+async def health():
+    try:
+        store = get_model_store()
+        loaded = store.loaded
+    except RuntimeError:
+        loaded = False
+    return HealthResponse(
+        status="ok" if loaded else "loading",
+        models_loaded=loaded,
+        pipelines_available=_available_pipelines(),
+    )
+
+
+@app.post("/api/predict", response_model=PredictionResponse)
+async def predict_landmarks(body: LandmarkMessage):
+    """REST fallback: send 63 landmark floats, receive prediction."""
+    return _run_ensemble(body.landmarks)
+
+
+@app.post("/api/predict/image", response_model=PredictionResponse)
+async def predict_image(body: ImageMessage):
+    """REST fallback: send a base-64 hand crop, receive prediction via Pipeline C."""
+    store = get_model_store()
+    if store.cnn_model is None or store.svm_model is None:
+        raise HTTPException(status_code=503, detail="Pipeline C (CNN+SVM) is not available.")
+    import app.inference.pipeline_c as _pc
+    result = _pc.predict(body.image_b64, store.cnn_model, store.svm_model)
+    return PredictionResponse(
+        sign=result.sign,
+        confidence=result.confidence,
+        pipeline=result.pipeline,
+        label_index=result.label_index,
+        probabilities=result.probabilities,
+        latency_ms=result.latency_ms,
+    )
+
+
+# ---------------------------------------------------------------------------
+# WebSocket — landmark stream  /ws/landmarks
+# ---------------------------------------------------------------------------
+
+@app.websocket("/ws/landmarks")
+async def ws_landmarks(ws: WebSocket):
+    """
+    Primary real-time endpoint.
+    Client sends: {"landmarks": [...63 floats...], "session_id": "..."}
+    Server replies: PredictionResponse JSON
+    """
+    await ws.accept()
+    session_id = "unknown"
+    try:
+        while True:
+            raw = await ws.receive_text()
+            try:
+                data = json.loads(raw)
+                msg  = LandmarkMessage(**data)
+                session_id = msg.session_id
+
+                response = _run_ensemble(msg.landmarks)
+                await ws.send_text(response.model_dump_json())
+
+            except ValueError as ve:
+                await ws.send_text(
+                    ErrorResponse(error="Validation error", detail=str(ve)).model_dump_json()
+                )
+            except Exception as e:
+                logger.error("[%s] Inference error: %s", session_id, e, exc_info=True)
+                await ws.send_text(
+                    ErrorResponse(error="Inference failed", detail=str(e)).model_dump_json()
+                )
+
+    except WebSocketDisconnect:
+        logger.info("Client disconnected: %s", session_id)
+
+
+# ---------------------------------------------------------------------------
+# WebSocket — image stream  /ws/image  (Pipeline C)
+# ---------------------------------------------------------------------------
+
+@app.websocket("/ws/image")
+async def ws_image(ws: WebSocket):
+    """
+    Image-based endpoint for Pipeline C (CNN+SVM).
+    Client sends: {"image_b64": "<base64 JPEG>", "session_id": "..."}
+    """
+    await ws.accept()
+    session_id = "unknown"
+    try:
+        while True:
+            raw = await ws.receive_text()
+            try:
+                data = json.loads(raw)
+                msg  = ImageMessage(**data)
+                session_id = msg.session_id
+
+                store = get_model_store()
+                if store.cnn_model is None or store.svm_model is None:
+                    await ws.send_text(
+                        ErrorResponse(error="Pipeline C not available").model_dump_json()
+                    )
+                    continue
+
+                import app.inference.pipeline_c as _pc
+                result = _pc.predict(msg.image_b64, store.cnn_model, store.svm_model)
+                response = PredictionResponse(
+                    sign=result.sign,
+                    confidence=result.confidence,
+                    pipeline=result.pipeline,
+                    label_index=result.label_index,
+                    probabilities=result.probabilities,
+                    latency_ms=result.latency_ms,
+                )
+                await ws.send_text(response.model_dump_json())
+
+            except Exception as e:
+                logger.error("[%s] Image inference error: %s", session_id, e, exc_info=True)
+                await ws.send_text(
+                    ErrorResponse(error="Inference failed", detail=str(e)).model_dump_json()
+                )
+
+    except WebSocketDisconnect:
+        logger.info("Image client disconnected: %s", session_id)
diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..287bb0fb722ccc194daf2345bc8c55e62a60b7fd
--- /dev/null
+++ b/backend/app/models/__init__.py
@@ -0,0 +1 @@
+# backend/app/models/__init__.py
diff --git a/backend/app/models/__pycache__/__init__.cpython-312.pyc b/backend/app/models/__pycache__/__init__.cpython-312.pyc
new file mode 100644
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diff --git a/backend/app/models/__pycache__/label_map.cpython-312.pyc b/backend/app/models/__pycache__/label_map.cpython-312.pyc
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diff --git a/backend/app/models/__pycache__/loader.cpython-312.pyc b/backend/app/models/__pycache__/loader.cpython-312.pyc
new file mode 100644
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diff --git a/backend/app/models/label_map.py b/backend/app/models/label_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6c5dbc460be4f0e6ae612446b57a14b5adfdf66
--- /dev/null
+++ b/backend/app/models/label_map.py
@@ -0,0 +1,58 @@
+"""
+Label map: index 0–33 → Gujarati sign name.
+
+The 34 classes cover the Gujarati consonant alphabet (ક–ળ / ક-ળ) as used in the
+training dataset.  Verify this order against your original data-collection script /
+notebook — if your dataset folder names differ, update the list below.
+
+Current assumption: classes are sorted by the Gujarati alphabet order (Unicode order
+of the Unicode Gujarati block, U+0A80–U+0AFF).
+"""
+from __future__ import annotations
+
+# ---- Primary label map (index → Gujarati character / word) -----------------
+# 34 classes: consonants + a few vowel signs used as standalone signs
+LABEL_MAP: dict[int, str] = {
+    0:  "ક",   # ka
+    1:  "ખ",   # kha
+    2:  "ગ",   # ga
+    3:  "ઘ",   # gha
+    4:  "ચ",   # cha
+    5:  "છ",   # chha
+    6:  "જ",   # ja
+    7:  "ઝ",   # jha
+    8:  "ટ",   # ṭa
+    9:  "ઠ",   # ṭha
+    10: "ડ",   # ḍa
+    11: "ઢ",   # ḍha
+    12: "ણ",   # ṇa
+    13: "ત",   # ta
+    14: "થ",   # tha
+    15: "દ",   # da
+    16: "ધ",   # dha
+    17: "ન",   # na
+    18: "પ",   # pa
+    19: "ફ",   # pha
+    20: "બ",   # ba
+    21: "ભ",   # bha
+    22: "મ",   # ma
+    23: "ય",   # ya
+    24: "ર",   # ra
+    25: "લ",   # la
+    26: "વ",   # va
+    27: "શ",   # sha
+    28: "ષ",   # ṣha
+    29: "સ",   # sa
+    30: "હ",   # ha
+    31: "ળ",   # ḷa
+    32: "ક્ષ", # ksha (conjunct)
+    33: "જ્ઞ", # gna  (conjunct)
+}
+
+# Reverse map: sign name → index (useful for testing)
+REVERSE_MAP: dict[str, int] = {v: k for k, v in LABEL_MAP.items()}
+
+
+def get_sign(label_index: int) -> str:
+    """Return the Gujarati sign for the given class index."""
+    return LABEL_MAP.get(label_index, f"[{label_index}]")
diff --git a/backend/app/models/loader.py b/backend/app/models/loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..82e93b560480c28503c3191605bb13ca90d2f740
--- /dev/null
+++ b/backend/app/models/loader.py
@@ -0,0 +1,188 @@
+"""
+Singleton model store — loads all model artifacts once at startup and holds them
+in memory for the lifetime of the process.
+
+Usage inside FastAPI:
+    from app.models.loader import get_model_store
+    store = get_model_store()          # dependency injection or direct call
+"""
+from __future__ import annotations
+
+import logging
+import os
+import pickle
+import time
+from dataclasses import dataclass, field
+from typing import Any
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Data class that holds every loaded artifact
+# ---------------------------------------------------------------------------
+
+@dataclass
+class ModelStore:
+    # Pipeline A
+    xgb_model: Any = field(default=None)
+
+    # Pipeline B
+    encoder_model: Any = field(default=None)   # Keras sub-model (encoder half)
+    lgbm_model: Any = field(default=None)
+
+    # Pipeline C
+    cnn_model: Any = field(default=None)        # Keras ResNet50 feature extractor
+    svm_model: Any = field(default=None)
+
+    loaded: bool = field(default=False)
+
+
+# Module-level singleton
+_store: ModelStore | None = None
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+def _load_pickle(path: str, label: str) -> Any:
+    t0 = time.perf_counter()
+    with open(path, "rb") as f:
+        obj = pickle.load(f)
+    elapsed = (time.perf_counter() - t0) * 1000
+    logger.info("Loaded %-35s  (%.1f ms)", label, elapsed)
+    return obj
+
+
+def _build_encoder(autoencoder_pkl_path: str) -> Any:
+    """
+    Load the full autoencoder from pickle and extract the encoder sub-model.
+    The autoencoder is a Keras Sequential:
+        InputLayer  (63)
+        Dense 32 relu      ← layer index 0
+        Dense 16 relu      ← layer index 1  ← bottleneck output
+        Dense 32 relu
+        Dense 63 linear
+    We build a Keras Model that maps input → output of the bottleneck Dense.
+    """
+    import os
+    os.environ.setdefault("KERAS_BACKEND", "tensorflow")
+    os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "3")
+
+    full_ae = _load_pickle(autoencoder_pkl_path, "autoencoder_model.pkl")
+
+    # Dynamically import keras after env vars are set
+    import keras
+
+    # The Sequential model's built layers: 0=Dense(32), 1=Dense(16), 2=Dense(32), 3=Dense(63)
+    # layer index 1 output is the 16-D bottleneck.
+    # We can't use full_ae.input directly on a Sequential that was pickled without
+    # a traced input tensor, so we wire the layers manually.
+    import numpy as _np
+    inp = keras.Input(shape=(63,), name="encoder_input")
+    x = full_ae.layers[0](inp)   # Dense(32, relu)
+    x = full_ae.layers[1](x)     # Dense(16, relu) — bottleneck
+    encoder = keras.Model(inputs=inp, outputs=x, name="encoder_only")
+    logger.info("Built encoder sub-model: input(%s) → output(%s)", encoder.input_shape, encoder.output_shape)
+    return encoder
+
+
+def _build_cnn_feature_extractor(cnn_pkl_path: str) -> Any:
+    """
+    Load the full CNN (ResNet50 Functional model) from pickle and return a
+    sub-model that outputs the 256-D penultimate Dense layer.
+
+    Architecture (tail of the model):
+        … ResNet50 backbone …
+        GlobalAveragePooling2D
+        Dropout(0.5)
+        Dense(256, relu)       ← feature vector we want
+        Dropout(0.5)
+        Dense(34, softmax)     ← final classification head (skip this)
+
+    The SVC was trained on the 256-D features, so we must stop before the
+    final Dense(34) layer.
+    """
+    import os
+    os.environ.setdefault("KERAS_BACKEND", "tensorflow")
+    os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "3")
+
+    import keras
+
+    full_cnn = _load_pickle(cnn_pkl_path, "cnn_model.pkl")
+
+    # Find the Dense(256) layer by scanning from the end
+    feature_layer = None
+    for layer in reversed(full_cnn.layers):
+        cfg = layer.get_config()
+        if layer.__class__.__name__ == 'Dense' and cfg.get('units') == 256:
+            feature_layer = layer
+            break
+
+    if feature_layer is None:
+        logger.warning(
+            "Could not find Dense(256) layer; using full CNN output as features."
+        )
+        return full_cnn
+
+    extractor = keras.Model(
+        inputs=full_cnn.input,
+        outputs=feature_layer.output,
+        name="cnn_feature_extractor",
+    )
+    logger.info(
+        "CNN feature extractor: input %s → output %s",
+        extractor.input_shape,
+        extractor.output_shape,
+    )
+    return extractor
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+def load_models() -> ModelStore:
+    """
+    Load all models and return a populated ModelStore.
+    Call this once from the FastAPI lifespan event.
+    """
+    global _store
+
+    from app import config  # local import to avoid circular at module level
+
+    store = ModelStore()
+
+    # ---- Pipeline A --------------------------------------------------------
+    if os.path.exists(config.PIPELINE_A_MODEL):
+        store.xgb_model = _load_pickle(config.PIPELINE_A_MODEL, "xgb model.pkl")
+    else:
+        logger.warning("Pipeline A model not found: %s", config.PIPELINE_A_MODEL)
+
+    # ---- Pipeline B --------------------------------------------------------
+    if os.path.exists(config.PIPELINE_B_AE) and os.path.exists(config.PIPELINE_B_LGBM):
+        store.encoder_model = _build_encoder(config.PIPELINE_B_AE)
+        store.lgbm_model    = _load_pickle(config.PIPELINE_B_LGBM, "lgbm_model.pkl")
+    else:
+        logger.warning("Pipeline B models not found — B will be skipped.")
+
+    # ---- Pipeline C --------------------------------------------------------
+    if os.path.exists(config.PIPELINE_C_CNN) and os.path.exists(config.PIPELINE_C_SVM):
+        store.cnn_model = _build_cnn_feature_extractor(config.PIPELINE_C_CNN)
+        store.svm_model = _load_pickle(config.PIPELINE_C_SVM, "svm_model.pkl")
+    else:
+        logger.warning("Pipeline C models not found — C will be skipped.")
+
+    store.loaded = True
+    logger.info("All models loaded successfully.")
