Update app.py

app.py

@@ -1,28 +1,62 @@
-import cv2
-import numpy as np
-import gradio as gr
-from ultralytics import YOLO
-
-# Load trained model
-model = YOLO("E:\\job related docs\\thegame\\yolov8\\yolov8_model.pt")  # or "yolov8s.onnx"
-
-def detect_defects(image):
-    # Run inference
-    results = model(image)
-    # Plot results
-    annotated_img = results[0].plot(line_width=2)
-    return annotated_img
-
-# Gradio UI
-title = "🔍 Steel Surface Defect Detector (YOLOv8)"
-description = "Upload a steel surface image to detect defects (crazing, scratches, etc.)."
-examples = ["defect_example1.jpg", "defect_example2.jpg"]
-
-gr.Interface(
-    fn=detect_defects,
-    inputs=gr.Image(type="numpy"),
-    outputs=gr.Image(type="numpy"),
-    title=title,
-    description=description,
-    examples=examples,
-).launch()
+import os
+import cv2
+import gradio as gr
+from ultralytics import YOLO
+
+# --- Fix 1: Unzip model file (if needed) ---
+# Hugging Face Spaces will automatically unzip uploaded .zip files,
+# so you can skip the !unzip command in Python code.
+
+# --- Fix 2: Use correct model path ---
+# Place your model file in the same directory as app.py and use relative path
+MODEL_PATH = "yolov8_model.pt"  # Make sure this matches your uploaded filename
+
+# --- Fix 3: Add error handling ---
+if not os.path.exists(MODEL_PATH):
+    raise FileNotFoundError(f"Model file not found at {MODEL_PATH}. "
+                          "Please upload your model file to the Space.")
+
+# Load model
+model = YOLO(MODEL_PATH)
+
+def detect_defects(image):
+    """Run object detection on input image and return annotated result."""
+    try:
+        # Run inference
+        results = model(image)
+        
+        # Plot results (with bounding boxes)
+        annotated_img = results[0].plot(line_width=2)
+        
+        # Convert BGR to RGB (OpenCV uses BGR by default)
+        annotated_img = cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB)
+        return annotated_img
+    
+    except Exception as e:
+        print(f"Error during detection: {e}")
+        return image  # Return original image if error occurs
+
+# Gradio UI
+title = "🔍 Steel Surface Defect Detector (YOLOv8)"
+description = "Upload a steel surface image to detect defects (crazing, scratches, etc.)."
+
+# --- Fix 4: Use relative paths for example images ---
+examples = [
+    "example1.jpg",  # Make sure these exist in your Space
+    "example2.jpg"
+]
+
+# Create interface
+interface = gr.Interface(
+    fn=detect_defects,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Image(type="numpy"),
+    title=title,
+    description=description,
+    examples=examples,
+    allow_flagging="never"
+)
+
+# Launch app
+if __name__ == "__main__":
+    interface.launch(debug=True)