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app.py

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+import albumentations
+import cv2
+import torch
+import timm
+import gradio as gr
+import numpy as np
+import os
+import random
+
+device = torch.device('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)  
+    with torch.no_grad():
+        output = model(image.unsqueeze(0))
+    out = output.sigmoid().detach().cpu().numpy().flatten()   
+    return out
+    
+    
+def predict(image=None) -> dict:  
+    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("/home/aswin/Downloads/paddy_model.pth", map_location=torch.device(device)))
+    model.to(device)
+
+    predicted = inference_fn(model, image)
+  
+    return {labels[i]: float(predicted[i]) for i in range(10)}
+    
+
+interface = gr.Interface(fn=predict, 
+             inputs=gr.inputs.Image(),
+             outputs=gr.outputs.Label(num_top_classes=10),
+             interpretation='default').launch()
+interface.launch()