+    _store = store
+    return store
+
+
+def get_model_store() -> ModelStore:
+    """Return the singleton ModelStore (must have been loaded via load_models() first)."""
+    if _store is None or not _store.loaded:
+        raise RuntimeError("ModelStore has not been initialised — call load_models() first.")
+    return _store
diff --git a/backend/app/schemas.py b/backend/app/schemas.py
new file mode 100644
index 0000000000000000000000000000000000000000..47f9406601c5fac2bae76dc720f940c26ac5cbc5
--- /dev/null
+++ b/backend/app/schemas.py
@@ -0,0 +1,74 @@
+"""
+Pydantic request / response schemas for SanketSetu backend.
+"""
+from __future__ import annotations
+
+from typing import List, Optional
+
+from pydantic import BaseModel, Field, field_validator
+
+
+# ---------------------------------------------------------------------------
+# Requests
+# ---------------------------------------------------------------------------
+
+class LandmarkMessage(BaseModel):
+    """
+    Payload sent by the client over /ws/landmarks or POST /api/predict.
+    'landmarks' is a flat list of [x0,y0,z0, x1,y1,z1, ..., x20,y20,z20]
+    extracted by MediaPipe Hands on the browser side.
+    """
+    landmarks: List[float] = Field(..., min_length=63, max_length=63)
+    session_id: str = Field(default="default")
+
+    @field_validator("landmarks")
+    @classmethod
+    def must_be_63_floats(cls, v: List[float]) -> List[float]:
+        if len(v) != 63:
+            raise ValueError(f"landmarks must contain exactly 63 values, got {len(v)}")
+        return v
+
+
+class ImageMessage(BaseModel):
+    """
+    Payload sent when Pipeline C (CNN+SVM) is invoked via /ws/image.
+    'image_b64' is a base-64 encoded JPEG of the cropped hand region (128×128).
+    """
+    image_b64: str = Field(..., description="Base-64 encoded JPEG of the hand crop (128×128 px)")
+    session_id: str = Field(default="default")
+
+
+class EnsembleMessage(BaseModel):
+    """
+    Combined payload: landmarks + optional image for the full ensemble pipeline.
+    """
+    landmarks: List[float] = Field(..., min_length=63, max_length=63)
+    image_b64: Optional[str] = Field(default=None)
+    session_id: str = Field(default="default")
+
+
+# ---------------------------------------------------------------------------
+# Responses
+# ---------------------------------------------------------------------------
+
+class PredictionResponse(BaseModel):
+    sign: str = Field(..., description="Gujarati sign character(s)")
+    confidence: float = Field(..., ge=0.0, le=1.0)
+    pipeline: str = Field(..., description="Which pipeline(s) produced this result: A, B, C, or ensemble")
+    label_index: int = Field(..., ge=0, le=33)
+    probabilities: Optional[List[float]] = Field(
+        default=None,
+        description="Full 34-class probability vector (optional, increases payload size)"
+    )
+    latency_ms: Optional[float] = Field(default=None, description="Server-side inference latency in ms")
+
+
+class HealthResponse(BaseModel):
+    status: str
+    models_loaded: bool
+    pipelines_available: List[str]
+
+
+class ErrorResponse(BaseModel):
+    error: str
+    detail: Optional[str] = None
diff --git a/backend/requirements-dev.txt b/backend/requirements-dev.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7fec44858704c1417cd075dbc54a1111cec51834
--- /dev/null
+++ b/backend/requirements-dev.txt
@@ -0,0 +1,4 @@
+# Development + testing dependencies (not required in production)
+pytest>=9.0
+httpx>=0.28
+pytest-anyio
diff --git a/backend/requirements.txt b/backend/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2509841b658cf79cca6a1dd208a0c14f0c2d0ed9
--- /dev/null
+++ b/backend/requirements.txt
@@ -0,0 +1,12 @@
+fastapi>=0.115.0
+uvicorn[standard]>=0.30.0
+websockets>=12.0
+xgboost>=2.0.0
+lightgbm>=4.3.0
+scikit-learn>=1.4.0
+keras==3.13.2
+tensorflow-cpu>=2.20.0
+numpy>=1.26.0
+opencv-python-headless>=4.9.0
+pillow>=10.3.0
+python-dotenv>=1.0.0
diff --git a/backend/tests/__init__.py b/backend/tests/__init__.py
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diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b8dfd36fc755c94cfbe8c278a4059f476d79b20
--- /dev/null
+++ b/backend/tests/conftest.py
@@ -0,0 +1,76 @@
+"""
+conftest.py — shared pytest fixtures for SanketSetu backend tests.
+
+All model fixtures use session scope so the (slow) models are loaded only once
+per pytest run.  Tests that need models are automatically skipped when the
+pickle files are not found (CI without model artifacts).
+"""
+from __future__ import annotations
+
+import os
+import sys
+from pathlib import Path
+
+import pytest
+
+# Make sure `app.*` imports resolve correctly when tests run from backend/
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+# Set env vars before any TF/Keras import
+os.environ.setdefault("KERAS_BACKEND",        "tensorflow")
+os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "3")
+os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
+os.environ.setdefault("TF_ENABLE_ONEDNN_OPTS","0")
+
+from app import config                               # noqa: E402
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _skip_if_missing(path: str, name: str):
+    if not Path(path).exists():
+        pytest.skip(f"Model file not found: {path}  ({name} skipped)")
+
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+@pytest.fixture(scope="session")
+def xgb_model():
+    _skip_if_missing(config.PIPELINE_A_MODEL, "xgb")
+    import pickle
+    with open(config.PIPELINE_A_MODEL, "rb") as f:
+        return pickle.load(f)
+
+
+@pytest.fixture(scope="session")
+def encoder_model():
+    _skip_if_missing(config.PIPELINE_B_AE, "autoencoder")
+    from app.models.loader import _build_encoder       # access internal helper
+    return _build_encoder(config.PIPELINE_B_AE)
+
+
+@pytest.fixture(scope="session")
+def lgbm_model():
+    _skip_if_missing(config.PIPELINE_B_LGBM, "lgbm")
+    import pickle
+    with open(config.PIPELINE_B_LGBM, "rb") as f:
+        return pickle.load(f)
+
+
+@pytest.fixture(scope="session")
+def cnn_model():
+    _skip_if_missing(config.PIPELINE_C_CNN, "cnn")
+    from app.models.loader import _build_cnn_feature_extractor
+    return _build_cnn_feature_extractor(config.PIPELINE_C_CNN)
+
+
+@pytest.fixture(scope="session")
+def svm_model():
+    _skip_if_missing(config.PIPELINE_C_SVM, "svm")
+    import pickle
+    with open(config.PIPELINE_C_SVM, "rb") as f:
+        return pickle.load(f)
diff --git a/backend/tests/test_pipeline_a.py b/backend/tests/test_pipeline_a.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4204ad7e512e579868e6ef7d1cdaf7026f8551c
--- /dev/null
+++ b/backend/tests/test_pipeline_a.py
@@ -0,0 +1,66 @@
+"""
+tests/test_pipeline_a.py — XGBoost landmark inference.
+"""
+from __future__ import annotations
+
+import pytest
+from app.inference.pipeline_a import predict
+from app.models.label_map import LABEL_MAP
+
+
+# ---------------------------------------------------------------------------
+# Common fixtures
+# ---------------------------------------------------------------------------
+
+ZEROS_63  = [0.0] * 63
+RAND_63   = [float(i % 11) / 10.0 for i in range(63)]
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+class TestPipelineA:
+
+    def test_predict_zeros(self, xgb_model):
+        """All-zero landmarks should return a valid PredictionResult."""
+        result = predict(ZEROS_63, xgb_model)
+        assert result.sign is not None
+        assert 0.0 <= result.confidence <= 1.0
+        assert 0 <= result.label_index <= 33
+        assert result.pipeline == "A"
+        assert result.latency_ms > 0
+
+    def test_predict_random_input(self, xgb_model):
+        result = predict(RAND_63, xgb_model)
+        assert 0.0 <= result.confidence <= 1.0
+        assert 0 <= result.label_index <= 33
+
+    def test_probabilities_sum_to_one(self, xgb_model):
+        result = predict(RAND_63, xgb_model)
+        assert result.probabilities is not None
+        total = sum(result.probabilities)
+        assert abs(total - 1.0) < 1e-4, f"Probabilities sum to {total}"
+
+    def test_probabilities_length(self, xgb_model):
+        result = predict(RAND_63, xgb_model)
+        assert len(result.probabilities) == 34
+
+    def test_sign_in_label_map(self, xgb_model):
+        result = predict(RAND_63, xgb_model)
+        assert result.sign == LABEL_MAP.get(result.label_index, "?"), (
+            f"sign '{result.sign}' doesn't match label_map[{result.label_index}]"
+        )
+
+    def test_confidence_equals_max_prob(self, xgb_model):
+        result = predict(RAND_63, xgb_model)
+        assert abs(result.confidence - max(result.probabilities)) < 1e-6
+
+    @pytest.mark.parametrize("bad_input", [
+        [0.0] * 62,   # too short
+        [0.0] * 64,   # too long
+    ])
+    def test_rejects_wrong_length(self, xgb_model, bad_input):
+        """XGBoost should raise if input has wrong feature count."""
+        with pytest.raises(Exception):
+            predict(bad_input, xgb_model)
diff --git a/backend/tests/test_pipeline_b.py b/backend/tests/test_pipeline_b.py
new file mode 100644
index 0000000000000000000000000000000000000000..e87012b6a2c68d7ad0b49ea4cdf2c4dd922933f4
--- /dev/null
+++ b/backend/tests/test_pipeline_b.py
@@ -0,0 +1,49 @@
+"""
+tests/test_pipeline_b.py — Autoencoder encoder + LightGBM inference.
+"""
+from __future__ import annotations
+
+import pytest
+from app.inference.pipeline_b import predict
+
+
+ZEROS_63 = [0.0] * 63
+RAND_63  = [float(i % 11) / 10.0 for i in range(63)]
+
+
+class TestPipelineB:
+
+    def test_predict_zeros(self, encoder_model, lgbm_model):
+        result = predict(ZEROS_63, encoder_model, lgbm_model)
+        assert result.sign is not None
+        assert 0.0 <= result.confidence <= 1.0
+        assert 0 <= result.label_index <= 33
+        assert result.pipeline == "B"
+        assert result.latency_ms > 0
+
+    def test_predict_random(self, encoder_model, lgbm_model):
+        result = predict(RAND_63, encoder_model, lgbm_model)
+        assert 0.0 <= result.confidence <= 1.0
+
+    def test_probabilities_sum_to_one(self, encoder_model, lgbm_model):
+        result = predict(RAND_63, encoder_model, lgbm_model)
+        assert result.probabilities is not None
+        total = sum(result.probabilities)
+        assert abs(total - 1.0) < 1e-4, f"Probabilities sum to {total}"
+
+    def test_probabilities_length(self, encoder_model, lgbm_model):
+        result = predict(RAND_63, encoder_model, lgbm_model)
+        assert len(result.probabilities) == 34
+
+    def test_encoder_output_shape(self, encoder_model):
+        """Encoder sub-model must produce a 16-D vector."""
+        import numpy as np
+        X = np.zeros((1, 63), dtype="float32")
+        out = encoder_model(X, training=False).numpy()
+        assert out.shape == (1, 16), f"Unexpected encoder output shape: {out.shape}"
+
+    def test_different_inputs_give_different_outputs(self, encoder_model, lgbm_model):
+        result_a = predict(ZEROS_63, encoder_model, lgbm_model)
+        result_b = predict([0.5] * 63, encoder_model, lgbm_model)
+        # At least the probabilities should differ (not a constant model)
+        assert result_a.probabilities != result_b.probabilities
diff --git a/backend/tests/test_pipeline_c.py b/backend/tests/test_pipeline_c.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c84cdb5b13440579a43ea086cc9d14a68f282b9
--- /dev/null
+++ b/backend/tests/test_pipeline_c.py
@@ -0,0 +1,85 @@
+"""
+tests/test_pipeline_c.py — ResNet50 CNN feature extractor + SVM inference.
+"""
+from __future__ import annotations
+
+import base64
+import io
+
+import numpy as np
+import pytest
+
+from app.inference.pipeline_c import predict
+
+
+# ---------------------------------------------------------------------------
+# Helpers — build dummy images
+# ---------------------------------------------------------------------------
+
+def _black_128_b64() -> str:
+    """128×128 black image as base64-encoded JPEG."""
+    from PIL import Image
+    img = Image.fromarray(np.zeros((128, 128, 3), dtype=np.uint8), mode="RGB")
+    buf = io.BytesIO()
+    img.save(buf, format="JPEG")
+    return base64.b64encode(buf.getvalue()).decode()
+
+
+def _noise_128_b64() -> str:
+    """128×128 random-noise image as base64-encoded JPEG."""
+    from PIL import Image
+    rng = np.random.default_rng(42)
+    arr = rng.integers(0, 255, (128, 128, 3), dtype=np.uint8)
+    img = Image.fromarray(arr, mode="RGB")
+    buf = io.BytesIO()
+    img.save(buf, format="JPEG")
+    return base64.b64encode(buf.getvalue()).decode()
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+class TestPipelineC:
+
+    def test_predict_black_image(self, cnn_model, svm_model):
+        result = predict(_black_128_b64(), cnn_model, svm_model)
+        assert result.sign is not None
+        assert 0.0 <= result.confidence <= 1.0
+        assert 0 <= result.label_index <= 33
+        assert result.pipeline == "C"
+        assert result.latency_ms > 0
+
+    def test_predict_noise_image(self, cnn_model, svm_model):
+        result = predict(_noise_128_b64(), cnn_model, svm_model)
+        assert 0.0 <= result.confidence <= 1.0
+
+    def test_probabilities_sum_to_one(self, cnn_model, svm_model):
+        result = predict(_noise_128_b64(), cnn_model, svm_model)
+        assert result.probabilities is not None
+        total = sum(result.probabilities)
+        assert abs(total - 1.0) < 1e-4, f"Probabilities sum to {total}"
+
+    def test_probabilities_length(self, cnn_model, svm_model):
+        result = predict(_black_128_b64(), cnn_model, svm_model)
+        assert len(result.probabilities) == 34
+
+    def test_cnn_feature_shape(self, cnn_model):
+        """CNN feature extractor must output 256-D vector."""
