updated the model

app.py

@@ -9,19 +9,8 @@ import random
 
 device = torch.device('cpu')
 
-def seed_everything(seed=42):
-    random.seed(seed)
-    os.environ['PYTHONHASHSEED'] = str(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = True
-    
-seed_everything(seed=42)
-
-
-labels = {0: 'bacterial_leaf_blight',
+labels = {
+ 0: 'bacterial_leaf_blight',
  1: 'bacterial_leaf_streak',
  2: 'bacterial_panicle_blight',
  3: 'blast',
@@ -30,7 +19,8 @@ labels = {0: 'bacterial_leaf_blight',
  6: 'downy_mildew',
  7: 'hispa',
  8: 'normal',
- 9: 'tungro'}
+ 9: 'tungro'
+ }
  
 def inference_fn(model, image=None):  
     model.eval()
@@ -41,8 +31,7 @@ def inference_fn(model, image=None):
     return out
     
     
-def predict(image = None) :  
-    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+def predict(image=None) -> dict:  
     mean = (0.485, 0.456, 0.406) 
     std = (0.229, 0.224, 0.225) 
     
@@ -71,4 +60,4 @@ def predict(image = None) :
 gr.Interface(fn=predict, 
              inputs=gr.inputs.Image(),
              outputs=gr.outputs.Label(num_top_classes=10),
-             examples=["200005.jpg", "200006.jpg"]).launch()
+             examples=["200005.jpg", "200006.jpg"], interpretation='default', capture_session=True).launch(share=True)