Upload 7 files

app.py

@@ -19,7 +19,7 @@ effnetb2.load_state_dict(
         f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
         map_location=torch.device("cpu") # load the model to the CPU
     )
-)  
+)
 ### Predict function
 
 def predict(img) -> Tuple[Dict, float]:
@@ -47,7 +47,7 @@ def predict(img) -> Tuple[Dict, float]:
 
 ## 4. Gradio app
 
-# Create title, description and article. 
+# Create title, description and article.
 title = "FoodVision Mini 🍕🥩🍣"
 description = "An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images as pizza, steak or sushi."
 article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/#74-building-a-gradio-interface)."
@@ -56,14 +56,14 @@ article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io
 example_list = [["examples/" + example] for exmple in os.listdir("examples")]
 
 # Create the Gradio demo
-demo = gr.Interface(fn=predict, # maps inputs to outputs
-                    inputs=gr.Image(type="pil"),
+demo = gr.Interface(fn=predict,  # maps inputs to outputs
+                    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)
-                    
+                                    examples=example_list,
+                                    title-title,
+                                    description=description,
+                                    article=article)
+
 # Launch the demo!
 demo.launch()