+        inp = np.zeros((1, 128, 128, 3), dtype="float32")
+        out = cnn_model(inp, training=False).numpy()
+        assert out.shape == (1, 256), f"Unexpected CNN output shape: {out.shape}"
+
+    def test_invalid_b64_raises(self, cnn_model, svm_model):
+        with pytest.raises(Exception):
+            predict("not-valid-base64!!", cnn_model, svm_model)
+
+    def test_wrong_size_image_is_resized(self, cnn_model, svm_model):
+        """A 64×64 image should be auto-resized to 128×128 without error."""
+        from PIL import Image
+        img = Image.fromarray(np.zeros((64, 64, 3), dtype=np.uint8), mode="RGB")
+        buf = io.BytesIO()
+        img.save(buf, format="JPEG")
+        b64 = base64.b64encode(buf.getvalue()).decode()
+        result = predict(b64, cnn_model, svm_model)
+        assert 0.0 <= result.confidence <= 1.0
diff --git a/backend/tests/test_websocket.py b/backend/tests/test_websocket.py
new file mode 100644
index 0000000000000000000000000000000000000000..a38f25807b0468ab2396b64cdecd26efcea2cbce
--- /dev/null
+++ b/backend/tests/test_websocket.py
@@ -0,0 +1,117 @@
+"""
+tests/test_websocket.py — WebSocket integration tests using FastAPI TestClient.
+
+These tests use httpx + anyio to exercise the full WebSocket round-trip without
+a running server.  Models are loaded via the lifespan context, so model fixtures
+are NOT used here — the app loads models itself.
+
+Skip the entire module when the model files are absent (CI).
+"""
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+
+import pytest
+
+# Skip module if any model file is missing
+from app import config as _cfg
+
+_REQUIRED = [
+    _cfg.PIPELINE_A_MODEL,
+    _cfg.PIPELINE_B_AE,
+    _cfg.PIPELINE_B_LGBM,
+    _cfg.PIPELINE_C_CNN,
+    _cfg.PIPELINE_C_SVM,
+]
+
+for _p in _REQUIRED:
+    if not Path(_p).exists():
+        pytest.skip(f"Model file not found: {_p}", allow_module_level=True)
+
+# heavy imports only after the skip guard
+from fastapi.testclient import TestClient    # noqa: E402
+from app.main import app                     # noqa: E402
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+@pytest.fixture(scope="module")
+def client():
+    with TestClient(app) as c:
+        yield c
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+ZEROS_63 = [0.0] * 63
+
+
+class TestHealthEndpoint:
+    def test_health_ok(self, client):
+        r = client.get("/health")
+        assert r.status_code == 200
+        body = r.json()
+        assert body["status"] == "ok"
+        assert body["models_loaded"] is True
+        assert set(body["pipelines_available"]) >= {"A", "B", "C"}
+
+
+class TestRestPredict:
+    def test_predict_zeros(self, client):
+        r = client.post(
+            "/api/predict",
+            json={"landmarks": ZEROS_63, "session_id": "test"},
+        )
+        assert r.status_code == 200
+        body = r.json()
+        assert "sign"        in body
+        assert "confidence"  in body
+        assert "pipeline"    in body
+        assert "label_index" in body
+        assert 0.0 <= body["confidence"] <= 1.0
+        assert 0   <= body["label_index"] <= 33
+
+    def test_predict_wrong_length(self, client):
+        r = client.post(
+            "/api/predict",
+            json={"landmarks": [0.0] * 62, "session_id": "bad"},
+        )
+        assert r.status_code == 422   # FastAPI validation error
+
+    def test_predict_random(self, client):
+        landmarks = [float(i % 11) / 10.0 for i in range(63)]
+        r = client.post(
+            "/api/predict",
+            json={"landmarks": landmarks, "session_id": "rand"},
+        )
+        assert r.status_code == 200
+
+
+class TestWebSocketLandmarks:
+    def test_ws_single_message(self, client):
+        with client.websocket_connect("/ws/landmarks") as ws:
+            ws.send_json({"landmarks": ZEROS_63, "session_id": "ws-test"})
+            data = ws.receive_json()
+            assert "sign"       in data
+            assert "confidence" in data
+            assert "pipeline"   in data
+            assert 0.0 <= data["confidence"] <= 1.0
+
+    def test_ws_multiple_messages(self, client):
+        with client.websocket_connect("/ws/landmarks") as ws:
+            for _ in range(3):
+                ws.send_json({"landmarks": ZEROS_63, "session_id": "ws-multi"})
+                data = ws.receive_json()
+                assert "sign" in data
+
+    def test_ws_invalid_message_returns_error(self, client):
+        """Sending malformed JSON should return an error frame, not crash."""
+        with client.websocket_connect("/ws/landmarks") as ws:
+            ws.send_text("not-json")
+            data = ws.receive_json()
+            assert "error" in data or data.get("status") == "error"
diff --git a/fly.toml b/fly.toml
new file mode 100644
index 0000000000000000000000000000000000000000..4c9e9a2ba3606ce05224c1928e460b3a88d7d9a6
--- /dev/null
+++ b/fly.toml
@@ -0,0 +1,42 @@
+# fly.toml — Fly.io deployment for SanketSetu backend
+# Deploy: flyctl deploy --dockerfile Dockerfile (from repo root)
+
+app = "sanketsetu-backend"
+primary_region = "maa"   # Mumbai — closest to India
+
+[build]
+  dockerfile = "Dockerfile"
+
+[http_service]
+  internal_port       = 8000
+  force_https         = true
+  auto_stop_machines  = "stop"
+  auto_start_machines = true
+  min_machines_running = 0
+  processes           = ["app"]
+
+  [http_service.concurrency]
+    type       = "requests"
+    soft_limit = 10
+    hard_limit = 25
+
+[vm]
+  size   = "shared-cpu-2x"   # 2 shared vCPUs, 512 MB RAM
+  memory = "1gb"
+
+[[vm.mounts]]
+  # Optional: mount model weights externally to keep image small
+  # source      = "sanketsetu_models"
+  # destination = "/models"
+
+[env]
+  KERAS_BACKEND       = "tensorflow"
+  TF_CPP_MIN_LOG_LEVEL = "3"
+  CUDA_VISIBLE_DEVICES = ""
+  TF_ENABLE_ONEDNN_OPTS = "0"
+  OMP_NUM_THREADS     = "4"
+  PIPELINE_MODE       = "ensemble"
+  CORS_ORIGINS        = "https://sanketsetu.vercel.app,http://localhost:5173"
+
+# Secrets to set via flyctl:
+#   flyctl secrets set CONFIDENCE_THRESHOLD=0.70
diff --git a/frontend/.gitignore b/frontend/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a547bf36d8d11a4f89c59c144f24795749086dd1
--- /dev/null
+++ b/frontend/.gitignore
@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
diff --git a/frontend/README.md b/frontend/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d2e77611fd3d959fee0487c41bd27b318be32b04
--- /dev/null
+++ b/frontend/README.md
@@ -0,0 +1,73 @@
+# React + TypeScript + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
+
+## React Compiler
+
+The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
+
+## Expanding the ESLint configuration
+
+If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules:
+
+```js
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{ts,tsx}'],
+    extends: [
+      // Other configs...
+
+      // Remove tseslint.configs.recommended and replace with this
+      tseslint.configs.recommendedTypeChecked,
+      // Alternatively, use this for stricter rules
+      tseslint.configs.strictTypeChecked,
+      // Optionally, add this for stylistic rules
+      tseslint.configs.stylisticTypeChecked,
+
+      // Other configs...
+    ],
+    languageOptions: {
+      parserOptions: {
+        project: ['./tsconfig.node.json', './tsconfig.app.json'],
+        tsconfigRootDir: import.meta.dirname,
+      },
+      // other options...
+    },
+  },
+])
+```
+
+You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules:
+
+```js
+// eslint.config.js
+import reactX from 'eslint-plugin-react-x'
+import reactDom from 'eslint-plugin-react-dom'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{ts,tsx}'],
+    extends: [
+      // Other configs...
+      // Enable lint rules for React
+      reactX.configs['recommended-typescript'],
+      // Enable lint rules for React DOM
+      reactDom.configs.recommended,
+    ],
+    languageOptions: {
+      parserOptions: {
+        project: ['./tsconfig.node.json', './tsconfig.app.json'],
+        tsconfigRootDir: import.meta.dirname,
+      },
+      // other options...
+    },
+  },
+])
+```
diff --git a/frontend/eslint.config.js b/frontend/eslint.config.js
new file mode 100644
index 0000000000000000000000000000000000000000..5e6b472f583e34a1cca751440d4f241495475723
--- /dev/null
+++ b/frontend/eslint.config.js
@@ -0,0 +1,23 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import tseslint from 'typescript-eslint'
+import { defineConfig, globalIgnores } from 'eslint/config'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{ts,tsx}'],
+    extends: [
+      js.configs.recommended,
+      tseslint.configs.recommended,
+      reactHooks.configs.flat.recommended,
+      reactRefresh.configs.vite,
+    ],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+    },
+  },
+])
diff --git a/frontend/index.html b/frontend/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..14ecd6a286b81323522316409bc280ebfb1a20c6
--- /dev/null
+++ b/frontend/index.html
@@ -0,0 +1,16 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <link rel="preconnect" href="https://fonts.googleapis.com" />
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap" rel="stylesheet" />
+    <title>SanketSetu | સંકેત-સેતુ</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
+</html>
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
new file mode 100644
index 0000000000000000000000000000000000000000..fccd9712b660835bd80630c1a587493f0a2e81c8
--- /dev/null
+++ b/frontend/package-lock.json
@@ -0,0 +1,3914 @@
+{
+  "name": "frontend",
+  "version": "0.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "frontend",
+      "version": "0.0.0",
+      "dependencies": {
+        "@mediapipe/tasks-vision": "^0.10.32",
+        "@tailwindcss/vite": "^4.2.1",
+        "framer-motion": "^12.34.3",
+        "lucide-react": "^0.576.0",
+        "react": "^19.2.0",
+        "react-dom": "^19.2.0",
+        "tailwindcss": "^4.2.1"
+      },
+      "devDependencies": {
+        "@eslint/js": "^9.39.1",
+        "@types/node": "^24.10.1",
+        "@types/react": "^19.2.7",
+        "@types/react-dom": "^19.2.3",
+        "@vitejs/plugin-react": "^5.1.1",
+        "eslint": "^9.39.1",
+        "eslint-plugin-react-hooks": "^7.0.1",
+        "eslint-plugin-react-refresh": "^0.4.24",
+        "globals": "^16.5.0",
+        "typescript": "~5.8.3",
+        "typescript-eslint": "^8.26.0",
+        "vite": "^6.3.5"
+      }
+    },
+    "node_modules/@babel/code-frame": {
+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.0.tgz",
+      "integrity": "sha512-9NhCeYjq9+3uxgdtp20LSiJXJvN0FeCtNGpJxuMFZ1Kv3cWUNb6DOhJwUvcVCzKGR66cw4njwM6hrJLqgOwbcw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-validator-identifier": "^7.28.5",
+        "js-tokens": "^4.0.0",
+        "picocolors": "^1.1.1"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/compat-data": {
+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.29.0.tgz",
+      "integrity": "sha512-T1NCJqT/j9+cn8fvkt7jtwbLBfLC/1y1c7NtCeXFRgzGTsafi68MRv8yzkYSapBnFA6L3U2VSc02ciDzoAJhJg==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/core": {
+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.29.0.tgz",
+      "integrity": "sha512-CGOfOJqWjg2qW/Mb6zNsDm+u5vFQ8DxXfbM09z69p5Z6+mE1ikP2jUXw+j42Pf1XTYED2Rni5f95npYeuwMDQA==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/code-frame": "^7.29.0",
+        "@babel/generator": "^7.29.0",
+        "@babel/helper-compilation-targets": "^7.28.6",
+        "@babel/helper-module-transforms": "^7.28.6",
+        "@babel/helpers": "^7.28.6",
+        "@babel/parser": "^7.29.0",
+        "@babel/template": "^7.28.6",
+        "@babel/traverse": "^7.29.0",
+        "@babel/types": "^7.29.0",
+        "@jridgewell/remapping": "^2.3.5",
+        "convert-source-map": "^2.0.0",
+        "debug": "^4.1.0",
+        "gensync": "^1.0.0-beta.2",
+        "json5": "^2.2.3",
+        "semver": "^6.3.1"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/babel"
+      }
+    },
+    "node_modules/@babel/generator": {
+      "version": "7.29.1",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.29.1.tgz",
