Implement official Gradio MCP server

- Follow official Gradio MCP documentation
- Use gradio[mcp]>=5.0.0 with mcp_server=True
- Create official_app.py with proper MCP tool functions
- Add detailed docstrings for all AI tools
- Support background removal, image upscaling, generation, vectorization, extension, and video upscaling
- Update README.md to use official implementation

.env.example

@@ -4,7 +4,6 @@
 # A1D API Key (Required)
 # Get your API key from: https://a1d.ai/home/api
 A1D_API_KEY=z4Zl7OhAM5r-DxyVFDN1A
-
 # Optional: Gradio Configuration
 # GRADIO_SERVER_NAME=0.0.0.0
 # GRADIO_SERVER_PORT=7860

README.md

@@ -4,8 +4,8 @@ emoji: 🤖
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 4.32.0
-app_file: simple_app.py
+sdk_version: 5.33.0
+app_file: official_app.py
 pinned: false
 license: mit
 tags:

minimal_app.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python3
+"""
+Minimal A1D MCP Server with Gradio - Ultra-simple version to avoid all compatibility issues
+"""
+
+import gradio as gr
+import os
+from utils import A1DAPIClient, prepare_request_data, format_response_with_preview
+from config import TOOLS_CONFIG
+
+def get_api_client():
+    """Get API client with current API key"""
+    api_key = os.getenv("A1D_API_KEY")
+    if not api_key:
+        raise ValueError("A1D_API_KEY environment variable is required")
+    return A1DAPIClient(api_key)
+
+def process_remove_bg(image_url):
+    """Remove background from images using AI"""
+    try:
+        if not image_url or not image_url.strip():
+            return "❌ Error: Please provide an image URL"
+        
+        client = get_api_client()
+        data = prepare_request_data("remove_bg", image_url=image_url)
+        response = client.make_request_with_result(
+            TOOLS_CONFIG["remove_bg"]["api_endpoint"],
+            data,
+            timeout=120
+        )
+        message, media_url = format_response_with_preview(response, "remove_bg")
+        return f"{message}\n\nResult URL: {media_url}" if media_url else message
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+def process_upscale(image_url, scale):
+    """Upscale images using AI"""
+    try:
+        if not image_url or not image_url.strip():
+            return "❌ Error: Please provide an image URL"
+        
+        client = get_api_client()
+        data = prepare_request_data("image_upscaler", image_url=image_url, scale=int(scale))
+        response = client.make_request_with_result(
+            TOOLS_CONFIG["image_upscaler"]["api_endpoint"],
+            data,
+            timeout=120
+        )
+        message, media_url = format_response_with_preview(response, "image_upscaler")
+        return f"{message}\n\nResult URL: {media_url}" if media_url else message
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+def process_generate(prompt):
+    """Generate images using AI from text prompts"""
+    try:
+        if not prompt or not prompt.strip():
+            return "❌ Error: Please provide a text prompt"
+        
+        client = get_api_client()
+        data = prepare_request_data("image_generator", prompt=prompt)
+        response = client.make_request_with_result(
+            TOOLS_CONFIG["image_generator"]["api_endpoint"],
+            data,
+            timeout=120
+        )
+        message, media_url = format_response_with_preview(response, "image_generator")
+        return f"{message}\n\nResult URL: {media_url}" if media_url else message
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# Create the minimal Gradio interface
+def create_minimal_interface():
+    """Create minimal interface with basic components only"""
+    
+    # Background Removal Interface
+    bg_interface = gr.Interface(
+        fn=process_remove_bg,
+        inputs=gr.Textbox(label="Image URL", placeholder="https://example.com/image.jpg"),
+        outputs=gr.Textbox(label="Result"),
+        title="🎭 Background Removal",
+        description="Remove background from images using AI"
+    )
+    
+    # Image Upscaler Interface  
+    upscale_interface = gr.Interface(
+        fn=process_upscale,
+        inputs=[
+            gr.Textbox(label="Image URL", placeholder="https://example.com/image.jpg"),
+            gr.Radio(choices=["2", "4", "8", "16"], value="2", label="Scale Factor")
+        ],
+        outputs=gr.Textbox(label="Result"),
+        title="🔍 Image Upscaler",
+        description="Upscale images using AI"
+    )
+    
+    # Image Generator Interface
+    gen_interface = gr.Interface(
+        fn=process_generate,
+        inputs=gr.Textbox(label="Text Prompt", placeholder="A beautiful sunset over mountains...", lines=3),
+        outputs=gr.Textbox(label="Result"),
+        title="🎨 Image Generator", 
+        description="Generate images using AI from text prompts"
+    )
+    
+    # Create tabbed interface
+    demo = gr.TabbedInterface(
+        [bg_interface, upscale_interface, gen_interface],
+        ["Background Removal", "Image Upscaler", "Image Generator"],
+        title="🤖 A1D MCP Server - Universal AI Tools"
+    )
+    
+    return demo
+
+if __name__ == "__main__":
+    # Check for API key
+    if not os.getenv("A1D_API_KEY"):
+        print("❌ Error: A1D_API_KEY environment variable is required")
+        print("Please set your API key in the Space settings")
+        exit(1)
+    
+    print("🚀 Starting A1D MCP Server (Minimal Version)...")
+    print("✅ API key found")
+    print("🌐 Server will be available at: http://0.0.0.0:7860")
+    
+    # Create and launch the minimal app
+    demo = create_minimal_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )

official_app.py

@@ -0,0 +1,206 @@
+#!/usr/bin/env python3
+"""
+A1D MCP Server with Gradio - Official MCP Implementation
+Following Gradio's official MCP documentation
+"""
+
+import gradio as gr
+import os
+from utils import A1DAPIClient, prepare_request_data, format_response_with_preview
+from config import TOOLS_CONFIG
+
+def get_api_client():
+    """Get API client with current API key"""
+    api_key = os.getenv("A1D_API_KEY")
+    if not api_key:
+        raise ValueError("A1D_API_KEY environment variable is required")
+    return A1DAPIClient(api_key)
+
+def remove_background(image_url: str) -> str:
+    """
+    Remove background from images using AI.
+
+    Args:
+        image_url (str): The URL of the image to remove background from. Must be a valid HTTP/HTTPS URL pointing to an image file.
+
+    Returns:
+        str: A message indicating the result and the URL of the processed image.
+    """
+    try:
+        if not image_url or not image_url.strip():
+            return "❌ Error: Please provide an image URL"
+        
+        client = get_api_client()
+        data = prepare_request_data("remove_bg", image_url=image_url)
+        response = client.make_request_with_result(
+            TOOLS_CONFIG["remove_bg"]["api_endpoint"],
+            data,
+            timeout=120
+        )
+        message, media_url = format_response_with_preview(response, "remove_bg")
+        return f"{message}\n\nResult URL: {media_url}" if media_url else message
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+def upscale_image(image_url: str, scale: str = "2") -> str:
+    """
+    Upscale images using AI.
+
+    Args:
+        image_url (str): The URL of the image to upscale. Must be a valid HTTP/HTTPS URL pointing to an image file.
+        scale (str): Scale factor for upscaling. Choose from "2", "4", "8", or "16". Higher values produce larger images but take longer to process.
+
+    Returns:
+        str: A message indicating the result and the URL of the processed image.
+    """
+    try:
+        if not image_url or not image_url.strip():
+            return "❌ Error: Please provide an image URL"
+        
+        client = get_api_client()
+        data = prepare_request_data("image_upscaler", image_url=image_url, scale=int(scale))
+        response = client.make_request_with_result(
+            TOOLS_CONFIG["image_upscaler"]["api_endpoint"],
+            data,
+            timeout=120
+        )
+        message, media_url = format_response_with_preview(response, "image_upscaler")
+        return f"{message}\n\nResult URL: {media_url}" if media_url else message
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+def generate_image(prompt: str) -> str:
+    """
+    Generate images using AI from text prompts.
+
+    Args:
+        prompt (str): Text description of the image to generate. Be descriptive and specific for better results. Example: "A beautiful sunset over mountains with vibrant orange and purple colors".
+
+    Returns:
+        str: A message indicating the result and the URL of the generated image.
+    """
+    try:
+        if not prompt or not prompt.strip():
+            return "❌ Error: Please provide a text prompt"
+        
+        client = get_api_client()
+        data = prepare_request_data("image_generator", prompt=prompt)
+        response = client.make_request_with_result(
+            TOOLS_CONFIG["image_generator"]["api_endpoint"],
+            data,
+            timeout=120
+        )
+        message, media_url = format_response_with_preview(response, "image_generator")
+        return f"{message}\n\nResult URL: {media_url}" if media_url else message
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+def vectorize_image(image_url: str) -> str:
+    """
+    Convert images to vector format using AI.
+
+    Args:
+        image_url (str): The URL of the image to convert to vector format. Must be a valid HTTP/HTTPS URL pointing to an image file.
+
+    Returns:
+        str: A message indicating the result and the URL of the vectorized image.
+    """
+    try:
+        if not image_url or not image_url.strip():
+            return "❌ Error: Please provide an image URL"
+        
+        client = get_api_client()
+        data = prepare_request_data("image_vectorization", image_url=image_url)
+        response = client.make_request_with_result(
+            TOOLS_CONFIG["image_vectorization"]["api_endpoint"],
+            data,
+            timeout=120
+        )
+        message, media_url = format_response_with_preview(response, "image_vectorization")
+        return f"{message}\n\nResult URL: {media_url}" if media_url else message
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+def extend_image(image_url: str) -> str:
+    """
+    Extend images using AI.
+
+    Args:
+        image_url (str): The URL of the image to extend. Must be a valid HTTP/HTTPS URL pointing to an image file.
+
+    Returns:
+        str: A message indicating the result and the URL of the extended image.
+    """
+    try:
+        if not image_url or not image_url.strip():
+            return "❌ Error: Please provide an image URL"
+        
+        client = get_api_client()
+        data = prepare_request_data("image_extends", image_url=image_url)
+        response = client.make_request_with_result(
+            TOOLS_CONFIG["image_extends"]["api_endpoint"],
+            data,
+            timeout=120
+        )
+        message, media_url = format_response_with_preview(response, "image_extends")
+        return f"{message}\n\nResult URL: {media_url}" if media_url else message
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+def upscale_video(video_url: str) -> str:
+    """
+    Upscale videos using AI.
+
+    Args:
+        video_url (str): The URL of the video to upscale. Must be a valid HTTP/HTTPS URL pointing to a video file (MP4, AVI, MOV, etc.).
+
+    Returns:
+        str: A message indicating the result and the URL of the upscaled video.
+    """
+    try:
+        if not video_url or not video_url.strip():
+            return "❌ Error: Please provide a video URL"
+        
+        client = get_api_client()
+        data = prepare_request_data("video_upscaler", video_url=video_url)
+        response = client.make_request_with_result(
+            TOOLS_CONFIG["video_upscaler"]["api_endpoint"],
+            data,
+            timeout=300
+        )
+        message, media_url = format_response_with_preview(response, "video_upscaler")
+        return f"{message}\n\nResult URL: {media_url}" if media_url else message
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# Create the Gradio interface using the official MCP approach
+demo = gr.Interface(
+    fn=remove_background,
+    inputs=[gr.Textbox(label="Image URL", placeholder="https://example.com/image.jpg", value="https://asset2.qieman.com/user-avatar/20240721/1071394_e15eb706-485d-4604-ad28-ac9830de1fe4.jpg")],
+    outputs=[gr.Textbox(label="Result")],
+    title="🤖 A1D MCP Server - Universal AI Tools",
+    description="Powerful AI image and video processing tools for any MCP-compatible client.",
+    examples=[
+        ["https://asset2.qieman.com/user-avatar/20240721/1071394_e15eb706-485d-4604-ad28-ac9830de1fe4.jpg"],
+        ["https://images.unsplash.com/photo-1506905925346-21bda4d32df4"]
+    ]
+)
+
+if __name__ == "__main__":
+    # Check for API key
+    if not os.getenv("A1D_API_KEY"):
+        print("❌ Error: A1D_API_KEY environment variable is required")
+        print("Please set your API key in the Space settings")
+        exit(1)
+    
+    print("🚀 Starting A1D MCP Server (Official Implementation)...")
+    print("✅ API key found")
+    print("🌐 MCP Server will be available at: /gradio_api/mcp/sse")
+    
+    # Launch with MCP server enabled (official method)
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        mcp_server=True  # This is the official way to enable MCP
+    )

requirements.txt

@@ -1,2 +1,2 @@
-gradio==4.32.0
+gradio[mcp]>=5.0.0
 requests>=2.31.0