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app.py

@@ -5,39 +5,39 @@ from PIL import Image
 import requests
 
 # Load the pre-trained ResNet model
-model = models.mobilenet_v2(pretrained=True)
-model.eval()
+# model = models.mobilenet_v2(pretrained=True)
+# model.eval()
 
-# Define the transformation for input images
-preprocess = transforms.Compose([
-    transforms.Resize(256),
-    transforms.CenterCrop(224),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-])
+# # Define the transformation for input images
+# preprocess = transforms.Compose([
+#     transforms.Resize(256),
+#     transforms.CenterCrop(224),
+#     transforms.ToTensor(),
+#     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+# ])
 
-# Define the labels for ImageNet classes (you may need to adjust this based on your model)
-LABELS_URL = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json"
-response = requests.get(LABELS_URL)
-labels = response.text.splitlines()
+# # Define the labels for ImageNet classes (you may need to adjust this based on your model)
+# LABELS_URL = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json"
+# response = requests.get(LABELS_URL)
+# labels = response.text.splitlines()
 
-# Function to perform image classification
-def classify_image(input_image):
-    # Preprocess the image
-    input_tensor = preprocess(input_image)
-    input_batch = input_tensor.unsqueeze(0)
+# # Function to perform image classification
+# def classify_image(input_image):
+#     # Preprocess the image
+#     input_tensor = preprocess(input_image)
+#     input_batch = input_tensor.unsqueeze(0)
 
-    # Make predictions
-    with torch.no_grad():
-        output = model(input_batch)
+#     # Make predictions
+#     with torch.no_grad():
+#         output = model(input_batch)
 
-    # Get the predicted class index
-    _, predicted_idx = torch.max(output, 1)
+#     # Get the predicted class index
+#     _, predicted_idx = torch.max(output, 1)
 
-    # Get the predicted label
-    predicted_label = labels[predicted_idx.item()]
+#     # Get the predicted label
+#     predicted_label = labels[predicted_idx.item()]
 
-    return predicted_label
+#     return predicted_label
 
 # Gradio UI components
 image_input = gr.Image()
@@ -45,7 +45,7 @@ output_label = gr.Textbox()
 
 # Gradio interface
 iface = gr.Interface(
-    fn=classify_image,
+    fn=None,
     inputs=image_input,
     outputs=output_label,
     live=True,