+      "integrity": "sha512-qsaF+9Qcm2Qv8SRIMMscAvG4O3lJ0F1GuMo5HR/Bp02LopNgnZBC/EkbevHFeGs4ls/oPz9v+Bsmzbkbe+0dUw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/parser": "^7.29.0",
+        "@babel/types": "^7.29.0",
+        "@jridgewell/gen-mapping": "^0.3.12",
+        "@jridgewell/trace-mapping": "^0.3.28",
+        "jsesc": "^3.0.2"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-compilation-targets": {
+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.28.6.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "@babel/compat-data": "^7.28.6",
+        "@babel/helper-validator-option": "^7.27.1",
+        "browserslist": "^4.24.0",
+        "lru-cache": "^5.1.1",
+        "semver": "^6.3.1"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-globals": {
+      "version": "7.28.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-globals/-/helper-globals-7.28.0.tgz",
+      "integrity": "sha512-+W6cISkXFa1jXsDEdYA8HeevQT/FULhxzR99pxphltZcVaugps53THCeiWA8SguxxpSp3gKPiuYfSWopkLQ4hw==",
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+      "license": "MIT",
+      "engines": {
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+      }
+    },
+    "node_modules/@babel/helper-module-imports": {
+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.28.6.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/traverse": "^7.28.6",
+        "@babel/types": "^7.28.6"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-module-transforms": {
+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.28.6.tgz",
+      "integrity": "sha512-67oXFAYr2cDLDVGLXTEABjdBJZ6drElUSI7WKp70NrpyISso3plG9SAGEF6y7zbha/wOzUByWWTJvEDVNIUGcA==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-module-imports": "^7.28.6",
+        "@babel/helper-validator-identifier": "^7.28.5",
+        "@babel/traverse": "^7.28.6"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "peerDependencies": {
+        "@babel/core": "^7.0.0"
+      }
+    },
+    "node_modules/@babel/helper-plugin-utils": {
+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.28.6.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-string-parser": {
+      "version": "7.27.1",
+      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.27.1.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-validator-identifier": {
+      "version": "7.28.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.28.5.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-validator-option": {
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diff --git a/frontend/package.json b/frontend/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..1f96a1f1bac3de74e937a3b6ca6be246919dafa1
--- /dev/null
+++ b/frontend/package.json
@@ -0,0 +1,35 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "tsc -b && vite build",
+    "lint": "eslint .",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "@mediapipe/tasks-vision": "^0.10.32",
+    "@tailwindcss/vite": "^4.2.1",
+    "framer-motion": "^12.34.3",
+    "lucide-react": "^0.576.0",
+    "react": "^19.2.0",
+    "react-dom": "^19.2.0",
+    "tailwindcss": "^4.2.1"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.39.1",
+    "@types/node": "^24.10.1",
+    "@types/react": "^19.2.7",
+    "@types/react-dom": "^19.2.3",
+    "@vitejs/plugin-react": "^5.1.1",
+    "eslint": "^9.39.1",
+    "eslint-plugin-react-hooks": "^7.0.1",
+    "eslint-plugin-react-refresh": "^0.4.24",
+    "globals": "^16.5.0",
+    "typescript": "~5.8.3",
+    "typescript-eslint": "^8.26.0",
+    "vite": "^6.3.5"
+  }
+}
diff --git a/frontend/public/vite.svg b/frontend/public/vite.svg
new file mode 100644
index 0000000000000000000000000000000000000000..e7b8dfb1b2a60bd50538bec9f876511b9cac21e3
--- /dev/null
+++ b/frontend/public/vite.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
\ No newline at end of file
diff --git a/frontend/src/App.css b/frontend/src/App.css
new file mode 100644
index 0000000000000000000000000000000000000000..b9d355df2a5956b526c004531b7b0ffe412461e0
--- /dev/null
+++ b/frontend/src/App.css
@@ -0,0 +1,42 @@
+#root {
+  max-width: 1280px;
+  margin: 0 auto;
+  padding: 2rem;
+  text-align: center;
+}
+
+.logo {
+  height: 6em;
+  padding: 1.5em;
+  will-change: filter;
+  transition: filter 300ms;
+}
+.logo:hover {
+  filter: drop-shadow(0 0 2em #646cffaa);
+}
+.logo.react:hover {
+  filter: drop-shadow(0 0 2em #61dafbaa);
+}
+
+@keyframes logo-spin {
+  from {
+    transform: rotate(0deg);
+  }
+  to {
+    transform: rotate(360deg);
+  }
+}
+
+@media (prefers-reduced-motion: no-preference) {
+  a:nth-of-type(2) .logo {
+    animation: logo-spin infinite 20s linear;
+  }
+}
+
+.card {
+  padding: 2em;
+}
+
+.read-the-docs {
+  color: #888;
+}
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..e711b30580c1b02a605bdc713b52fb0c711df52a
--- /dev/null
+++ b/frontend/src/App.tsx
@@ -0,0 +1,177 @@
+import { useEffect, useRef, useState } from 'react'
+import { motion, AnimatePresence } from 'framer-motion'
+import { Settings, Hand } from 'lucide-react'
+
+import { useWebcam }    from './hooks/useWebcam'
+import { useMediaPipe } from './hooks/useMediaPipe'
+import { useWebSocket } from './hooks/useWebSocket'
+
+import { WebcamFeed }      from './components/WebcamFeed'
+import { PredictionHUD }   from './components/PredictionHUD'
+import { OnboardingGuide } from './components/OnboardingGuide'
+import { Calibration }     from './components/Calibration'
+
+type AppStage = 'onboarding' | 'calibration' | 'running'
+
+function App() {
+  // ── Stage management ─────────────────────────────────────────
+  const showOnboarding = !localStorage.getItem('sanketsetu-onboarded')
+  const [stage, setStage] = useState<AppStage>(showOnboarding ? 'onboarding' : 'calibration')
+
+  const handleOnboardingDone = () => {
+    localStorage.setItem('sanketsetu-onboarded', '1')
+    setStage('calibration')
+  }
+
+  // ── Webcam ───────────────────────────────────────────────────
+  const { videoRef, isReady, error, facingMode, switchCamera } = useWebcam()
+
+  // ── MediaPipe ────────────────────────────────────────────────
+  const { landmarks, rawLandmarks, isLoading: mpLoading, error: mpError, startDetection, stopDetection } =
+    useMediaPipe()
+
+  useEffect(() => {
+    if (isReady && videoRef.current && stage === 'running') {
+      startDetection(videoRef.current)
+    } else if (stage !== 'running') {
+      stopDetection()
+    }
+  }, [isReady, stage, startDetection, stopDetection, videoRef])
+
+  // Start detecting during calibration too (to detect hand)
+  useEffect(() => {
+    if (isReady && videoRef.current && stage === 'calibration') {
+      startDetection(videoRef.current)
+    }
+  }, [isReady, stage, startDetection, videoRef])
+
+  // ── WebSocket ────────────────────────────────────────────────
+  const { lastPrediction, isConnected, latency, lowBandwidth, sendLandmarks } = useWebSocket()
+
+  // Send landmarks on every new frame
+  useEffect(() => {
+    if (stage === 'running' && landmarks) {
+      sendLandmarks(landmarks)
+    }
+  }, [landmarks, stage, sendLandmarks])
+
+  // Was the last prediction recently (within 1.5s)?
+  const lastPredTs = useRef(0)
+  const [recognised, setRecognised] = useState(false)
+  useEffect(() => {
+    if (lastPrediction) {
+      lastPredTs.current = Date.now()
+      setRecognised(true)
+      setTimeout(() => setRecognised(false), 800)
+    }
+  }, [lastPrediction])
+
+  return (
+    <div className="bg-animated min-h-screen flex flex-col">
+      {/* ── Onboarding overlay ─────────────────────────────────── */}
+      <AnimatePresence>
+        {stage === 'onboarding' && (
+          <OnboardingGuide onComplete={handleOnboardingDone} />
+        )}
+      </AnimatePresence>
+
+      {/* ── Calibration overlay ────────────────────────────────── */}
+      <AnimatePresence>
+        {stage === 'calibration' && (
+          <Calibration
+            handDetected={!!rawLandmarks}
+            onReady={() => setStage('running')}
+          />
+        )}
+      </AnimatePresence>
+
+      {/* ── Reconnecting banner ───────────────────────────────── */}
+      <AnimatePresence>
+        {!isConnected && stage === 'running' && (
+          <motion.div
+            key="reconnect-banner"
+            initial={{ opacity: 0, y: -20 }}
+            animate={{ opacity: 1, y: 0 }}
+            exit={{ opacity: 0, y: -20 }}
+            className="fixed top-0 left-0 right-0 z-50 text-center py-2 text-sm font-medium"
+            style={{ background: 'rgba(251,113,133,0.9)', color: '#fff' }}
+          >
+            Reconnecting to server…
+          </motion.div>
+        )}
+      </AnimatePresence>
+
+      {/* ── Low-bandwidth banner ─────────────────────────────────── */}
+      <AnimatePresence>
+        {lowBandwidth && isConnected && (
+          <motion.div
+            key="lowbw-banner"
+            initial={{ opacity: 0, y: -20 }}
+            animate={{ opacity: 1, y: 0 }}
+            exit={{ opacity: 0, y: -20 }}
+            className="fixed top-0 left-0 right-0 z-50 text-center py-2 text-sm font-medium"
+            style={{ background: 'rgba(234,179,8,0.9)', color: '#000' }}
+          >
+            High latency detected — reduced to 5 fps to conserve bandwidth
+          </motion.div>
+        )}
+      </AnimatePresence>
+
+      {/* ── Header ─────────────────────────────────────────────── */}
+      <header className="flex items-center justify-between px-6 py-4">
+        <div className="flex items-center gap-3">
+          <Hand size={22} style={{ color: '#00f5d4' }} />
+          <h1 className="text-xl font-bold tracking-wide" style={{ color: '#e2e8f0' }}>
+            Sanket<span className="glow-text">Setu</span>
+            <span className="ml-2 text-base font-normal text-slate-500">| સંકેત-સેતુ</span>
+          </h1>
+        </div>
+        <div className="flex items-center gap-3 text-slate-500 text-xs">
+          {mpLoading && <span>Loading AI…</span>}
+          {mpError   && <span className="text-rose-400">{mpError}</span>}
+          <Settings size={18} className="cursor-pointer hover:text-slate-300 transition-colors" />
+        </div>
+      </header>
+
+      {/* ── Main content ───────────────────────────────────────── */}
+      <main className="flex-1 flex flex-col lg:flex-row items-start justify-center gap-6 px-4 pb-8 lg:px-8">
+
+        {/* Webcam panel */}
+        <motion.div
+          initial={{ opacity: 0, y: 20 }}
+          animate={{ opacity: 1, y: 0 }}
+          transition={{ delay: 0.1 }}
+          className="w-full lg:flex-1 flex justify-center"
+        >
+          <WebcamFeed
+            videoRef={videoRef}
+            isReady={isReady}
+            error={error}
+            rawLandmarks={rawLandmarks}
+            recognised={recognised}
+            facingMode={facingMode}
+            onSwitchCamera={switchCamera}
+          />
+        </motion.div>
+
+        {/* HUD panel */}
+        <motion.div
+          initial={{ opacity: 0, x: 20 }}
+          animate={{ opacity: 1, x: 0 }}
+          transition={{ delay: 0.2 }}
+          className="w-full lg:w-auto flex justify-center lg:justify-start pt-0 lg:pt-2"
+        >
+          <PredictionHUD
+            prediction={lastPrediction}
+            isConnected={isConnected}
+            latency={latency}
+            lowBandwidth={lowBandwidth}
+          />
+        </motion.div>
+
+      </main>
+    </div>
+  )
+}
+
+export default App
diff --git a/frontend/src/assets/react.svg b/frontend/src/assets/react.svg
new file mode 100644
index 0000000000000000000000000000000000000000..6c87de9bb3358469122cc991d5cf578927246184
--- /dev/null
