Update app.py

app.py

@@ -1,54 +1,54 @@
-### 1 Imports and class names setup###
-import gradio as gr
-import os 
-import torch
-from model import create_effnetb2_model
-from timeit import default_timer as timer
-from typing import List, Dict
-
-class_names = ["pizza", "steak", "sushi"]
-
-### 2 model and transform preparation###
-effnetb2, transforms = create_effnetb2_model(num_classes=len(class_names))
-effnetb2_loaded.load_state_dict("11-model_deployment_effnetb2.pth",map_location="cpu")
-
-
-### 3 we need a predict function###
-def predict(img) -> Tuple[Dict,float]:
-    #start a timer
-    start_time = timer()
-
-    # transform the image
-    transformed_image = effnet_transform(img).unsqueeze(0)
-    
-    # putting the model in eval mode and make the prediction  
-    effnetb2_loaded.eval()
-    with torch.inference_mode():
-        logit = effnetb2_loaded(transformed_image)
-
-        probs = torch.softmax(logit, dim=1)
-        # Create a prediction label and prediction probability dictionary
-        pred_label_dict ={class_names[i] : probs[0][i].item() for i in range(len(class_names))}
-
-    # calculate the pred time 
-    end_time = timer()
-    inference_time = round(end_time - start_time, 4)
-    # return the label dict and inference time
-    return pred_label_dict, inference_time
-###Grad###
-title = "FoodVision mini models 🍕,🥩,🍣"
-description = "An EfficientnetB2 feature extraction model is used to classifay images as pizza, steak, sushi"
-
-example_list =[["example/"+example] for example in os.listdir("example")]
-# create a gradio demo
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs=[gr.Label(num_top_classes = 3,label= "prediction"),
-             gr.Number(label=" Prediction time in second")],
-    examples=example_list[0],
-    title=title,
-    description=description
-)
-demo.launch(share= False)
-
+### 1 Imports and class names setup###
+import gradio as gr
+import os 
+import torch
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import List, Dict
+
+class_names = ["pizza", "steak", "sushi"]
+
+### 2 model and transform preparation###
+effnetb2_loaded, transforms = create_effnetb2_model(num_classes=len(class_names))
+effnetb2_loaded.load_state_dict("11-model_deployment_effnetb2.pth",map_location="cpu")
+
+
+### 3 we need a predict function###
+def predict(img) -> Tuple[Dict,float]:
+    #start a timer
+    start_time = timer()
+
+    # transform the image
+    transformed_image = effnet_transform(img).unsqueeze(0)
+    
+    # putting the model in eval mode and make the prediction  
+    effnetb2_loaded.eval()
+    with torch.inference_mode():
+        logit = effnetb2_loaded(transformed_image)
+
+        probs = torch.softmax(logit, dim=1)
+        # Create a prediction label and prediction probability dictionary
+        pred_label_dict ={class_names[i] : probs[0][i].item() for i in range(len(class_names))}
+
+    # calculate the pred time 
+    end_time = timer()
+    inference_time = round(end_time - start_time, 4)
+    # return the label dict and inference time
+    return pred_label_dict, inference_time
+###Grad###
+title = "FoodVision mini models 🍕,🥩,🍣"
+description = "An EfficientnetB2 feature extraction model is used to classifay images as pizza, steak, sushi"
+
+example_list =[["example/"+example] for example in os.listdir("example")]
+# create a gradio demo
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Label(num_top_classes = 3,label= "prediction"),
+             gr.Number(label=" Prediction time in second")],
+    examples=example_list[0],
+    title=title,
+    description=description
+)
+demo.launch(share= False)
+