Add OpenTryOn MCP Server v0.0.1

mcp-server/config.py

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+"""Configuration management for OpenTryOn MCP Server."""
+
+import os
+from pathlib import Path
+from typing import Optional
+from dotenv import load_dotenv
+
+# Load environment variables from parent directory
+parent_dir = Path(__file__).parent.parent
+env_path = parent_dir / ".env"
+try:
+    if env_path.exists():
+        load_dotenv(env_path)
+except Exception:
+    # Silently fail if .env cannot be loaded (e.g., permission issues)
+    pass
+
+
+class Config:
+    """Configuration for OpenTryOn MCP Server."""
+
+    # Server settings
+    SERVER_NAME = "opentryon-mcp"
+    SERVER_VERSION = "0.0.1"
+
+    # AWS/Amazon Nova Canvas
+    AWS_ACCESS_KEY_ID: Optional[str] = os.getenv("AWS_ACCESS_KEY_ID")
+    AWS_SECRET_ACCESS_KEY: Optional[str] = os.getenv("AWS_SECRET_ACCESS_KEY")
+    AMAZON_NOVA_REGION: str = os.getenv("AMAZON_NOVA_REGION", "us-east-1")
+    AMAZON_NOVA_MODEL_ID: str = os.getenv("AMAZON_NOVA_MODEL_ID", "amazon.nova-canvas-v1:0")
+
+    # Kling AI
+    KLING_AI_API_KEY: Optional[str] = os.getenv("KLING_AI_API_KEY")
+    KLING_AI_SECRET_KEY: Optional[str] = os.getenv("KLING_AI_SECRET_KEY")
+    KLING_AI_BASE_URL: str = os.getenv("KLING_AI_BASE_URL", "https://api-singapore.klingai.com")
+
+    # Segmind
+    SEGMIND_API_KEY: Optional[str] = os.getenv("SEGMIND_API_KEY")
+
+    # Google Gemini (Nano Banana)
+    GEMINI_API_KEY: Optional[str] = os.getenv("GEMINI_API_KEY")
+
+    # BFL API (FLUX.2)
+    BFL_API_KEY: Optional[str] = os.getenv("BFL_API_KEY")
+
+    # Luma AI
+    LUMA_AI_API_KEY: Optional[str] = os.getenv("LUMA_AI_API_KEY")
+
+    # U2Net Checkpoint
+    U2NET_CLOTH_SEG_CHECKPOINT_PATH: Optional[str] = os.getenv("U2NET_CLOTH_SEG_CHECKPOINT_PATH")
+
+    # File handling
+    MAX_FILE_SIZE_MB = 50
+    ALLOWED_IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".webp"}
+    ALLOWED_VIDEO_EXTENSIONS = {".mp4", ".mov", ".avi"}
+
+    # Temporary files
+    TEMP_DIR = Path("/tmp/opentryon_mcp")
+    TEMP_DIR.mkdir(exist_ok=True, parents=True)
+
+    @classmethod
+    def validate(cls) -> dict[str, bool]:
+        """Validate configuration and return status of each service."""
+        return {
+            "amazon_nova": bool(cls.AWS_ACCESS_KEY_ID and cls.AWS_SECRET_ACCESS_KEY),
+            "kling_ai": bool(cls.KLING_AI_API_KEY and cls.KLING_AI_SECRET_KEY),
+            "segmind": bool(cls.SEGMIND_API_KEY),
+            "gemini": bool(cls.GEMINI_API_KEY),
+            "flux2": bool(cls.BFL_API_KEY),
+            "luma_ai": bool(cls.LUMA_AI_API_KEY),
+            "u2net": bool(cls.U2NET_CLOTH_SEG_CHECKPOINT_PATH),
+        }
+
+    @classmethod
+    def get_status_message(cls) -> str:
+        """Get a human-readable status message with helpful guidance."""
+        status = cls.validate()
+        lines = ["OpenTryOn MCP Server Configuration Status:"]
+
+        # Virtual Try-On Services
+        nova_status = "✓ Configured" if status['amazon_nova'] else "✗ Not configured (optional - requires AWS)"
+        kling_status = "✓ Configured" if status['kling_ai'] else "✗ Not configured (recommended)"
+        segmind_status = "✓ Configured" if status['segmind'] else "✗ Not configured (recommended)"
+
+        lines.append(f"  Amazon Nova Canvas: {nova_status}")
+        lines.append(f"  Kling AI: {kling_status}")
+        lines.append(f"  Segmind: {segmind_status}")
+
+        # Image Generation Services