+++ b/frontend/src/assets/react.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="35.93" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 228"><path fill="#00D8FF" d="M210.483 73.824a171.49 171.49 0 0 0-8.24-2.597c.465-1.9.893-3.777 1.273-5.621c6.238-30.281 2.16-54.676-11.769-62.708c-13.355-7.7-35.196.329-57.254 19.526a171.23 171.23 0 0 0-6.375 5.848a155.866 155.866 0 0 0-4.241-3.917C100.759 3.829 77.587-4.822 63.673 3.233C50.33 10.957 46.379 33.89 51.995 62.588a170.974 170.974 0 0 0 1.892 8.48c-3.28.932-6.445 1.924-9.474 2.98C17.309 83.498 0 98.307 0 113.668c0 15.865 18.582 31.778 46.812 41.427a145.52 145.52 0 0 0 6.921 2.165a167.467 167.467 0 0 0-2.01 9.138c-5.354 28.2-1.173 50.591 12.134 58.266c13.744 7.926 36.812-.22 59.273-19.855a145.567 145.567 0 0 0 5.342-4.923a168.064 168.064 0 0 0 6.92 6.314c21.758 18.722 43.246 26.282 56.54 18.586c13.731-7.949 18.194-32.003 12.4-61.268a145.016 145.016 0 0 0-1.535-6.842c1.62-.48 3.21-.974 4.76-1.488c29.348-9.723 48.443-25.443 48.443-41.52c0-15.417-17.868-30.326-45.517-39.844Zm-6.365 70.984c-1.4.463-2.836.91-4.3 1.345c-3.24-10.257-7.612-21.163-12.963-32.432c5.106-11 9.31-21.767 12.459-31.957c2.619.758 5.16 1.557 7.61 2.4c23.69 8.156 38.14 20.213 38.14 29.504c0 9.896-15.606 22.743-40.946 31.14Zm-10.514 20.834c2.562 12.94 2.927 24.64 1.23 33.787c-1.524 8.219-4.59 13.698-8.382 15.893c-8.067 4.67-25.32-1.4-43.927-17.412a156.726 156.726 0 0 1-6.437-5.87c7.214-7.889 14.423-17.06 21.459-27.246c12.376-1.098 24.068-2.894 34.671-5.345a134.17 134.17 0 0 1 1.386 6.193ZM87.276 214.515c-7.882 2.783-14.16 2.863-17.955.675c-8.075-4.657-11.432-22.636-6.853-46.752a156.923 156.923 0 0 1 1.869-8.499c10.486 2.32 22.093 3.988 34.498 4.994c7.084 9.967 14.501 19.128 21.976 27.15a134.668 134.668 0 0 1-4.877 4.492c-9.933 8.682-19.886 14.842-28.658 17.94ZM50.35 144.747c-12.483-4.267-22.792-9.812-29.858-15.863c-6.35-5.437-9.555-10.836-9.555-15.216c0-9.322 13.897-21.212 37.076-29.293c2.813-.98 5.757-1.905 8.812-2.773c3.204 10.42 7.406 21.315 12.477 32.332c-5.137 11.18-9.399 22.249-12.634 32.792a134.718 134.718 0 0 1-6.318-1.979Zm12.378-84.26c-4.811-24.587-1.616-43.134 6.425-47.789c8.564-4.958 27.502 2.111 47.463 19.835a144.318 144.318 0 0 1 3.841 3.545c-7.438 7.987-14.787 17.08-21.808 26.988c-12.04 1.116-23.565 2.908-34.161 5.309a160.342 160.342 0 0 1-1.76-7.887Zm110.427 27.268a347.8 347.8 0 0 0-7.785-12.803c8.168 1.033 15.994 2.404 23.343 4.08c-2.206 7.072-4.956 14.465-8.193 22.045a381.151 381.151 0 0 0-7.365-13.322Zm-45.032-43.861c5.044 5.465 10.096 11.566 15.065 18.186a322.04 322.04 0 0 0-30.257-.006c4.974-6.559 10.069-12.652 15.192-18.18ZM82.802 87.83a323.167 323.167 0 0 0-7.227 13.238c-3.184-7.553-5.909-14.98-8.134-22.152c7.304-1.634 15.093-2.97 23.209-3.984a321.524 321.524 0 0 0-7.848 12.897Zm8.081 65.352c-8.385-.936-16.291-2.203-23.593-3.793c2.26-7.3 5.045-14.885 8.298-22.6a321.187 321.187 0 0 0 7.257 13.246c2.594 4.48 5.28 8.868 8.038 13.147Zm37.542 31.03c-5.184-5.592-10.354-11.779-15.403-18.433c4.902.192 9.899.29 14.978.29c5.218 0 10.376-.117 15.453-.343c-4.985 6.774-10.018 12.97-15.028 18.486Zm52.198-57.817c3.422 7.8 6.306 15.345 8.596 22.52c-7.422 1.694-15.436 3.058-23.88 4.071a382.417 382.417 0 0 0 7.859-13.026a347.403 347.403 0 0 0 7.425-13.565Zm-16.898 8.101a358.557 358.557 0 0 1-12.281 19.815a329.4 329.4 0 0 1-23.444.823c-7.967 0-15.716-.248-23.178-.732a310.202 310.202 0 0 1-12.513-19.846h.001a307.41 307.41 0 0 1-10.923-20.627a310.278 310.278 0 0 1 10.89-20.637l-.001.001a307.318 307.318 0 0 1 12.413-19.761c7.613-.576 15.42-.876 23.31-.876H128c7.926 0 15.743.303 23.354.883a329.357 329.357 0 0 1 12.335 19.695a358.489 358.489 0 0 1 11.036 20.54a329.472 329.472 0 0 1-11 20.722Zm22.56-122.124c8.572 4.944 11.906 24.881 6.52 51.026c-.344 1.668-.73 3.367-1.15 5.09c-10.622-2.452-22.155-4.275-34.23-5.408c-7.034-10.017-14.323-19.124-21.64-27.008a160.789 160.789 0 0 1 5.888-5.4c18.9-16.447 36.564-22.941 44.612-18.3ZM128 90.808c12.625 0 22.86 10.235 22.86 22.86s-10.235 22.86-22.86 22.86s-22.86-10.235-22.86-22.86s10.235-22.86 22.86-22.86Z"></path></svg>
\ No newline at end of file
diff --git a/frontend/src/components/Calibration.tsx b/frontend/src/components/Calibration.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..00877a3e44b19c08e87808f556d5316cc7eef8d9
--- /dev/null
+++ b/frontend/src/components/Calibration.tsx
@@ -0,0 +1,105 @@
+import { useEffect, useState } from 'react';
+import { motion, AnimatePresence } from 'framer-motion';
+import { CheckCircle, Loader2 } from 'lucide-react';
+
+interface Props {
+  handDetected: boolean;
+  onReady: () => void;
+}
+
+const STABLE_DURATION_MS = 1000; // hand must be visible for this long
+
+/**
+ * Brief calibration screen shown after onboarding.
+ * Waits until a hand is stable in frame, then auto-transitions.
+ */
+export function Calibration({ handDetected, onReady }: Props) {
+  const [stableMs, setStableMs] = useState(0);
+  const [done, setDone]         = useState(false);
+
+  useEffect(() => {
+    if (!handDetected) {
+      setStableMs(0);
+      return;
+    }
+    const interval = setInterval(() => {
+      setStableMs(prev => {
+        const next = prev + 100;
+        if (next >= STABLE_DURATION_MS && !done) {
+          setDone(true);
+          setTimeout(onReady, 600); // brief pause before transitioning
+        }
+        return next;
+      });
+    }, 100);
+    return () => clearInterval(interval);
+  }, [handDetected, done, onReady]);
+
+  const progress  = Math.min((stableMs / STABLE_DURATION_MS) * 100, 100);
+  const isChecked = done;
+
+  return (
+    <div
+      className="fixed inset-0 z-40 flex flex-col items-center justify-center gap-8"
+      style={{ background: 'rgba(5,8,22,0.92)', backdropFilter: 'blur(12px)' }}
+    >
+      <motion.h1
+        initial={{ opacity: 0, y: -20 }}
+        animate={{ opacity: 1, y: 0 }}
+        className="text-3xl font-bold glow-text"
+      >
+        Ready?
+      </motion.h1>
+
+      <motion.p
+        initial={{ opacity: 0 }}
+        animate={{ opacity: 1 }}
+        transition={{ delay: 0.2 }}
+        className="text-slate-400 text-center max-w-xs"
+      >
+        {handDetected
+          ? 'Hand detected — hold steady…'
+          : 'Please show your hand to the camera.'}
+      </motion.p>
+
+      {/* Circular progress / check */}
+      <div className="relative w-24 h-24 flex items-center justify-center">
+        <svg className="absolute inset-0 w-full h-full -rotate-90" viewBox="0 0 96 96">
+          <circle cx="48" cy="48" r="40" fill="none" stroke="rgba(255,255,255,0.06)" strokeWidth="6" />
+          <motion.circle
+            cx="48" cy="48" r="40"
+            fill="none"
+            stroke="#00f5d4"
+            strokeWidth="6"
+            strokeLinecap="round"
+            strokeDasharray={`${2 * Math.PI * 40}`}
+            animate={{ strokeDashoffset: 2 * Math.PI * 40 * (1 - progress / 100) }}
+            transition={{ duration: 0.1 }}
+            style={{ filter: 'drop-shadow(0 0 6px #00f5d4)' }}
+          />
+        </svg>
+
+        <AnimatePresence mode="wait">
+          {isChecked ? (
+            <motion.div
+              key="check"
+              initial={{ scale: 0, opacity: 0 }}
+              animate={{ scale: 1, opacity: 1 }}
+              transition={{ type: 'spring', stiffness: 400, damping: 20 }}
+            >
+              <CheckCircle size={40} style={{ color: '#00f5d4' }} />
+            </motion.div>
+          ) : (
+            <motion.div key="spinner" animate={{ rotate: 360 }} transition={{ duration: 1.5, repeat: Infinity, ease: 'linear' }}>
+              <Loader2 size={32} strokeWidth={1.5} style={{ color: handDetected ? '#00f5d4' : '#4b5563' }} />
+            </motion.div>
+          )}
+        </AnimatePresence>
+      </div>
+
+      <p className="text-xs text-slate-600">
+        {isChecked ? 'All set!' : 'Keep your hand visible for 1 second'}
+      </p>
+    </div>
+  );
+}
diff --git a/frontend/src/components/LandmarkCanvas.tsx b/frontend/src/components/LandmarkCanvas.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..4547bcb248b116da2d179870475474d4e0f7e1f7
--- /dev/null
+++ b/frontend/src/components/LandmarkCanvas.tsx
@@ -0,0 +1,97 @@
+import { useEffect, useRef } from 'react';
+import type { NormalizedLandmark } from '@mediapipe/tasks-vision';
+import { HAND_CONNECTIONS } from '../lib/landmarkUtils';
+
+interface Props {
+  rawLandmarks: NormalizedLandmark[] | null;
+  videoWidth: number;
+  videoHeight: number;
+  /** Pulse/glow when a sign was just recognised */
+  recognised: boolean;
+}
+
+const DOT_RADIUS     = 5;
+const NEON_CYAN      = '#00f5d4';
+const NEON_PURPLE    = '#9b5de5';
+const LINE_COLOR     = 'rgba(0, 245, 212, 0.55)';
+const GLOW_COLOR_ON  = '#00f5d4';
+const GLOW_COLOR_OFF = 'rgba(0,245,212,0.6)';
+
+/**
+ * Canvas overlay that draws MediaPipe hand landmarks on top of the webcam feed.
+ * The canvas is absolutely positioned and sized to match the video element.
+ */
+export function LandmarkCanvas({ rawLandmarks, videoWidth, videoHeight, recognised }: Props) {
+  const canvasRef = useRef<HTMLCanvasElement>(null);
+
+  useEffect(() => {
+    const canvas = canvasRef.current;
+    if (!canvas) return;
+    const ctx = canvas.getContext('2d');
+    if (!ctx) return;
+
+    ctx.clearRect(0, 0, canvas.width, canvas.height);
+    if (!rawLandmarks || rawLandmarks.length !== 21) return;
+
+    const w = canvas.width;
+    const h = canvas.height;
+
+    const px = (lm: NormalizedLandmark) => lm.x * w;
+    const py = (lm: NormalizedLandmark) => lm.y * h;
+
+    // ── Bones ────────────────────────────────────────────────────
+    ctx.lineWidth = 2;
+    ctx.strokeStyle = LINE_COLOR;
+    ctx.shadowBlur  = recognised ? 12 : 6;
+    ctx.shadowColor = LINE_COLOR;
+
+    for (const [a, b] of HAND_CONNECTIONS) {
+      ctx.beginPath();
+      ctx.moveTo(px(rawLandmarks[a]), py(rawLandmarks[a]));
+      ctx.lineTo(px(rawLandmarks[b]), py(rawLandmarks[b]));
+      ctx.stroke();
+    }
+
+    // ── Landmark dots ────────────────────────────────────────────
+    const glowColor  = recognised ? GLOW_COLOR_ON : GLOW_COLOR_OFF;
+    const glowRadius = recognised ? 20 : 10;
+
+    for (let i = 0; i < rawLandmarks.length; i++) {
+      const lm = rawLandmarks[i];
+      const x  = px(lm);
+      const y  = py(lm);
+
+      // Outer glow ring
+      ctx.beginPath();
+      ctx.arc(x, y, DOT_RADIUS + 2, 0, Math.PI * 2);
+      ctx.fillStyle   = 'transparent';
+      ctx.shadowBlur  = glowRadius;
+      ctx.shadowColor = glowColor;
+      ctx.strokeStyle = glowColor;
+      ctx.lineWidth   = 1.5;
+      ctx.stroke();
+
+      // Filled dot (wrist = larger)
+      ctx.beginPath();
+      ctx.arc(x, y, i === 0 ? DOT_RADIUS + 2 : DOT_RADIUS, 0, Math.PI * 2);
+      ctx.fillStyle   = i === 0 ? NEON_PURPLE : NEON_CYAN;
+      ctx.shadowBlur  = recognised ? 16 : 8;
+      ctx.shadowColor = i === 0 ? NEON_PURPLE : NEON_CYAN;
+      ctx.fill();
+    }
+
+    // Reset shadow so it doesn't bleed onto future draws
+    ctx.shadowBlur  = 0;
+    ctx.shadowColor = 'transparent';
+  }, [rawLandmarks, recognised]);
+
+  return (
+    <canvas
+      ref={canvasRef}
+      width={videoWidth}
+      height={videoHeight}
+      className="absolute inset-0 w-full h-full pointer-events-none"
+      style={{ mixBlendMode: 'screen' }}
+    />
+  );
+}
diff --git a/frontend/src/components/OnboardingGuide.tsx b/frontend/src/components/OnboardingGuide.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..d38a2b440a04f6789d492fe2cb926d1b6bb21937
--- /dev/null
+++ b/frontend/src/components/OnboardingGuide.tsx
@@ -0,0 +1,143 @@
+import { useState } from 'react';
+import { motion, AnimatePresence } from 'framer-motion';
+import { Hand, Sun, Eye, ArrowRight, X } from 'lucide-react';
+
+interface Props {
+  onComplete: () => void;
+}
+
+const STEPS = [
+  {
+    icon: Hand,
+    title: 'Position Your Hand',
+    body: 'Hold your hand 30–60 cm from the camera with the palm facing forward. Make sure all five fingers are clearly visible.',
+    color: '#00f5d4',
+  },
+  {
+    icon: Sun,
+    title: 'Check Your Lighting',
+    body: 'Ensure your hand is well-lit from the front. Avoid strong back-lighting or dark backgrounds for best accuracy.',
+    color: '#fee440',
+  },
+  {
+    icon: Eye,
+    title: 'Sign Clearly',
+    body: 'Form each Gujarati sign deliberately. The system detects one hand at a time. Signs appear instantly in the HUD.',
+    color: '#9b5de5',
+  },
+] as const;
+
+const variants = {
+  enter: (dir: number) => ({ x: dir > 0 ? 80 : -80, opacity: 0 }),
+  center: { x: 0, opacity: 1 },
+  exit:   (dir: number) => ({ x: dir > 0 ? -80 : 80, opacity: 0 }),
+};
+
+/**
+ * 3-step animated onboarding wizard shown on first visit.
