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_effnet_b1
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names (Hard Coded)
+class_names = ['bengal', 'domestic_shorthair', 'maine_coon', 'ragdoll', 'siamese']
+
+### 2. Model and transforms preparation ###
+
+# Create EffNetB1 model
+effnet_b1, effnet_b1_transforms = create_effnet_b1()
+
+# Load saved weights
+effnet_b1.load_state_dict(
+    torch.load(
+        f="effnet_b1.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+
+### 3. Predict function ###
+
+# Create 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 = effnetb0_transforms(img).unsqueeze(0)
+
+    # Put model into evaluation mode and turn on inference mode
+    effnetb0.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(effnetb0(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 = "Cat Breed Classifier"
+description = "This application is designed to categorize cat breeds and is limited to only Bengal, Domestic Shorthair, Maine Coon, Ragdoll, and Siamese breeds."
+article = "Created by kaiku meoowww 😻 "
+
+
+
+# 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,
+                    article=article)
+
+# Launch the demo!
+demo.launch()