Upload app.py

app.py

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+
+import gradio as gr 
+import os 
+import torch 
+
+
+from model import create_effnet_b2
+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.strip() for food in f.readlines()]
+ 
+
+#Create effnetb2 model 
+
+#create model and transforms preparation 
+effnetb2, effnetb2_transforms = create_effnetb2(num_classes = 101)
+
+effnetb2.load_state_dict(
+    torch.load(
+        f='pretrained_effnetb2_feature_extractor_food101_20_percent.pth',
+        map_location = torch.device('cpu')))
+
+
+#Predict function
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+    start_time = timer()
+    
+    # Transform the target image and add a batch dimension
+    img = effnetb2_transforms(img).unsqueeze(0)
+    
+    # Put 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 prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
+    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 BIG 🍕🥩🍣"
+description = "An EfficientNetB2 feature extractor computer vision model to classify 101 classes of food from the food 101 dataset"
+
+# 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"), # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=5, 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
+                    )
+
+# Launch the demo!
+demo.launch()
+
+