Update app.py

app.py

@@ -1,4 +1,4 @@
-"""import torch
+import torch
 from PIL import Image
 from torchvision.transforms import v2
 import torchvision.models as models
@@ -63,57 +63,5 @@ gradio_app = gr.Interface(
 
 if __name__ == "__main__":
     gradio_app.launch()
-"""
-
-import torch
-from PIL import Image
-from torchvision.transforms import v2
-import torchvision.models as models
-import gradio as gr
-
-# Load the pre-trained model and set it up (as in your original code)
-model = models.resnet18()
-num_ftrs = model.fc.in_features
-model.fc = torch.nn.Linear(num_ftrs, 73)
-model.load_state_dict(torch.load('Dogrun2.pth', map_location=torch.device('cpu')))
-model.eval()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-# Define image transformations
-transforms_test = v2.Compose([
-    v2.Resize((224, 224), antialias=True),
-    v2.CenterCrop((224, 224)),
-    v2.ToImage(),
-    v2.ToDtype(torch.float32, scale=True),
-    v2.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-])
 
-labels = [...]  # Your list of dog breeds
 
-def predict(input_img):
-    transformed_img = transforms_test(input_img)
-    transformed_img = transformed_img.unsqueeze(0).to(device)
-    logits = model(transformed_img)
-    output_softmax = torch.nn.functional.softmax(logits, dim=1)
-    topk_values, topk_indices = torch.topk(output_softmax, 3)
-    
-    topk_indices = topk_indices.tolist()[0]
-    topk_labels = [labels[index] for index in topk_indices]
-    topk_probs = topk_values[0].tolist()
-    
-    result_lines = [f"**{label}**: {prob*100:.2f}%" for label, prob in zip(topk_labels, topk_probs)]
-    result = "\n".join(result_lines)
-    
-    return result
-
-# Create Gradio interface
-gradio_app = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(label="Please select a clear image of your good dog to upload.", sources=['upload'], type="pil"),
-    outputs=gr.Markdown(label="**Predicted Breeds:**"),
-    title="What is Your Dog Breed?",
-)
-
-if __name__ == "__main__":
-    gradio_app.launch()