Update app.py

app.py

@@ -6,18 +6,17 @@ 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 ---
+
+# --- Tool 1: The Main Analysis Function ---
 def analyze_image_authenticity(image_url: str) -> dict:
     """
     Analyzes an image from a URL to determine if it is real or AI-generated.
@@ -35,8 +34,7 @@ def analyze_image_authenticity(image_url: str) -> dict:
     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)}")
+        raise gr.Error(f"Could not load image from URL. Error: {str(e)}")
 
     labels = ["a real photograph", "a computer-generated image", "an illustration or drawing"]
     results = image_detector(image, candidate_labels=labels)
@@ -44,16 +42,39 @@ def analyze_image_authenticity(image_url: str) -> dict:
     print(f"Analysis successful. Results: {results}")
     return {"analysis_results": results}
 
+
+# --- Tool 2: The Validation Function for Sharing (NEW) ---
+def validate() -> str:
+    """
+    Validates the server ownership for sharing. Returns the owner's phone number.
+    
+    Returns:
+        The owner's phone number as a string.
+    """
+    return OWNER_PHONE_NUMBER
+
+
 # --- 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."
+# We now create a list of Interfaces to expose both tools
+demo = gr.TabbedInterface(
+    [
+        gr.Interface(
+            fn=analyze_image_authenticity,
+            inputs=[gr.Textbox(label="Image URL")],
+            outputs=[gr.JSON(label="Analysis Results")],
+            title="AI Image Authenticity Detector",
+            description="Tool to analyze an image's authenticity."
+        ),
+        gr.Interface(
+            fn=validate,
+            inputs=[],
+            outputs="text",
+            title="Validation Tool",
+            description="Used by the Puch AI platform to validate shared servers."
+        )
+    ],
+    ["Image Analyzer", "Validator"]
 )
 
 # --- 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)