Update app.py

app.py

@@ -1,9 +1,9 @@
+%%writefile demos/foodvision_mini/app.py
 ### 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 Tuple, Dict
@@ -21,7 +21,7 @@ effnetb2, effnetb2_transforms = create_effnetb2_model(
 # Load saved weights
 effnetb2.load_state_dict(
     torch.load(
-        f="food_model.pth",
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
         map_location=torch.device("cpu"),  # load to CPU
     )
 )
@@ -55,22 +55,24 @@ def predict(img) -> Tuple[Dict, float]:
 
 ### 4. Gradio app ###
 
-# import gradio as gr
-# example_list = [["examples/" + example] for example in os.listdir("examples")]
 # Create title, description and article strings
-title = "Shukurullo FoodVision Mini 🍕🥩🍣"
+title = "FoodVision Mini 🍕🥩🍣"
 description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
-article = "Created by Shukurullo Meliboev at [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")]
 
 # Create the Gradio demo
 demo = gr.Interface(fn=predict, # mapping function from input to output
                     inputs=gr.Image(type="pil"), # what are the inputs?
                     outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
                              gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
-                    # examples=example_list, 
+                    # Create examples list from "examples/" directory
+                    examples=example_list, 
                     title=title,
                     description=description,
                     article=article)
 
 # Launch the demo!
-demo.launch(debug=False)
+demo.launch()