Update app.py

app.py

@@ -1,33 +1,82 @@
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+### 1. Imports and class names setup ### 
 import gradio as gr
+import os
 import torch
-import torchvision
+
 from model import create_model
 from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+class_names = ["pizza", "steak", "sushi"]
+
+### 2. Model and transforms preparation ###
 
-model,transform=create_model()
-model=model.to("cpu")
-model.load_state_dict(
+# Create EffNetB2 model
+effnetb2, effnetb2_transforms = create_model(
+    num_classes=3, # len(class_names) would also work
+)
+
+# Load saved weights
+effnetb2.load_state_dict(
     torch.load(
-        f="deneme_modeli.pth",
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
         map_location=torch.device("cpu"),  # load to CPU
     )
 )
 
-class_names = ["pizza","steak","sushi"]
-def predict(image):
-    start=timer()
-    image=transform(image).unsqueeze(0).to("cpu")
-    output=model(image)
-    preds={class_names[i]:torch.softmax(output,dim=1)[0][i].item() for i in range(len(class_names))}
-    time=timer()-start
-    return preds,round(time,3)
-
-inputs = gr.Image(type="pil", label = "Resim")
-outputs = [gr.Label(num_top_classes=3,label="Tahminler"),gr.Number(label="Süre")]
-demo=gr.Interface(fn = predict,
-                 inputs=inputs,
-                 outputs=outputs,
-                 examples=[["examples/673127.jpg"],["examples/690177.jpg"]],
-                 title="Yeni Model")
+### 3. Predict function ###
+
+# Create predict function
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+    start_time = timer()
+    
+    # Transform the target image and add a batch dimension
+    img = effnetb2_transforms(img).unsqueeze(0)
+    
+    # Put model into evaluation mode and turn on inference mode
+    effnetb2.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(effnetb2(img), dim=1)
+    
+    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+    
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+    
+    # Return the prediction dictionary and prediction time 
+    return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+
+# Create title, description and article strings
+title = "FoodVision Mini 🍕🥩🍣"
+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/)."
+
+# 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()