+ */
+export function OnboardingGuide({ onComplete }: Props) {
+  const [step, setStep] = useState(0);
+  const [dir,  setDir]  = useState(1);
+
+  const go = (next: number) => {
+    setDir(next > step ? 1 : -1);
+    setStep(next);
+  };
+
+  const { icon: Icon, title, body, color } = STEPS[step];
+
+  return (
+    <div className="fixed inset-0 z-50 flex items-center justify-center"
+         style={{ background: 'rgba(5,8,22,0.85)', backdropFilter: 'blur(8px)' }}>
+      <div className="glass glow-border p-8 max-w-sm w-full mx-4 relative">
+
+        {/* Skip / close */}
+        <button
+          onClick={onComplete}
+          className="absolute top-4 right-4 text-slate-400 hover:text-white transition-colors"
+          aria-label="Skip"
+        >
+          <X size={18} />
+        </button>
+
+        {/* Step indicator */}
+        <div className="flex gap-2 mb-6">
+          {STEPS.map((_, i) => (
+            <div
+              key={i}
+              className="h-1 rounded-full flex-1 transition-all duration-300"
+              style={{ background: i <= step ? color : 'rgba(255,255,255,0.1)' }}
+            />
+          ))}
+        </div>
+
+        {/* Animated step content */}
+        <AnimatePresence custom={dir} mode="wait">
+          <motion.div
+            key={step}
+            custom={dir}
+            variants={variants}
+            initial="enter"
+            animate="center"
+            exit="exit"
+            transition={{ type: 'spring', stiffness: 300, damping: 30 }}
+            className="flex flex-col items-center text-center gap-4"
+          >
+            <div
+              className="w-20 h-20 rounded-full flex items-center justify-center"
+              style={{
+                background: `${color}15`,
+                border: `2px solid ${color}40`,
+                boxShadow: `0 0 20px ${color}30`,
+              }}
+            >
+              <Icon size={36} style={{ color }} />
+            </div>
+            <h2 className="text-xl font-bold text-white">{title}</h2>
+            <p className="text-slate-300 text-sm leading-relaxed">{body}</p>
+          </motion.div>
+        </AnimatePresence>
+
+        {/* Navigation */}
+        <div className="flex items-center justify-between mt-8">
+          {step > 0 ? (
+            <button
+              onClick={() => go(step - 1)}
+              className="text-sm text-slate-400 hover:text-white transition-colors px-3 py-1.5"
+            >
+              Back
+            </button>
+          ) : <div />}
+
+          {step < STEPS.length - 1 ? (
+            <button
+              onClick={() => go(step + 1)}
+              className="flex items-center gap-2 px-5 py-2.5 rounded-lg text-sm font-semibold transition-all"
+              style={{
+                background: `${color}20`,
+                border: `1px solid ${color}50`,
+                color,
+              }}
+            >
+              Next <ArrowRight size={14} />
+            </button>
+          ) : (
+            <button
+              onClick={onComplete}
+              className="flex items-center gap-2 px-5 py-2.5 rounded-lg text-sm font-semibold"
+              style={{
+                background: 'rgba(0,245,212,0.15)',
+                border: '1px solid rgba(0,245,212,0.4)',
+                color: '#00f5d4',
+                boxShadow: '0 0 12px rgba(0,245,212,0.2)',
+              }}
+            >
+              Start <ArrowRight size={14} />
+            </button>
+          )}
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/components/PredictionHUD.tsx b/frontend/src/components/PredictionHUD.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..40b0430b77992e8da69ec3f34807ab6a86f3dce1
--- /dev/null
+++ b/frontend/src/components/PredictionHUD.tsx
@@ -0,0 +1,181 @@
+import { useEffect, useRef, useState } from 'react';
+import { motion, AnimatePresence } from 'framer-motion';
+import { Cpu, Zap, Clock } from 'lucide-react';
+import type { PredictionResponse } from '../types';
+
+interface Props {
+  prediction: PredictionResponse | null;
+  isConnected: boolean;
+  latency: number;
+  lowBandwidth?: boolean;
+}
+
+const PIPELINE_COLORS: Record<string, string> = {
+  A: 'text-emerald-400',
+  B: 'text-sky-400',
+  C: 'text-purple-400',
+  ensemble: 'text-amber-400',
+};
+
+const PIPELINE_LABELS: Record<string, string> = {
+  A: 'XGBoost',
+  B: 'AE + LGBM',
+  C: 'CNN + SVM',
+};
+
+function confidenceColor(c: number) {
+  if (c >= 0.85) return '#00f5d4';    // neon-cyan
+  if (c >= 0.60) return '#fee440';    // yellow
+  return '#f15bb5';                   // pink-red
+}
+
+function ConfidenceBar({ value }: { value: number }) {
+  return (
+    <div className="w-full h-2 rounded-full" style={{ background: 'rgba(255,255,255,0.1)' }}>
+      <motion.div
+        className="h-2 rounded-full"
+        style={{ background: confidenceColor(value) }}
+        initial={{ width: 0 }}
+        animate={{ width: `${Math.round(value * 100)}%` }}
+        transition={{ type: 'spring', stiffness: 120, damping: 20 }}
+      />
+    </div>
+  );
+}
+
+/**
+ * Floating HUD panel that shows the current sign prediction, confidence,
+ * active pipeline, latency and a rolling history of the last 10 signs.
+ */
+export function PredictionHUD({ prediction, isConnected, latency, lowBandwidth = false }: Props) {
+  const [history, setHistory] = useState<PredictionResponse[]>([]);
+  const prevSignRef = useRef<string | null>(null);
+
+  // Add to history only when the sign changes
+  useEffect(() => {
+    if (!prediction) return;
+    if (prediction.sign === prevSignRef.current) return;
+    prevSignRef.current = prediction.sign;
+    setHistory(prev => [prediction, ...prev].slice(0, 10));
+  }, [prediction]);
+
+  const pipelineKey = prediction?.pipeline ?? 'A';
+  const pipelineColor = PIPELINE_COLORS[pipelineKey] ?? 'text-slate-400';
+  const pipelineLabel = PIPELINE_LABELS[pipelineKey] ?? pipelineKey;
+
+  return (
+    <div className="glass glow-border flex flex-col gap-4 p-5 min-w-[260px] max-w-xs w-full">
+      {/* Connection status */}
+      <div className="flex items-center justify-between text-xs text-slate-400">
+        <span className="flex items-center gap-1.5">
+          <span
+            className="inline-block w-2 h-2 rounded-full"
+            style={{ background: isConnected ? '#00f5d4' : '#f15bb5',
+                     boxShadow: isConnected ? '0 0 8px #00f5d4' : '0 0 8px #f15bb5' }}
+          />
+          {isConnected ? 'Connected' : 'Reconnecting…'}
+        </span>
+        <span className="flex items-center gap-1">
+          <Clock size={11} />
+          {latency > 0 ? `${latency}ms` : '—'}
+          {lowBandwidth && (
+            <span
+              title="Low-bandwidth mode: 5fps"
+              className="ml-1 text-yellow-400 font-semibold"
+              style={{ fontSize: '10px' }}
+            >
+              LB
+            </span>
+          )}
+        </span>
+      </div>
+
+      {/* Main sign display */}
+      <div className="flex flex-col items-center gap-1 py-2">
+        <AnimatePresence mode="popLayout">
+          {prediction ? (
+            <motion.div
+              key={prediction.sign}
+              initial={{ opacity: 0, scale: 0.6, y: 10 }}
+              animate={{ opacity: 1, scale: 1, y: 0 }}
+              exit={{ opacity: 0, scale: 0.4, y: -10 }}
+              transition={{ type: 'spring', stiffness: 300, damping: 22 }}
+              className="glow-text text-7xl font-bold select-none"
+              style={{ color: '#00f5d4' }}
+            >
+              {prediction.sign}
+            </motion.div>
+          ) : (
+            <motion.div
+              key="placeholder"
+              initial={{ opacity: 0 }}
+              animate={{ opacity: 0.3 }}
+              exit={{ opacity: 0 }}
+              className="text-5xl font-bold text-slate-500 select-none"
+            >
+              ?
+            </motion.div>
+          )}
+        </AnimatePresence>
+
+        {/* Confidence bar */}
+        {prediction && (
+          <div className="w-full px-2 mt-1">
+            <div className="flex justify-between text-xs text-slate-400 mb-1">
+              <span>Confidence</span>
+              <span style={{ color: confidenceColor(prediction.confidence) }}>
+                {Math.round(prediction.confidence * 100)}%
+              </span>
+            </div>
+            <ConfidenceBar value={prediction.confidence} />
+          </div>
+        )}
+      </div>
+
+      {/* Pipeline badge */}
+      {prediction && (
+        <div className="flex items-center gap-1.5 text-xs">
+          <Cpu size={12} className={pipelineColor} />
+          <span className={pipelineColor}>Pipeline {pipelineKey}</span>
+          <span className="text-slate-500">·</span>
+          <span className="text-slate-400">{pipelineLabel}</span>
+        </div>
+      )}
+
+      {/* Divider */}
+      <div className="border-t" style={{ borderColor: 'rgba(255,255,255,0.08)' }} />
+
+      {/* History */}
+      <div className="flex flex-col gap-1">
+        <p className="text-xs text-slate-500 mb-1 flex items-center gap-1">
+          <Zap size={11} /> Recent signs
+        </p>
+        <div className="flex flex-wrap gap-1.5">
+          <AnimatePresence>
+            {history.map((h, i) => (
+              <motion.span
+                key={`${h.sign}-${i}`}
+                initial={{ opacity: 0, scale: 0.5 }}
+                animate={{ opacity: 1 - i * 0.08, scale: 1 }}
+                exit={{ opacity: 0, scale: 0.3 }}
+                transition={{ duration: 0.2 }}
+                className="px-2 py-0.5 rounded-full text-sm font-semibold"
+                style={{
+                  background: 'rgba(0,245,212,0.08)',
+                  border: '1px solid rgba(0,245,212,0.2)',
+                  color: '#00f5d4',
+                  fontSize: i === 0 ? '1.1rem' : '0.85rem',
+                }}
+              >
+                {h.sign}
+              </motion.span>
+            ))}
+          </AnimatePresence>
+          {history.length === 0 && (
+            <span className="text-xs text-slate-600 italic">None yet</span>
+          )}
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/components/WebcamFeed.tsx b/frontend/src/components/WebcamFeed.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..0c873103d58d39fd0ef282adc212958e876ef60f
--- /dev/null
+++ b/frontend/src/components/WebcamFeed.tsx
@@ -0,0 +1,110 @@
+import { useRef } from 'react';
+import { CameraOff, RotateCcw } from 'lucide-react';
+import type { NormalizedLandmark } from '@mediapipe/tasks-vision';
+import { LandmarkCanvas } from './LandmarkCanvas';
+
+interface Props {
+  videoRef: React.RefObject<HTMLVideoElement | null>;
+  isReady: boolean;
+  error: string | null;
+  rawLandmarks: NormalizedLandmark[] | null;
+  recognised: boolean;
+  facingMode: 'user' | 'environment';
+  onSwitchCamera: () => void;
+}
+
+/**
+ * Webcam feed panel with the landmark canvas overlay and error states.