+        gemini_status = "✓ Configured" if status['gemini'] else "✗ Not configured (recommended)"
+        flux_status = "✓ Configured" if status['flux2'] else "✗ Not configured (recommended)"
+        luma_status = "✓ Configured" if status['luma_ai'] else "✗ Not configured (optional - for video)"
+
+        lines.append(f"  Gemini (Nano Banana): {gemini_status}")
+        lines.append(f"  FLUX.2: {flux_status}")
+        lines.append(f"  Luma AI: {luma_status}")
+
+        # Preprocessing
+        u2net_status = "✓ Configured" if status['u2net'] else "✗ Not configured (optional - for local segmentation)"
+        lines.append(f"  U2Net (Preprocessing): {u2net_status}")
+
+        # Add helpful message
+        vton_count = sum([status['amazon_nova'], status['kling_ai'], status['segmind']])
+        img_count = sum([status['gemini'], status['flux2'], status['luma_ai']])
+
+        lines.append("")
+        if vton_count == 0:
+            lines.append("⚠️  Warning: No virtual try-on service configured!")
+            lines.append("   Configure at least one: Kling AI (recommended) or Segmind")
+        if img_count == 0:
+            lines.append("⚠️  Warning: No image generation service configured!")
+            lines.append("   Configure at least one: Gemini (recommended) or FLUX.2")
+
+        if vton_count > 0 and img_count > 0:
+            lines.append("✅ Ready! At least one service from each category is configured.")
+            lines.append(f"   Virtual Try-On: {vton_count}/3 services")
+            lines.append(f"   Image Generation: {img_count}/3 services")
+
+        lines.append("")
+        lines.append("💡 Tip: Copy env.template to .env and add your API keys")
+        lines.append("📖 Setup guide: mcp-server/README.md")
+
+        return "\n".join(lines)
+
+    @classmethod
+    def get_missing_services(cls) -> dict[str, list[str]]:
+        """Get list of missing services by category."""
+        status = cls.validate()
+
+        missing = {
+            "virtual_tryon": [],
+            "image_generation": [],
+            "optional": []
+        }
+
+        # Virtual Try-On (at least one required)
+        if not status['kling_ai']:
+            missing["virtual_tryon"].append("Kling AI")
+        if not status['segmind']:
+            missing["virtual_tryon"].append("Segmind")
+        if not status['amazon_nova']:
+            missing["optional"].append("Amazon Nova Canvas (requires AWS)")
+
+        # Image Generation (at least one required)
+        if not status['gemini']:
+            missing["image_generation"].append("Gemini (Nano Banana)")
+        if not status['flux2']:
+            missing["image_generation"].append("FLUX.2")
+
+        # Optional services
+        if not status['luma_ai']:
+            missing["optional"].append("Luma AI (for video generation)")
+        if not status['u2net']:
+            missing["optional"].append("U2Net (for local garment segmentation)")
+
+        return missing
+
+    @classmethod
+    def is_ready(cls) -> bool:
+        """Check if minimum required services are configured."""
+        status = cls.validate()
+
+        # Need at least one virtual try-on service
+        has_vton = status['kling_ai'] or status['segmind'] or status['amazon_nova']
+
+        # Need at least one image generation service
+        has_img_gen = status['gemini'] or status['flux2'] or status['luma_ai']
+
+        return has_vton and has_img_gen
+
+
+config = Config()
+

