fixes

app.py

@@ -20,7 +20,7 @@ effnetb2, effnetb2_transforms = create_effnetb2_model(
 # Load saved weights
 effnetb2.load_state_dict(
     torch.load(
-        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        f="pretrained_effnetb2.pth",
         map_location=torch.device("cpu"),  # load to CPU
     )
 )
@@ -55,9 +55,9 @@ def predict(img) -> Tuple[Dict, float]:
 ### 4. Gradio app ###
 
 # Create title, description and article strings
-title = "FoodVision Mini 🍕🥩🍣"
+title = "Food Classifier for 🍕🥩🍣"
 description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
-article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+# article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
 
 # Create examples list from "examples/" directory
 example_list = [["examples/" + example] for example in os.listdir("examples")]
@@ -71,7 +71,8 @@ demo = gr.Interface(fn=predict, # mapping function from input to output
                     examples=example_list, 
                     title=title,
                     description=description,
-                    article=article)
+                    # article=article
+                    )
 
 # Launch the demo!
 demo.launch()