Update app.py

app.py

@@ -3,6 +3,7 @@ import os
 import torch
 from model import create_effnetb2_model
 from timeit import default_timer as timer
+from PIL import Image
 from typing import Tuple, Dict
 
 class_names = ['pizza', 'steak', 'sushi']
@@ -14,14 +15,16 @@ effnetb2.load_state_dict(
   )
 )
 
-def predict(img):
+### Prediction function: EffNetB2 ###
+
+def predict(img: Image.Image) -> Tuple[Dict[str, float], float]:
   # Start a timer
   start_time = timer()
-  # Transform the input image for use wit EffNetB2
+  # Transform the input image for use with EffNetB2
   img = effnetb2_transforms(img).unsqueeze(0)
   # Put model into eval mode, make prediction
   effnetb2.eval()
-  with torch.inference_mode():
+  with torch.no_grad():
     pred_probs = torch.softmax(effnetb2(img), dim=1)
   # Create a prediction labal and prediction probability dictionary
   pred_labels_and_probs = {class_names[i]:float(pred_probs[0][i]) for i in range(len(class_names))}
@@ -32,20 +35,26 @@ def predict(img):
   return pred_labels_and_probs, pred_time
 
 
-title = 'FoodVision Mini 🍕🥩🍣'
+### Gradio app ###
+
+# Create title, description and article strings
+title = "FoodVision Mini 🍕🥩🍣"
 description = 'An [EfficientNetB2 feature extractor](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html)'
-article = 'Created with Pytorch model deployment'
-example_list = [["./examples/" + file] for file in os.listdir("./examples")]
-
-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=False)
+article = 'Created by Arik Kodenzov with 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
+                    # Create examples list from "examples/" directory
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch()