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app.py

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+import gradio as gr
+import requests
+from PIL import Image
+from transformers import pipeline, Pipeline
+import os
+from dotenv import load_dotenv
+
+# --- Configuration ---
+# Load secrets from the Space's "Repository secrets" settings
+load_dotenv()
+VALID_BEARER_TOKEN = os.getenv("VALID_BEARER_TOKEN")
+OWNER_PHONE_NUMBER = os.getenv("OWNER_PHONE_NUMBER")
+
+# --- AI Model Setup ---
+# This is loaded once when the Space starts
+print("Loading AI Image Detection model...")
+image_detector: Pipeline = pipeline("image-classification", model="openai/clip-vit-base-patch32")
+print("✅ Model loaded successfully.")
+
+# --- Main Tool Function ---
+def analyze_image_authenticity(image_url: str) -> dict:
+    """
+    Analyzes an image from a URL to determine if it is real or AI-generated.
+    
+    Args:
+        image_url: The URL of the image to analyze.
+        
+    Returns:
+        A dictionary with the analysis results and probability scores.
+    """
+    if not image_url:
+        raise gr.Error("Image URL parameter is missing.")
+    
+    print(f"Analyzing image from URL: {image_url}")
+    try:
+        image = Image.open(requests.get(image_url, stream=True, timeout=10).raw)
+    except Exception as e:
+        # For Gradio, it's better to raise a gr.Error for user-facing issues
+        raise gr.Error(f"Could not load image from URL. It might be invalid or inaccessible. Error: {str(e)}")
+
+    labels = ["a real photograph", "a computer-generated image", "an illustration or drawing"]
+    results = image_detector(image, candidate_labels=labels)
+    
+    print(f"Analysis successful. Results: {results}")
+    return {"analysis_results": results}
+
+# --- Gradio Interface ---
+# This defines the UI and the MCP endpoint
+demo = gr.Interface(
+    fn=analyze_image_authenticity,
+    inputs=[gr.Textbox(label="Image URL")],
+    outputs=[gr.JSON(label="Analysis Results")],
+    title="AI Image Authenticity Detector",
+    description="Provide an image URL to determine if it is a real photograph or AI-generated."
+)
+
+# --- Launch the App and MCP Server ---
+# mcp_server=True is the magic parameter that exposes your function as an MCP tool
+demo.launch(mcp_server=True)

requirements.txt

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+gradio[mcp]
+requests
+Pillow
+torch
+torchvision
+transformers
+python-dotenv