+ */
+export function WebcamFeed({
+  videoRef,
+  isReady,
+  error,
+  rawLandmarks,
+  recognised,
+  facingMode,
+  onSwitchCamera,
+}: Props) {
+  const containerRef = useRef<HTMLDivElement>(null);
+
+  if (error) {
+    return (
+      <div className="glass flex flex-col items-center justify-center gap-4 p-8 aspect-video w-full max-w-2xl text-center">
+        <CameraOff size={48} className="text-rose-400 opacity-70" />
+        <p className="text-rose-300 font-semibold">Camera Unavailable</p>
+        <p className="text-slate-400 text-sm max-w-xs">{error}</p>
+        <button
+          onClick={() => window.location.reload()}
+          className="flex items-center gap-2 px-4 py-2 rounded-lg text-sm"
+          style={{ background: 'rgba(244,63,94,0.15)', border: '1px solid rgba(244,63,94,0.3)', color: '#fb7185' }}
+        >
+          <RotateCcw size={14} /> Reload page
+        </button>
+      </div>
+    );
+  }
+
+  return (
+    <div
+      ref={containerRef}
+      className="relative rounded-2xl overflow-hidden w-full max-w-2xl"
+      style={{
+        aspectRatio: '16/9',
+        border: '1px solid rgba(0,245,212,0.2)',
+        boxShadow: '0 0 30px rgba(0,245,212,0.08)',
+        background: '#0a0a1a',
+      }}
+    >
+      {/* Video element — mirrored for front camera */}
+      <video
+        ref={videoRef}
+        className="w-full h-full object-cover"
+        style={{ transform: facingMode === 'user' ? 'scaleX(-1)' : 'none' }}
+        playsInline
+        muted
+      />
+
+      {/* Landmark canvas overlay */}
+      {isReady && (
+        <LandmarkCanvas
+          rawLandmarks={rawLandmarks}
+          videoWidth={containerRef.current?.clientWidth ?? 640}
+          videoHeight={containerRef.current?.clientHeight ?? 360}
+          recognised={recognised}
+        />
+      )}
+
+      {/* Loading shimmer */}
+      {!isReady && !error && (
+        <div className="absolute inset-0 flex items-center justify-center">
+          <div className="w-10 h-10 rounded-full border-2 border-transparent"
+               style={{ borderTopColor: '#00f5d4', animation: 'spin 1s linear infinite' }} />
+        </div>
+      )}
+
+      {/* Camera switch button (mobile) */}
+      <button
+        onClick={onSwitchCamera}
+        className="absolute bottom-3 right-3 glass p-2 rounded-xl text-slate-300 hover:text-white transition-colors"
+        title="Switch camera"
+      >
+        <RotateCcw size={16} />
+      </button>
+
+      {/* Hand detected indicator */}
+      {isReady && (
+        <div className="absolute top-3 left-3 flex items-center gap-1.5 px-2 py-1 rounded-lg text-xs"
+             style={{ background: 'rgba(0,0,0,0.5)' }}>
+          <span
+            className="w-2 h-2 rounded-full"
+            style={{
+              background: rawLandmarks ? '#00f5d4' : '#4b5563',
+              boxShadow: rawLandmarks ? '0 0 6px #00f5d4' : 'none',
+            }}
+          />
+          <span className="text-slate-300">{rawLandmarks ? 'Hand detected' : 'No hand'}</span>
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/frontend/src/hooks/useMediaPipe.ts b/frontend/src/hooks/useMediaPipe.ts
new file mode 100644
index 0000000000000000000000000000000000000000..3aef18ac1af69fb2fcbf119992903da2b90eb8ca
--- /dev/null
+++ b/frontend/src/hooks/useMediaPipe.ts
@@ -0,0 +1,145 @@
+import { useEffect, useRef, useState, useCallback } from 'react';
+import {
+  HandLandmarker,
+  FilesetResolver,
+  type HandLandmarkerResult,
+  type NormalizedLandmark,
+} from '@mediapipe/tasks-vision';
+import { normaliseLandmarks } from '../lib/landmarkUtils';
+
+export interface MediaPipeState {
+  landmarks: number[] | null;          // 63-float normalised vector
+  rawLandmarks: NormalizedLandmark[] | null; // 21-point raw result (for canvas drawing)
+  handedness: 'Left' | 'Right' | null;
+  isDetecting: boolean;
+  isLoading: boolean;
+  error: string | null;
+  startDetection: (video: HTMLVideoElement) => void;
+  stopDetection: () => void;
+}
+
+const WASM_URL =
+  'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@latest/wasm';
+const MODEL_URL =
+  'https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/1/hand_landmarker.task';
+
+/**
+ * Hook that drives MediaPipe HandLandmarker inference on a video element.
+ * Runs at ~30 fps using requestAnimationFrame.
+ */
+export function useMediaPipe(): MediaPipeState {
+  const landmarkerRef = useRef<HandLandmarker | null>(null);
+  const rafRef        = useRef<number>(0);
+  const lastTsRef     = useRef<number>(0);
+
+  const [landmarks,    setLandmarks]    = useState<number[] | null>(null);
+  const [rawLandmarks, setRawLandmarks] = useState<NormalizedLandmark[] | null>(null);
+  const [handedness,   setHandedness]   = useState<'Left' | 'Right' | null>(null);
+  const [isDetecting,  setIsDetecting]  = useState(false);
+  const [isLoading,    setIsLoading]    = useState(false);
+  const [error,        setError]        = useState<string | null>(null);
+
+  // initialise on mount
+  useEffect(() => {
+    let cancelled = false;
+    setIsLoading(true);
+
+    (async () => {
+      try {
+        const vision = await FilesetResolver.forVisionTasks(WASM_URL);
+        const hl = await HandLandmarker.createFromOptions(vision, {
+          baseOptions: { modelAssetPath: MODEL_URL, delegate: 'GPU' },
+          runningMode: 'VIDEO',
+          numHands: 1,
+          minHandDetectionConfidence: 0.5,
+          minHandPresenceConfidence: 0.5,
+          minTrackingConfidence: 0.5,
+        });
+        if (!cancelled) {
+          landmarkerRef.current = hl;
+          setIsLoading(false);
+        }
+      } catch (err) {
+        if (!cancelled) {
+          console.error('MediaPipe init error', err);
+          setError('Failed to load hand detection model. Check network.');
+          setIsLoading(false);
+        }
+      }
+    })();
+
+    return () => {
+      cancelled = true;
+      cancelAnimationFrame(rafRef.current);
+      landmarkerRef.current?.close();
+    };
+  }, []);
+
+  const startDetection = useCallback((video: HTMLVideoElement) => {
+    if (!landmarkerRef.current) return;
+    setIsDetecting(true);
+
+    const detect = (now: number) => {
+      if (!landmarkerRef.current || !video || video.paused || video.ended) {
+        rafRef.current = requestAnimationFrame(detect);
+        return;
+      }
+
+      // Throttle to ~30 fps
+      if (now - lastTsRef.current < 33) {
+        rafRef.current = requestAnimationFrame(detect);
+        return;
+      }
+      lastTsRef.current = now;
+
+      let result: HandLandmarkerResult;
+      try {
+        result = landmarkerRef.current.detectForVideo(video, now);
+      } catch {
+        rafRef.current = requestAnimationFrame(detect);
+        return;
+      }
+
+      if (result.handednesses.length > 0 && result.landmarks.length > 0) {
+        const raw = result.landmarks[0];          // NormalizedLandmark[]
+        const hand = result.handednesses[0][0].categoryName as 'Left' | 'Right';
+        try {
+          const flat = normaliseLandmarks(raw);
+          setLandmarks(flat);
+          setRawLandmarks(raw);
+          setHandedness(hand);
+        } catch {
+          setLandmarks(null);
+          setRawLandmarks(null);
+        }
+      } else {
+        setLandmarks(null);
+        setRawLandmarks(null);
+        setHandedness(null);
+      }
+
+      rafRef.current = requestAnimationFrame(detect);
+    };
+
+    rafRef.current = requestAnimationFrame(detect);
+  }, []);
+
+  const stopDetection = useCallback(() => {
+    cancelAnimationFrame(rafRef.current);
+    setIsDetecting(false);
+    setLandmarks(null);
+    setRawLandmarks(null);
+    setHandedness(null);
+  }, []);
+
+  return {
+    landmarks,
+    rawLandmarks,
+    handedness,
+    isDetecting,
+    isLoading,
+    error,
+    startDetection,
+    stopDetection,
+  };
+}
diff --git a/frontend/src/hooks/useWebSocket.ts b/frontend/src/hooks/useWebSocket.ts
new file mode 100644
index 0000000000000000000000000000000000000000..bf0f689136e9487cbae6a38f320b6f306b1408fd
--- /dev/null
+++ b/frontend/src/hooks/useWebSocket.ts
@@ -0,0 +1,103 @@
+import { useEffect, useRef, useState, useCallback } from 'react';
+import type { PredictionResponse } from '../types';
+
+const WS_URL = import.meta.env.VITE_WS_URL ?? 'ws://localhost:8080';
+const RECONNECT_BASE_MS    = 1000;
+const MAX_RECONNECT_MS     = 30_000;
+const MAX_SEND_RATE        = 15; // frames/sec — normal
+const LOW_BW_SEND_RATE     = 5;  // frames/sec — high-latency fallback
+const LOW_BW_LATENCY_MS    = 500; // threshold to activate low-bandwidth mode
+
+export interface WebSocketState {
+  lastPrediction: PredictionResponse | null;
+  isConnected: boolean;
+  latency: number;
+  lowBandwidth: boolean;
+  sendLandmarks: (landmarks: number[], sessionId?: string) => void;
+}
+
+/**
+ * WebSocket hook for sending landmark vectors and receiving predictions.
+ * Implements auto-reconnect with exponential back-off and send-rate throttling.
+ */
+export function useWebSocket(): WebSocketState {
+  const wsRef          = useRef<WebSocket | null>(null);
+  const reconnectDelay = useRef(RECONNECT_BASE_MS);
+  const reconnectTimer = useRef<ReturnType<typeof setTimeout> | null>(null);
+  const lastSendTime   = useRef(0);
+
+  const [lastPrediction, setLastPrediction] = useState<PredictionResponse | null>(null);
+  const [isConnected,    setIsConnected]    = useState(false);
+  const [latency,        setLatency]        = useState(0);
+  const [lowBandwidth,   setLowBandwidth]   = useState(false);
+
+  const inflightTs = useRef<number | null>(null);
+
+  const connect = useCallback(() => {
+    if (wsRef.current?.readyState === WebSocket.OPEN) return;
+
+    const url = `${WS_URL}/ws/landmarks`;
+    const ws  = new WebSocket(url);
+    wsRef.current = ws;
+
+    ws.onopen = () => {
+      setIsConnected(true);
+      reconnectDelay.current = RECONNECT_BASE_MS;
+    };
+
+    ws.onmessage = (evt) => {
+      if (inflightTs.current !== null) {
+        const rtt = Date.now() - inflightTs.current;
+        setLatency(rtt);
+        setLowBandwidth(rtt > LOW_BW_LATENCY_MS);
+        inflightTs.current = null;
+      }
+      try {
+        const data: PredictionResponse = JSON.parse(evt.data);
+        if ('sign' in data) setLastPrediction(data);
+      } catch {
+        // ignore non-JSON messages / error frames
+      }
+    };
+
+    ws.onclose = () => {
+      setIsConnected(false);
+      wsRef.current = null;
+      // Exponential back-off reconnect
+      reconnectTimer.current = setTimeout(() => {
+        reconnectDelay.current = Math.min(reconnectDelay.current * 2, MAX_RECONNECT_MS);
+        connect();
+      }, reconnectDelay.current);
+    };
+
+    ws.onerror = (e) => {
+      console.warn('WebSocket error', e);
+      ws.close();
+    };
+  }, []);
+
+  useEffect(() => {
+    connect();
+    return () => {
+      if (reconnectTimer.current) clearTimeout(reconnectTimer.current);
+      wsRef.current?.close();
+    };
+  }, [connect]);
+
+  /** Throttled send — adapts to 5fps in low-bandwidth mode (latency > 500ms) */
+  const sendLandmarks = useCallback((landmarks: number[], sessionId = 'browser') => {
+    const ws = wsRef.current;
+    if (!ws || ws.readyState !== WebSocket.OPEN) return;
+
+    const now = Date.now();
+    const effectiveRate = lowBandwidth ? LOW_BW_SEND_RATE : MAX_SEND_RATE;
+    const minInterval = 1000 / effectiveRate;
+    if (now - lastSendTime.current < minInterval) return;
+    lastSendTime.current = now;
+
+    inflightTs.current = now;
+    ws.send(JSON.stringify({ landmarks, session_id: sessionId }));
+  }, [lowBandwidth]);
+
+  return { lastPrediction, isConnected, latency, lowBandwidth, sendLandmarks };
+}
diff --git a/frontend/src/hooks/useWebcam.ts b/frontend/src/hooks/useWebcam.ts
new file mode 100644
index 0000000000000000000000000000000000000000..f7d867370e3836d9dde5fb17d48c334b5fd01405
--- /dev/null
+++ b/frontend/src/hooks/useWebcam.ts
@@ -0,0 +1,65 @@
+import { useEffect, useRef, useState, useCallback } from 'react';
+
+export interface WebcamState {
+  videoRef: React.RefObject<HTMLVideoElement | null>;
+  isReady: boolean;
+  error: string | null;
+  facingMode: 'user' | 'environment';
+  switchCamera: () => void;
+}
+
+/**
+ * Hook to access the device webcam.
+ * Supports front/back camera toggle for mobile devices.
+ */
+export function useWebcam(): WebcamState {
+  const videoRef = useRef<HTMLVideoElement | null>(null);
+  const streamRef = useRef<MediaStream | null>(null);
+  const [isReady, setIsReady] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [facingMode, setFacingMode] = useState<'user' | 'environment'>('user');
+
+  const startCamera = useCallback(async (mode: 'user' | 'environment') => {
+    // Stop any existing stream first
+    streamRef.current?.getTracks().forEach(t => t.stop());
+    setIsReady(false);
+    setError(null);
+
+    try {
+      const stream = await navigator.mediaDevices.getUserMedia({
+        video: { width: { ideal: 1280 }, height: { ideal: 720 }, facingMode: mode },
+        audio: false,
+      });
+      streamRef.current = stream;
+
+      if (videoRef.current) {
+        videoRef.current.srcObject = stream;
+        videoRef.current.onloadedmetadata = () => {
+          videoRef.current?.play();
+          setIsReady(true);
+        };
+      }
+    } catch (err) {
+      const msg = err instanceof Error ? err.message : String(err);
+      setError(
+        msg.includes('Permission')
+          ? 'Camera permission denied. Please allow camera access and reload.'
