Upload app.py

app.py

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+
+### 1. Imports
+
+import gradio as gr
+import os
+import torch
+
+from model import create_model_alexnet
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+### 2. Model and transforms preparation ###
+
+# Create model_alexnet
+model_alexnet, transforms = create_model_alexnet( num_classes=2)
+
+# Load saved weights
+model_alexnet.load_state_dict(torch.load(f="cat_dog_classifier.pth", map_location=torch.device("cpu")))  # load to CPU
+
+### 3. Predict function ###
+
+# Create predict function
+def predict(img):
+
+    # Start the timer
+    start_time = timer()
+
+    model_alexnet.eval()
+
+    # Reading the image and size transformation
+    features = Image.open(img)
+    img = auto_transform(features).unsqueeze(0)
+
+    with torch.inference_mode():
+      output = model_alexnet(img).to(device)
+    _, predicted = torch.max(output, 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 = 'dog' if predicted.item() ==1  else 'cat'
+
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+
+    # Return the prediction dictionary and prediction time
+    return pred_labels, pred_time
+
+
+### 4. Gradio app ###
+
+import gradio as gr
+
+# Create title, description and article strings
+title = "Classification Demo"
+description = "Cat/Dog classification - Transfer Learning "
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type='filepath'), # what are the inputs?
+                    outputs=[gr.Label(label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    #examples=example_list,
+                    title=title,
+                    description=description,)
+
+# Launch the demo!
+demo.launch()