hf host model

app.py

@@ -6,6 +6,7 @@ import os
 
 # Get model path (Windows compatible)
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+MODEL_ID = "dshi01/my-benthic-classifier"
 
 # Try different possible filenames
 possible_names = ['ConvNextmodel.pth', 'convnextmodel.pth', 'ConvNext_model.pth']
@@ -33,27 +34,29 @@ SPECIES_CATEGORIES = [
 ]
 
 # Load model
-print(f"Loading model from: {model_path}")
-model = AutoModelForImageClassification.from_pretrained(
-    'facebook/convnext-tiny-224',
-    num_labels=7,
-    ignore_mismatched_sizes=True
-)
+print(f"Loading model from: {MODEL_ID}")
+# model = AutoModelForImageClassification.from_pretrained(
+#     'facebook/convnext-tiny-224',
+#     num_labels=7,
+#     ignore_mismatched_sizes=True
+# )
+processor=AutoImageProcessor.from_pretrained(MODEL_ID)
+model = AutoModelForImageClassification.from_pretrained(MODEL_ID)
 
 # Load weights
-checkpoint = torch.load(model_path, map_location='cpu', weights_only=False)
-if isinstance(checkpoint, dict):
-    if 'model' in checkpoint:
-        checkpoint = checkpoint['model']
-    elif 'state_dict' in checkpoint:
-        checkpoint = checkpoint['state_dict']
+# checkpoint = torch.load(model_path, map_location='cpu', weights_only=False)
+# if isinstance(checkpoint, dict):
+#     if 'model' in checkpoint:
+#         checkpoint = checkpoint['model']
+#     elif 'state_dict' in checkpoint:
+#         checkpoint = checkpoint['state_dict']
 
-model.load_state_dict(checkpoint, strict=False)
-model.eval()
+# model.load_state_dict(checkpoint, strict=False)
+# model.eval()
 
 # Load processor
-processor = AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224')
-print("✓ Model loaded successfully!")
+# processor = AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224')
+# print("✓ Model loaded successfully!")
 
 def classify_image(image):
     """