Upload app.py

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   # this will create our feature extractor
+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(num_classes=101)
+
+# Load the saved weights
+effnetb2.load_state_dict(
+    torch.load(f="09_pretrained_effnetb2_feature_extractor_food101_20pct.pth",
+              map_location=torch.device("cpu")) # it's important to map to cpu if trained on gpu or it will default to gpu
+)
+
+### 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 to add batch dimension
+    
+    # Put model into eval mode, make prediction
+    effnetb2.eval()
+    with torch.inference_mode():
+        # Pass transformed image through the model and turn the prediction logits into probabilities
+        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, title, description and article
+title = "Test ML title"
+description = "this is a test description"
+article = "this is a test article"
+
+# 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()