+          : `Camera error: ${msg}`
+      );
+    }
+  }, []);
+
+  // Start camera on mount and whenever facingMode changes
+  useEffect(() => {
+    startCamera(facingMode);
+    return () => {
+      streamRef.current?.getTracks().forEach(t => t.stop());
+    };
+  }, [facingMode, startCamera]);
+
+  const switchCamera = useCallback(() => {
+    setFacingMode(prev => (prev === 'user' ? 'environment' : 'user'));
+  }, []);
+
+  return { videoRef, isReady, error, facingMode, switchCamera };
+}
diff --git a/frontend/src/index.css b/frontend/src/index.css
new file mode 100644
index 0000000000000000000000000000000000000000..6c3f0a9cebb4997d3830ed5b8bd27a6fdd8b1989
--- /dev/null
+++ b/frontend/src/index.css
@@ -0,0 +1,64 @@
+@import "tailwindcss";
+
+/* ── Custom CSS variables ──────────────────────────────────────── */
+:root {
+  --color-neon: #00f5d4;
+  --color-accent: #9b5de5;
+  --color-warn: #fee440;
+  --color-danger: #f15bb5;
+  --color-bg: #050816;
+  --color-surface: rgba(255, 255, 255, 0.06);
+  --color-border: rgba(255, 255, 255, 0.12);
+}
+
+html,
+body,
+#root {
+  height: 100%;
+  margin: 0;
+  font-family: 'Inter', 'Segoe UI', system-ui, sans-serif;
+  background-color: var(--color-bg);
+  color: #e2e8f0;
+  overflow-x: hidden;
+  -webkit-font-smoothing: antialiased;
+}
+
+/* ── Glassmorphism utility ─────────────────────────────────────── */
+.glass {
+  background: var(--color-surface);
+  backdrop-filter: blur(16px) saturate(180%);
+  -webkit-backdrop-filter: blur(16px) saturate(180%);
+  border: 1px solid var(--color-border);
+  border-radius: 1rem;
+}
+
+/* ── Neon glow text ────────────────────────────────────────────── */
+.glow-text {
+  text-shadow:
+    0 0 8px var(--color-neon),
+    0 0 20px var(--color-neon),
+    0 0 40px rgba(0, 245, 212, 0.4);
+}
+
+/* ── Neon glow border ──────────────────────────────────────────── */
+.glow-border {
+  box-shadow:
+    0 0 10px rgba(0, 245, 212, 0.3),
+    inset 0 0 10px rgba(0, 245, 212, 0.05);
+  border-color: rgba(0, 245, 212, 0.5) !important;
+}
+
+/* ── Animated gradient background ────────────────────────────── */
+.bg-animated {
+  background:
+    radial-gradient(ellipse at 20% 50%, rgba(155, 93, 229, 0.15) 0%, transparent 60%),
+    radial-gradient(ellipse at 80% 20%, rgba(0, 245, 212, 0.10) 0%, transparent 50%),
+    radial-gradient(ellipse at 60% 80%, rgba(241, 91, 181, 0.08) 0%, transparent 50%),
+    #050816;
+}
+
+/* ── Scrollbar ────────────────────────────────────────────────── */
+::-webkit-scrollbar { width: 4px; }
+::-webkit-scrollbar-track  { background: transparent; }
+::-webkit-scrollbar-thumb  { background: rgba(0, 245, 212, 0.3); border-radius: 2px; }
+::-webkit-scrollbar-thumb:hover { background: rgba(0, 245, 212, 0.6); }
diff --git a/frontend/src/lib/landmarkUtils.ts b/frontend/src/lib/landmarkUtils.ts
new file mode 100644
index 0000000000000000000000000000000000000000..cc4e562b314ac6ee7e524eab6542c9b181084e34
--- /dev/null
+++ b/frontend/src/lib/landmarkUtils.ts
@@ -0,0 +1,50 @@
+/**
+ * Landmark utility functions — mirrors the backend preprocessing.
+ *
+ * The XGBoost model (Pipeline A) was trained on MediaPipe landmarks that are
+ * already normalised to [0,1] relative to the image frame.  The simplest
+ * preprocessing that makes the model translation-invariant is to subtract the
+ * wrist landmark (index 0) so every coordinate is relative to the wrist.
+ *
+ * ⚠️  If your training notebook used a different normalisation (e.g. min-max
+ *     scaling or only x/y without z), update normaliseLandmarks() to match.
+ */
+
+/** MediaPipe Hands result landmark shape */
+export interface RawLandmark {
+  x: number;
+  y: number;
+  z: number;
+}
+
+/**
+ * Convert MediaPipe NormalizedLandmark[] (21 points) to a flat 63-element
+ * array, then subtract the wrist position to centre the hand.
+ */
+export function normaliseLandmarks(raw: RawLandmark[]): number[] {
+  if (raw.length !== 21) {
+    throw new Error(`Expected 21 landmarks, got ${raw.length}`);
+  }
+
+  const wrist = raw[0];
+
+  const flat: number[] = [];
+  for (const lm of raw) {
+    flat.push(lm.x - wrist.x, lm.y - wrist.y, lm.z - wrist.z);
+  }
+  return flat; // length 63
+}
+
+/**
+ * MediaPipe hand topology — pairs of landmark indices that form bones.
+ * Used by LandmarkCanvas to draw connections between landmarks.
+ */
+export const HAND_CONNECTIONS: [number, number][] = [
+  // Palm
+  [0, 1], [1, 2], [2, 3], [3, 4],         // Thumb
+  [0, 5], [5, 6], [6, 7], [7, 8],          // Index
+  [5, 9], [9, 10], [10, 11], [11, 12],     // Middle
+  [9, 13], [13, 14], [14, 15], [15, 16],   // Ring
+  [13, 17], [17, 18], [18, 19], [19, 20],  // Pinky
+  [0, 17],                                  // Wrist–pinky base
+];
diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..bef5202a32cbd0632c43de40f6e908532903fd42
--- /dev/null
+++ b/frontend/src/main.tsx
@@ -0,0 +1,10 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.tsx'
+
+createRoot(document.getElementById('root')!).render(
+  <StrictMode>
+    <App />
+  </StrictMode>,
+)
diff --git a/frontend/src/types.ts b/frontend/src/types.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4ccbcb9b6762ce7f2db1b24446b8aaec6c8fa253
--- /dev/null
+++ b/frontend/src/types.ts
@@ -0,0 +1,24 @@
+/**
+ * Shared TypeScript types for SanketSetu frontend.
+ */
+
+export interface PredictionResponse {
+  sign: string;
+  confidence: number;
+  pipeline: string;
+  label_index: number;
+  probabilities?: number[];
+  latency_ms?: number;
+}
+
+export interface HealthResponse {
+  status: string;
+  models_loaded: boolean;
+  pipelines_available: string[];
+}
+
+/** Normalised 21-landmark hand data from MediaPipe */
+export interface HandLandmarks {
+  landmarks: number[];   // flat [x0,y0,z0 … x20,y20,z20] — 63 floats
+  handedness: 'Left' | 'Right';
+}
diff --git a/frontend/tsconfig.app.json b/frontend/tsconfig.app.json
new file mode 100644
index 0000000000000000000000000000000000000000..a9b5a59ca647ca0102d2501b56cedd83ad7b6818
--- /dev/null
+++ b/frontend/tsconfig.app.json
@@ -0,0 +1,28 @@
+{
+  "compilerOptions": {
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
+    "target": "ES2022",
+    "useDefineForClassFields": true,
+    "lib": ["ES2022", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "types": ["vite/client"],
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "verbatimModuleSyntax": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+    "jsx": "react-jsx",
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "erasableSyntaxOnly": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedSideEffectImports": true
+  },
+  "include": ["src"]
+}
diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json
new file mode 100644
index 0000000000000000000000000000000000000000..1ffef600d959ec9e396d5a260bd3f5b927b2cef8
--- /dev/null
+++ b/frontend/tsconfig.json
@@ -0,0 +1,7 @@
+{
+  "files": [],
+  "references": [
+    { "path": "./tsconfig.app.json" },
+    { "path": "./tsconfig.node.json" }
+  ]
+}
diff --git a/frontend/tsconfig.node.json b/frontend/tsconfig.node.json
new file mode 100644
index 0000000000000000000000000000000000000000..8a67f62f4ceebff3424e6e8cd4b3c25b17347546
--- /dev/null
+++ b/frontend/tsconfig.node.json
@@ -0,0 +1,26 @@
+{
+  "compilerOptions": {
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
+    "target": "ES2023",
+    "lib": ["ES2023"],
+    "module": "ESNext",
+    "types": ["node"],
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "verbatimModuleSyntax": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "erasableSyntaxOnly": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedSideEffectImports": true
+  },
+  "include": ["vite.config.ts"]
+}
diff --git a/frontend/vercel.json b/frontend/vercel.json
new file mode 100644
index 0000000000000000000000000000000000000000..c1923912373c0325c4bd6db733173d171cd07f7e
--- /dev/null
+++ b/frontend/vercel.json
@@ -0,0 +1,23 @@
+{
+  "buildCommand": "npm run build",
+  "outputDirectory": "dist",
+  "framework": "vite",
+  "rewrites": [
+    { "source": "/(.*)", "destination": "/index.html" }
+  ],
+  "headers": [
+    {
+      "source": "/assets/(.*)",
+      "headers": [
+        { "key": "Cache-Control", "value": "public, max-age=31536000, immutable" }
+      ]
+    },
+    {
+      "source": "/(.*).wasm",
+      "headers": [
+        { "key": "Content-Type", "value": "application/wasm" },
+        { "key": "Cache-Control", "value": "public, max-age=86400" }
+      ]
+    }
+  ]
+}
diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts
new file mode 100644
index 0000000000000000000000000000000000000000..62645b731d40bbb353b7f34e079fe4b183619385
--- /dev/null
+++ b/frontend/vite.config.ts
@@ -0,0 +1,24 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+import tailwindcss from '@tailwindcss/vite'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [
+    react(),
+    tailwindcss(),
+  ],
+  server: {
+    proxy: {
+      '/api': {
+        target: 'http://localhost:8000',
+        changeOrigin: true,
+      },
+      '/ws': {
+        target: 'ws://localhost:8000',
+        ws: true,
+        changeOrigin: true,
+      },
+    },
+  },
+})
diff --git a/start.ps1 b/start.ps1
new file mode 100644
index 0000000000000000000000000000000000000000..f5e130a43092229d5276526ef17aec5931928ea4
--- /dev/null
+++ b/start.ps1
@@ -0,0 +1,80 @@
+#!/usr/bin/env pwsh
+# ============================================================
+# SanketSetu — start both servers with a single command
+# Usage:  .\start.ps1
+#
+# Starts:
+#   Backend  → http://localhost:8000
+#   Frontend → http://localhost:5173
+# ============================================================
+
+$ROOT     = $PSScriptRoot
+$VENV_PY  = "$ROOT\.venv\Scripts\python.exe"
+$VENV_UV  = "$ROOT\.venv\Scripts\uvicorn.exe"
+$BACKEND  = "$ROOT\backend"
+$FRONTEND = "$ROOT\frontend"
+
+# ── sanity checks ──────────────────────────────────────────
+if (-not (Test-Path $VENV_UV)) {
+    Write-Error "venv not found. Run:  python -m venv .venv  then  .venv\Scripts\pip install -r backend\requirements.txt"
+    exit 1
+}
+if (-not (Test-Path "$FRONTEND\node_modules")) {
+    Write-Host "⚙ Installing frontend deps…" -ForegroundColor Cyan
+    Push-Location $FRONTEND
+    npm install
+    Pop-Location
+}
+
+# ── port check & kill ─────────────────────────────────────
+foreach ($port in @(8000, 5173)) {
+    $pid_ = (netstat -ano | Select-String ":$port .*LISTENING") -replace '.*\s(\d+)$','$1' | Select-Object -Last 1
+    if ($pid_) {
+        Write-Host "[port $port] Freeing PID $pid_…" -ForegroundColor Yellow
+        taskkill /PID $pid_ /F 2>$null | Out-Null
+        Start-Sleep -Milliseconds 500
+    }
+}
+
+# ── start backend in a new window ─────────────────────────
+Write-Host "🚀 Starting backend on :8000 …" -ForegroundColor Green
+$backendJob = Start-Process -FilePath "powershell.exe" -ArgumentList `
+    "-NoProfile", "-Command",
+    "Set-Location '$BACKEND'; & '$VENV_UV' app.main:app --port 8000" `
+    -PassThru -WindowStyle Normal
+
+# ── wait for backend ready (max 3 min) ────────────────────
+Write-Host "⏳ Waiting for backend to load models (may take ~2 min)…" -ForegroundColor Cyan
+$ready = $false
+for ($i = 0; $i -lt 36; $i++) {
+    Start-Sleep -Seconds 5
+    try {
+        $r = Invoke-WebRequest "http://localhost:8000/health" -UseBasicParsing -TimeoutSec 3 -ErrorAction Stop
+        Write-Host "✅ Backend ready: $($r.Content)" -ForegroundColor Green
+        $ready = $true; break
+    } catch { Write-Host "   …waiting ($([int]($i*5+5))s)" }
+}
+if (-not $ready) { Write-Warning "Backend didn't respond in time. Check the backend window." }
+
+# ── start frontend ─────────────────────────────────────────
+Write-Host "🌐 Starting frontend on :5173 …" -ForegroundColor Green
+$frontendJob = Start-Process -FilePath "powershell.exe" -ArgumentList `
+    "-NoProfile", "-Command",
+    "Set-Location '$FRONTEND'; npm run dev" `
+    -PassThru -WindowStyle Normal
+
+Start-Sleep -Seconds 3
+Write-Host ""
+Write-Host "╔══════════════════════════════════════════╗" -ForegroundColor Magenta
+Write-Host "║  SanketSetu is running!                  ║" -ForegroundColor Magenta
+Write-Host "║  Frontend : http://localhost:5173         ║" -ForegroundColor Magenta
+Write-Host "║  Backend  : http://localhost:8000         ║" -ForegroundColor Magenta
+Write-Host "║  API Docs : http://localhost:8000/docs    ║" -ForegroundColor Magenta
+Write-Host "╚══════════════════════════════════════════╝" -ForegroundColor Magenta
+Write-Host ""
+Write-Host "Press any key to stop both servers…" -ForegroundColor Yellow
+$null = $Host.UI.RawUI.ReadKey("NoEcho,IncludeKeyDown")
+
+Stop-Process -Id $backendJob.Id  -Force -ErrorAction SilentlyContinue
+Stop-Process -Id $frontendJob.Id -Force -ErrorAction SilentlyContinue
+Write-Host "Servers stopped." -ForegroundColor Red