Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+import os
+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
+MODEL_FILENAME = "best.pt"
+
+print(f"Attempting to download {MODEL_FILENAME} from {MODEL_REPO}...")
+
+try:
+    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
+    )
+    print(f"Model downloaded to: {model_path}")
+    model = YOLO(model_path)
+except Exception as e:
+    print(f"CRITICAL ERROR loading model: {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.
+    
+    Args:
+        image: The image to analyze (uploaded file).
+        conf_threshold: Confidence threshold for detection (default 0.25).
+    
+    Returns:
+        A tuple containing the annotated image with bounding boxes 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."}
+
+    try:
+        # Run Inference
+        results = model.predict(
+            source=image,
+            conf=conf_threshold,
+            iou=0.45,
+            imgsz=640,
+            verbose=False,
+            device='cpu', # Spaces usually run on CPU unless you pay for GPU
+            max_det=300
+        )
+        
+        # 1. Generate Visual Output
+        # plot() returns BGR numpy array, convert to RGB PIL
+        annotated_array = results[0].plot() 
+        annotated_image = Image.fromarray(annotated_array[..., ::-1])
+        
+        # 2. Generate Data Output (JSON)
+        detections = []
+        gardiner_counts = {}
+        
+        for box in results[0].boxes:
+            if box.cls.numel() > 0:
+                cls_id = int(box.cls[0])
+                if 0 <= cls_id < len(model.names):
+                    code = model.names[cls_id]
+                    conf = float(box.conf[0])
+                    
+                    if code not in gardiner_counts: gardiner_counts[code] = 0
+                    gardiner_counts[code] += 1
+                    
+                    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),
+            "unique_symbols": list(gardiner_counts.keys()),
+            "counts": gardiner_counts
+        }
+        
+        return annotated_image, summary
+
+    except Exception as e:
+        print(f"Error during inference: {e}")
+        return None, {"error": str(e)}
+
+# --- 3. INTERFACE & SERVER ---
+# mcp_server=True creates the endpoint automatically
+demo = gr.Interface(
+    fn=detect_hieroglyphs,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Number(value=0.25, label="Confidence Threshold")
+    ],
+    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."
+)
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)