update app.py

app.py

@@ -7,12 +7,10 @@ from torchvision import transforms
 from timeit import default_timer as timer
 from typing import Tuple, Dict
 
-# Setup class names
-class_names = ["Smiling", "Not Smiling",]
 
-### 2. Model and transforms preparation ###
+###  Model and transforms preparation ###
 
-model = torch.load(f="smile_classifier/smile_classifier.pth")
+model = torch.load(f="smile_classifier.pth")
 
 transform = transforms.Compose([
     transforms.CenterCrop(size=[178, 178]),
@@ -20,7 +18,7 @@ transform = transforms.Compose([
     transforms.ToTensor()
 ])
 
-### 3. Predict function ###
+### Predict function ###
 
 # Create predict function
 def predict(img) -> Tuple[Dict, float]:
@@ -50,7 +48,7 @@ def predict(img) -> Tuple[Dict, float]:
     # Return the prediction dictionary and prediction time 
     return pred_labels_and_probs, pred_time
 
-### 4. Gradio app ###
+### Gradio app ###
 
 # Create title, description and article strings
 title = "Smile Classifier 🙂😊😃"
@@ -59,7 +57,7 @@ article = "Please select an image from provided examples and submit, the model w
 is smiling or not and will also provide prediction probabilities."
 
 # Create examples list from "examples/" directory
-example_list = [["smile_classifier/examples/" + example] for example in os.listdir("smile_classifier/examples")]
+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