created new file

app.py

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+import albumentations
+import cv2
+import torch
+import timm
+import gradio as gr
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+labels = {0: 'bacterial_leaf_blight',
+ 1: 'bacterial_leaf_streak',
+ 2: 'bacterial_panicle_blight',
+ 3: 'blast',
+ 4: 'brown_spot',
+ 5: 'dead_heart',
+ 6: 'downy_mildew',
+ 7: 'hispa',
+ 8: 'normal',
+ 9: 'tungro'}
+ 
+ def inference_fn(model, image=None):  
+    model.eval()
+    image = image.to(device)  
+    print(image.shape)
+    with torch.no_grad():
+        output = model(image.unsqueeze(0))
+    out = output.sigmoid().detach().cpu().numpy().flatten()   
+    return out
+    
+    
+ def predict(image = None) :  
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    mean = (0.485, 0.456, 0.406) 
+    std = (0.229, 0.224, 0.225) 
+    
+    augmentations = albumentations.Compose(
+            [
+                albumentations.Resize(256, 256),
+                albumentations.HorizontalFlip(p=0.5),
+                albumentations.VerticalFlip(p=0.5),
+                albumentations.Normalize(mean, std, max_pixel_value=255.0, always_apply=True),
+            ]
+    )
+    
+    augmented = augmentations(image=image)
+    image = augmented["image"]
+    image = np.transpose(image, (2, 0, 1))
+    image = torch.tensor(image, dtype=torch.float32)
+    model = timm.create_model('efficientnet_b0', pretrained=False, num_classes=10)
+    model.load_state_dict(torch.load("paddy_model.pth"))
+    model.to(device)
+
+    predicted = inference_fn(model, image)
+    
+    del model
+    gc.collect()
+    torch.cuda.empty_cache()
+    
+    return {labels[i]: float(predicted[i]) for i in range(10)}
+    
+
+gr.Interface(fn=predict, 
+             inputs=gr.inputs.Image(shape=(256, 256)),
+             outputs=gr.outputs.Label(num_top_classes=10),
+             examples=["200001.jpg", "100028.jpg"]).launch()