Update app.py

app.py

@@ -6,42 +6,45 @@ from huggingface_hub import hf_hub_download
 import numpy as np
 
 # --- 1. SETUP & MODEL LOADING ---
-# We download the model securely at startup
-MODEL_REPO = "youkii-xr/hieroglyphic-detection" # <--- REPLACE THIS
+# Replace with your actual private repo ID
+MODEL_REPO = "youkii-xr/hieroglyphic-detection"
 MODEL_FILENAME = "best.pt"
 
-print(f"Attempting to download {MODEL_FILENAME} from {MODEL_REPO}...")
+print(f"Server Status: Public MCP Endpoint Active")
+print(f"Security: Model weights are protected (private repo)")
 
 try:
+    # 🔒 SECURE DOWNLOAD: 
+    # This uses the 'HF_TOKEN' Secret from Space Settings to authenticate.
+    # Users of the Space CANNOT see this token or the downloaded file.
     model_path = hf_hub_download(
         repo_id=MODEL_REPO,
         filename=MODEL_FILENAME,
-        token=os.environ.get("HF_TOKEN") # Needs 'HF_TOKEN' secret in Space settings
+        token=os.environ.get("HF_TOKEN")
     )
-    print(f"Model downloaded to: {model_path}")
+    print(f"System: Model loaded successfully from private storage.")
     model = YOLO(model_path)
 except Exception as e:
-    print(f"CRITICAL ERROR loading model: {e}")
+    print(f"CRITICAL ERROR: Could not load model. Check HF_TOKEN in Settings. {e}")
     model = None
 
 # --- 2. DETECTION LOGIC ---
-# NOTE: Type hints (image: Image.Image) and Docstrings are MANDATORY for MCP
 def detect_hieroglyphs(image: Image.Image, conf_threshold: float = 0.25):
     """
-    Detects Egyptian hieroglyph symbols in an image.
+    Analyzes an image to find Egyptian hieroglyphs.
     
     Args:
-        image: The image to analyze (uploaded file).
-        conf_threshold: Confidence threshold for detection (default 0.25).
+        image: The image to analyze.
+        conf_threshold: Confidence level (0.1 to 1.0). Default is 0.25.
     
     Returns:
-        A tuple containing the annotated image with bounding boxes and a JSON summary of findings.
+        A tuple containing the annotated image and a JSON summary of findings.
     """
     if image is None:
         return None, {"error": "No image provided"}
     
     if model is None:
-        return None, {"error": "Model failed to load on server."}
+        return None, {"error": "Server Error: Model not loaded."}
 
     try:
         # Run Inference
@@ -51,12 +54,11 @@ def detect_hieroglyphs(image: Image.Image, conf_threshold: float = 0.25):
             iou=0.45,
             imgsz=640,
             verbose=False,
-            device='cpu', # Spaces usually run on CPU unless you pay for GPU
+            device='cpu',
             max_det=300
         )
         
-        # 1. Generate Visual Output
-        # plot() returns BGR numpy array, convert to RGB PIL
+        # 1. Generate Visual Output (RGB Image)
         annotated_array = results[0].plot() 
         annotated_image = Image.fromarray(annotated_array[..., ::-1])
         
@@ -77,13 +79,12 @@ def detect_hieroglyphs(image: Image.Image, conf_threshold: float = 0.25):
                     detections.append({
                         "code": code,
                         "confidence": round(conf, 2),
-                        # Convert bbox to list for JSON serialization
                         "box": [round(x, 1) for x in box.xyxy[0].cpu().numpy().tolist()]
                     })
 
         summary = {
             "status": "success",
-            "total_detected": len(detections),
+            "total_found": len(detections),
             "unique_symbols": list(gardiner_counts.keys()),
             "counts": gardiner_counts
         }
@@ -91,24 +92,30 @@ def detect_hieroglyphs(image: Image.Image, conf_threshold: float = 0.25):
         return annotated_image, summary
 
     except Exception as e:
-        print(f"Error during inference: {e}")
+        print(f"Inference Error: {e}")
         return None, {"error": str(e)}
 
-# --- 3. INTERFACE & SERVER ---
-# mcp_server=True creates the endpoint automatically
+# --- 3. INTERFACE ---
 demo = gr.Interface(
     fn=detect_hieroglyphs,
     inputs=[
         gr.Image(type="pil", label="Upload Image"),
-        gr.Number(value=0.25, label="Confidence Threshold")
+        gr.Number(value=0.25, label="Confidence")
     ],
     outputs=[
         gr.Image(label="Annotated Result"),
         gr.JSON(label="Detection Data")
     ],
     title="Egyptian Hieroglyph MCP Server",
-    description="MCP-compatible server for Hieroglyph Detection. Connect this to Claude Desktop."
+    description="Public MCP Endpoint for Hieroglyph Detection. (Model Weights are Private)"
 )
 
 if __name__ == "__main__":
-    demo.launch(mcp_server=True)
+    # Settings to ensure Public access works without 403 errors:
+    # ssr_mode=False: Disables Server-Side Rendering (helps with API proxies)
+    # allowed_paths: Grants permission to read temp files uploaded by MCP
+    demo.launch(
+        mcp_server=True, 
+        ssr_mode=False, 
+        allowed_paths=["/tmp"]
+    )