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app.py

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+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
+
+class_names = ["pizza", "steak", "sushi"]
+
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=3
+)
+
+effnetb2.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location=torch.device("cpu")
+    )
+)
+
+
+def predict(img) -> Tuple[Dict, float]:
+  """Transforms and performs a prediction on img and returns prediction and time taken."""
+  start_time = timer()
+  img = effnetb2_transforms(img).unsqueeze(0)
+
+  effnetb2.eval()
+  with torch.inference_mode():
+    pred_probs = torch.softmax(effnetb2(img), dim=1)
+
+  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+  pred_time = round(timer() - start_time, 5)
+
+  return pred_labels_and_probs, pred_time
+
+
+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/)."
+
+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(share=True)

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):
+  """Creates an EfficientNetB2 feature extractor model and transforms.
+
+  Args:
+    num_classes (int, optional): number of classes in the classifier head.
+      Defaults to 3.
+    seed (int, optional): random seed value. Defaults to 42.
+  """
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transforms = weights.transforms()
+  model = torchvision.models.efficientnet_b2(weights=weights)
+
+  for param in model.parameters():
+    param.requires_grad = False
+
+  torch.manual_seed(seed)
+  model.classifier = nn.Sequential(
+      nn.Dropout(p=0.2, 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>=3.1.4