Update app.py

app.py

@@ -70,34 +70,16 @@ def load_image_model():
     import tensorflow as tf
     import requests
 
-    model_path = "/tmp/image_detector_v2.h5"
-
-    # Always re-download (remove old corrupted file)
-    if os.path.exists(model_path):
-        os.remove(model_path)
-
-    url = "https://huggingface.co/Muniba930/image-detector/resolve/main/image_detector_v2.h5?download=true"
-    
-    with requests.get(url, stream=True, allow_redirects=True) as r:
-        r.raise_for_status()
-        
-        # Debug: show what we're getting
-        content_type = r.headers.get("Content-Type", "unknown")
-        content_length = r.headers.get("Content-Length", "unknown")
-        st.write(f"Debug → Content-Type: {content_type}, Size: {content_length} bytes")
-        
-        with open(model_path, "wb") as f:
-            for chunk in r.iter_content(chunk_size=8192):
-                f.write(chunk)
-
-    size_mb = os.path.getsize(model_path) / (1024 * 1024)
-    st.write(f"Debug → Downloaded file size: {size_mb:.2f} MB")
-
-    # Check first bytes to verify it's a real HDF5 file
-    with open(model_path, "rb") as f:
-        header = f.read(8)
-    st.write(f"Debug → File header bytes: {header.hex()}")
-    # Real HDF5 file starts with: 894844460d0a1a0a
+    # Use .keras extension so TF knows it's zip format
+    model_path = "/tmp/image_detector_v2.keras"
+
+    if not os.path.exists(model_path):
+        url = "https://huggingface.co/Muniba930/image-detector/resolve/main/image_detector_v2.h5?download=true"
+        with requests.get(url, stream=True, allow_redirects=True) as r:
+            r.raise_for_status()
+            with open(model_path, "wb") as f:  # save as .keras not .h5
+                for chunk in r.iter_content(chunk_size=8192):
+                    f.write(chunk)
 
     model = tf.keras.models.load_model(model_path)
     return model