Upload app.py

app.py

@@ -0,0 +1,70 @@
+
+### 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
+
+# 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
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=101)
+
+# Load saved weights
+effnetb2.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
+        map_location=torch.device("cpu") # load the model to the CPU
+    )
+)
+
+### 3. Predict function ###
+def predict(img) -> Tuple[Dict, float]:
+    # Start a timer
+    start_time = timer()
+    
+    # Transform the input image for use with EffNetB2
+    img = effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index
+    
+    # Put model into eval mode, make prediction
+    effnetb2.eval()
+    with torch.inference_mode():
+        pred_probs = torch.softmax(effnetb2(img), dim=1)
+    
+    # Create a prediction label and prediction probability dictionary
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+    
+    # Calculate pred time
+    end_time = timer()
+    pred_time = round(end_time-start_time, 4)
+    
+    # Return pred dict and pred time
+    return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+# Create a title, description, article
+title = "FoodVision Big 🍜🍔🥩"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt)."
+article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+
+# 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=3, label="Predictions"),
+                            gr.Number(label="Prediction time (s)")],
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch()