Docker File Update

Dockerfile

@@ -0,0 +1,52 @@
+# Stage 1: Build Frontend (SvelteKit)
+FROM node:20-slim AS frontend-builder
+WORKDIR /app
+COPY package.json package-lock.json ./
+# Install dependencies
+RUN npm ci
+# Copy source
+COPY . .
+# Build static files (outputs to build/)
+RUN npm run build
+
+# Stage 2: Backend (Python Flask + GPU Support)
+# Use official PyTorch image with CUDA 12.1 support
+FROM pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime
+
+# Install system dependencies for audio (libsndfile) and ffmpeg
+# reliable ffmpeg install on debian-based images
+RUN apt-get update && apt-get install -y \
+    libsndfile1 \
+    ffmpeg \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Copy python requirements
+# We remove torch/torchaudio from requirements briefly during install to avoid re-installing CPU versions
+# or we trust pip to see the installed version satisfies it.
+COPY server/requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy backend code
+COPY server/ .
+
+# Copy built frontend from Stage 1 into the expected relative path
+# app.py expects '../build', so we copy to /build and structure appropriately
+# However, simpler is to copy build/ to /app/build and adjust app.py or folder structure.
+# Let's mirror the structure: /app/server (WORKDIR) and /app/build
+COPY --from=frontend-builder /app/build /app/build
+
+# Set working directory to server where app.py resides
+WORKDIR /app/server
+
+# Environment variables
+ENV PYTHONUNBUFFERED=1
+ENV PORT=5000
+
+# Expose port
+EXPOSE ${PORT}
+
+# Run the application
+CMD ["python", "app.py"]

README.md

@@ -21,6 +21,18 @@ AudioImage is a powerful web-based tool for Audio Spectrogram Art and Digital St
 
 ## Installation
 
+### Method 1: Docker (Recommended for GPU)
+The easiest way to run AudioImage with full GPU acceleration is using Docker.
+
+1.  **Prerequisites**: Docker Desktop + NVIDIA Container Toolkit (for GPU).
+2.  **Run**:
+    ```bash
+    docker compose up --build
+    ```
+3.  Open `http://localhost:5000`.
+
+### Method 2: Manual Setup
+
 1.  **Backend Setup**:
     ```bash
     cd server
@@ -29,12 +41,6 @@ AudioImage is a powerful web-based tool for Audio Spectrogram Art and Digital St
     pip install -r requirements.txt
     ```
 
-    *Optional for GPU support:*
-    ```bash
-    pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118
-    ```
-    *(Adjust CUDA version as needed)*
-
 2.  **Frontend Setup**:
     ```bash
     # Root directory
@@ -42,15 +48,11 @@ AudioImage is a powerful web-based tool for Audio Spectrogram Art and Digital St
     npm run build
     ```
 
-## Running the Application
+3.  **Run**:
-
-1.  Start the Flask server (which serves the frontend):
     ```bash
     cd server
-    source venv/bin/activate
     python app.py
     ```
-2.  Open your browser to `http://127.0.0.1:5000`.
 
 ## Architecture
 

docker-compose.yml

@@ -0,0 +1,19 @@
+services:
+  audio-image:
+    build: .
+    container_name: audio-image
+    ports:
+      - "${PORT:-5000}:5000"
+    volumes:
+      - ./server/uploads:/app/server/uploads
+    environment:
+      - FLASK_APP=app.py
+      - FLASK_DEBUG=${FLASK_DEBUG:-false}
+    restart: unless-stopped
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [ gpu ]

example.env

@@ -0,0 +1,12 @@
+# Server Configuration
+PORT=5000
+FLASK_DEBUG=false
+FLASK_ENV=production
+
+# Application Settings
+# Max upload size in MB (default 40 in code)
+MAX_UPLOAD_MB=40
+
+# Optional: UID/GID for permission handling in Docker volume mapping
+# PUID=1000
+# PGID=1000

server/app.py

@@ -234,4 +234,4 @@ def get_result(result_id):
     return jsonify({"error": "Result not found"}), 404
 
 if __name__ == '__main__':
-    app.run(debug=True, port=5000, threaded=True)
+    app.run(host='0.0.0.0', debug=True, port=5000, threaded=True)

server/requirements.txt

@@ -1,3 +1,4 @@
+--extra-index-url https://download.pytorch.org/whl/cu121
 Flask
 Flask-Cors
 numpy