fix app.py to remove share=True

app.py

@@ -1,48 +1,48 @@
-
-import gradio as gr
-import os
-import torch
-
-from model import create_effnetb2_model
-from timeit import default_timer as timer
-
-class_names = ["pizza", "steake", "sushi"]
-
-effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3)
-
-effnetb2.load_state_dict(
-    torch.load(f"09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20.pth",
-               map_location=torch.device("cpu"))
-)
-
-def predict(img) -> tuple[dict, float]:
-    start_time = timer()
-    img = effnetb2_transforms(img).unsqueeze(0)  # unsqueeze = add batch dimension on 0th dimension
-    effnetb2.eval()
-    with torch.inference_mode():
-        pred_probs = torch.softmax(effnetb2(img), dim=1)
-
-    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
-
-    pred_time = round(timer() - start_time, 4)
-
-    return pred_labels_and_probs, pred_time
-
-
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-
-title = "FoodVision Mini😊"
-description = "An [EffNetB2 feature extractor](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html) computer vision model to classify images as pizza, steak and sushi"
-article = "Create at [09. PyTorch Model Deployment](http://keivanjamali.com)."
-
-demo = gr.Interface(fn=predict,
-                    inputs=gr.Image(type="pil"),
-                    outputs=[gr.Label(num_top_classes=3, label="Predictions"),
-                             gr.Number(label="Prediction Time (s)")],
-                    examples=example_list,
-                    title=title,
-                    description=description,
-                    article=article)
-
-demo.launch(debug=False, share=True)
+
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+
+class_names = ["pizza", "steake", "sushi"]
+
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3)
+
+effnetb2.load_state_dict(
+    torch.load(f"09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20.pth",
+               map_location=torch.device("cpu"))
+)
+
+def predict(img) -> tuple[dict, float]:
+    start_time = timer()
+    img = effnetb2_transforms(img).unsqueeze(0)  # unsqueeze = add batch dimension on 0th dimension
+    effnetb2.eval()
+    with torch.inference_mode():
+        pred_probs = torch.softmax(effnetb2(img), dim=1)
+
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+    pred_time = round(timer() - start_time, 4)
+
+    return pred_labels_and_probs, pred_time
+
+
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+
+title = "FoodVision Mini😊"
+description = "An [EffNetB2 feature extractor](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html) computer vision model to classify images as pizza, steak and sushi"
+article = "Create at [09. PyTorch Model Deployment](http://keivanjamali.com)."
+
+demo = gr.Interface(fn=predict,
+                    inputs=gr.Image(type="pil"),
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"),
+                             gr.Number(label="Prediction Time (s)")],
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+demo.launch(debug=False) # Don't need share