app.py

### 1. Imports and class names setup ### 
import gradio as gr
import os
import torch

from model import create_vggface2_model
from timeit import default_timer as timer
from typing import Tuple, Dict

# Setup class names
class_names = ["real", "spoof"]

### 2. Model and transforms preparation ###

# Create EffNetB2 model
vggface2, data_transform = create_vggface2_model(
    num_classes=2, # len(class_names) would also work
)

# Load saved weights
vggface2.load_state_dict(
    torch.load(
        f="01_pretrained_vggface2_anti_spoof_18_3_11_2_2023_facenet.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 = data_transform(img).unsqueeze(0)
    
    # Put model into evaluation mode and turn on inference mode
    vggface2.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(vggface2(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 = 'Liveness Detection System 🤖'
description = 'A vggface2 pretrained computer vision model to classify images as spoof or real.'
article = 'Prototype 1 for detecting liveness in an image'

# 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 my inputs?
                    outputs=[gr.Label(num_top_classes=2, label= 'Predictions'), #what are my outputs?
                             gr.Number(label='Prediction time(s)')],  #our fn has 2 outputs
                    examples=example_list,
                    title=title,
                    description=description,
                    article=article)

#Launch the demo! 
demo.launch()