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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a87d9e104a39a3abdfae52a5351f17ec9c6cd2ed3e5ac3d632d8b4cff37b19ff
+size 31313869

app.py

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+### 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 
+
+# Set up class names 
+class_names = ['pizza', 'steak', 'sushi']
+
+### 2. Model and transforms preparation ### 
+
+# Create EffNetB2 model 
+effnetb2, effnetb2_transforms = create_effnetb2_model( 
+    num_classes=3
+)
+
+# Load saved weights 
+effnetb2.load_state_dict( 
+    torch.load( 
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth", 
+        map_location=torch.device("cpu") # load to CPU 
+    )
+)
+
+### 3. Predict function ### 
+
+# Create predict function
+def predict(img) -> Tuple[Dict, float]: 
+  """
+  Transforms and peforms a prediction on img and retunrs predictions
+  """
+
+  # Start the timer 
+  start_time = timer()
+
+  # Transform the target image and add a batch dimension
+  img = effnetb2_transforms(img).unsqueeze(0)
+
+  # put the 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 predictoin probability dictionary Gradio interface
+  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 pizze, 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'), 
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), 
+                             gr.Number(label="Prediction time (s)")], 
+                    examples=example_list, 
+                    title=title,
+                    description=description, 
+                    article=article 
+                    )
+
+# Launch the demo 
+demo.launch()

model.py

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+import torch 
+import torchvision 
+
+from torch import nn 
+
+def create_effnetb2_model(num_classes: int=3, 
+                          seed: int=42): 
+
+  """
+
+  """
+
+  # Create EffNetB2 pretrained weights, transforms and model 
+  weights =  torchvision.models.EfficientNet_B2_Weights.DEFAULT 
+  transforms = weights.transforms()
+  model = torchvision.models.efficientnet_b2(weights=weights)
+
+  # Freeze all layers in the base model 
+  for param in model.parameters(): 
+    param.requires_grad = False 
+
+  # Change classifier head with random seed for reproducibility 
+  torch.manual_seed(seed) 
+  model.classifier = nn.Sequential( 
+      nn.Dropout(p=0.3, inplace=True),
+      nn.Linear(in_features=1408, out_features=num_classes)
+  )
+
+  return model, transforms

requirements.txt

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+torch==1.12.0 
+torchvision==0.13.0 
+gradio==3.14.0