Upload app.py

app.py

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+### 1. Imports and class names app ###
+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
+
+# Setup class names
+with open('class_names.txt', 'r') as f:
+    class_names = [food_name.strip() for food_name in f.readlines()]
+
+### 2. Model and transforms preparation ###
+# Create model and transforms
+effnetb2, effnetb2_transforms = create_effnetb2_model()
+
+# Load saved weight
+effnetb2.load_state_dict(torch.load(f='09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth',
+                                    map_location=torch.device('cpu')))  # load to cpu
+
+### 3. Predict function
+def predict(img) -> Tuple[Dict, float]:
+    # Start a timer
+    start_time = timer()
+
+    # Transform the input image for use with EffNetB2
+    transformed_img = effnetb2_transforms(img).unsqueeze(dim=0)  # unsqueeze = add batch dimension on 0th
+
+    # Put model into eval mode, make prediction
+    with torch.inference_mode():
+        effnetb2.eval()
+        
+        # Pass the transformed image through the model and turn the prediction logits into probabilities
+        pred_prob = effnetb2(transformed_img).softmax(dim=1)
+
+    # Create a prediction label and prediction probability dictionary
+    pred_labels_and_probs = {class_names[i]: pred_prob[0][i].item() for i in range(len(class_names))}
+
+    # Calcualte pred time
+    end_time = timer()
+    inference_time = round(end_time - start_time, 4)
+
+    # Return pred dict and pred time
+    return pred_labels_and_probs, inference_time
+
+### 4. Gradio app ###
+# Create title, description and aritcle
+title = 'FoodVision Big 🍔👁️💪'
+description = 'An [EfficientNetB2 feature extractor](https://pytorch.org/vision/0.16/models/generated/torchvision.models.efficientnet_b2.html#efficientnet-b2) computer vision model to classify images 101 classes of food from the Food101 dataset.'
+article = 'Created at [11. Turning our FoodVision Big model into a deployable app](https://www.learnpytorch.io/09_pytorch_model_deployment/#11-turning-our-foodvision-big-model-into-a-deployable-app).'
+
+# Create example list
+example_list = [['examples/' + example] for example in os.listdir('examples')]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict,  # maps inputs to outputs
+                    inputs=gr.Image(type='pil'),
+                    outputs=[gr.Label(num_top_classes=5, label='Predictions'),
+                             gr.Number(label='Prediction time (s)')],
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch(debug=False,  # print errors locally?
+            share=True)  # generate a publically shareable URL