mcp-server/examples/claude_desktop_config.json

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+{
+  "mcpServers": {
+    "opentryon": {
+      "command": "python",
+      "args": [
+        "/absolute/path/to/opentryon/mcp-server/server.py"
+      ],
+      "env": {
+        "PYTHONPATH": "/absolute/path/to/opentryon"
+      }
+    }
+  }
+}
+

mcp-server/examples/example_usage.py

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+"""Example usage of OpenTryOn MCP Server tools."""
+
+import json
+from pathlib import Path
+import sys
+
+# Add parent directory to path to import tools
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from tools import (
+    virtual_tryon_nova,
+    virtual_tryon_kling,
+    virtual_tryon_segmind,
+    generate_image_nano_banana,
+    generate_image_nano_banana_pro,
+    generate_image_flux2_pro,
+    generate_video_luma_ray,
+    segment_garment,
+    load_fashion_mnist,
+)
+
+
+def example_virtual_tryon():
+    """Example: Virtual try-on with different providers."""
+    print("\n=== Virtual Try-On Examples ===\n")
+
+    # Example 1: Amazon Nova Canvas
+    print("1. Amazon Nova Canvas Virtual Try-On")
+    result = virtual_tryon_nova(
+        source_image="path/to/person.jpg",
+        reference_image="path/to/garment.jpg",
+        garment_class="UPPER_BODY",
+        output_dir="outputs/nova"
+    )
+    print(json.dumps(result, indent=2))
+
+    # Example 2: Kling AI
+    print("\n2. Kling AI Virtual Try-On")
+    result = virtual_tryon_kling(
+        source_image="path/to/person.jpg",
+        reference_image="path/to/garment.jpg",
+        output_dir="outputs/kling"
+    )
+    print(json.dumps(result, indent=2))
+
+    # Example 3: Segmind
+    print("\n3. Segmind Virtual Try-On")
+    result = virtual_tryon_segmind(
+        model_image="path/to/person.jpg",
+        cloth_image="path/to/garment.jpg",
+        category="Upper body",
+        num_inference_steps=35,
+        guidance_scale=2.5,
+        output_dir="outputs/segmind"
+    )
+    print(json.dumps(result, indent=2))
+
+
+def example_image_generation():
+    """Example: Image generation with different models."""
+    print("\n=== Image Generation Examples ===\n")
+
+    # Example 1: Nano Banana (Fast)
+    print("1. Nano Banana - Fast Image Generation")
+    result = generate_image_nano_banana(
+        prompt="A professional fashion model wearing elegant evening wear on a runway",
+        aspect_ratio="16:9",
+        output_dir="outputs/nano_banana"
+    )
+    print(json.dumps(result, indent=2))
+
+    # Example 2: Nano Banana Pro (4K)
+    print("\n2. Nano Banana Pro - 4K Image Generation")
+    result = generate_image_nano_banana_pro(
+        prompt="Professional fashion photography of elegant evening wear",
+        resolution="4K",
+        aspect_ratio="16:9",
+        use_search_grounding=True,
+        output_dir="outputs/nano_banana_pro"
+    )
+    print(json.dumps(result, indent=2))
+
+    # Example 3: FLUX.2 PRO
+    print("\n3. FLUX.2 PRO - High-Quality Image Generation")
+    result = generate_image_flux2_pro(
+        prompt="A stylish fashion model in modern casual wear",
+        width=1024,
+        height=1024,
+        seed=42,
+        output_dir="outputs/flux2_pro"
+    )
+    print(json.dumps(result, indent=2))
+
+
+def example_video_generation():
+    """Example: Video generation with Luma AI."""
+    print("\n=== Video Generation Examples ===\n")
+
+    # Example 1: Text-to-Video
+    print("1. Text-to-Video with Ray 2")
+    result = generate_video_luma_ray(
+        prompt="A fashion model walking on a runway in elegant evening wear",
+        model="ray-2",
+        mode="text_video",
+        resolution="720p",
+        duration="5s",
+        aspect_ratio="16:9",
+        output_dir="outputs/videos"
+    )
+    print(json.dumps(result, indent=2))
+
+    # Example 2: Image-to-Video with Keyframes
+    print("\n2. Image-to-Video with Keyframes")
+    result = generate_video_luma_ray(
+        prompt="Model walking gracefully",
+        model="ray-2",
+        mode="image_video",
+        start_image="path/to/start_frame.jpg",
+        end_image="path/to/end_frame.jpg",
+        resolution="720p",
+        duration="5s",
+        output_dir="outputs/videos"
+    )
+    print(json.dumps(result, indent=2))
+
+
+def example_preprocessing():
+    """Example: Preprocessing tools."""
+    print("\n=== Preprocessing Examples ===\n")
+
+    # Example 1: Segment Garment
+    print("1. Segment Garment")
+    result = segment_garment(
+        input_path="path/to/garment_images",
+        output_dir="outputs/segmented",
+        garment_class="upper"
+    )
+    print(json.dumps(result, indent=2))
+
+
+def example_datasets():
+    """Example: Dataset loading."""
+    print("\n=== Dataset Examples ===\n")
+
+    # Example 1: Fashion-MNIST
+    print("1. Load Fashion-MNIST Dataset")
+    result = load_fashion_mnist(
+        download=True,
+        normalize=True,
+        flatten=False
+    )
+    print(json.dumps(result, indent=2))
+
+
+def main():
+    """Run all examples."""
+    print("OpenTryOn MCP Server - Example Usage")
+    print("=" * 50)
+
+    # Note: These examples show the API structure
+    # Actual execution requires valid image paths and API keys
+
+    print("\nNote: These are example API calls.")
+    print("Replace paths and ensure API keys are configured in .env file.")
+    print("\nUncomment the examples you want to run:\n")
+
+    # Uncomment to run examples:
+    # example_virtual_tryon()
+    # example_image_generation()
+    # example_video_generation()
+    # example_preprocessing()
+    # example_datasets()
+
+    print("\nExamples complete!")
+
+
+if __name__ == "__main__":
+    main()
+