app.py

import gradio as gr #Gradio for creating the web interface
from fastai.vision.all import load_learner, PILImage #FastAI fxns for model loading and image processing 

# Load the trained model
learner = load_learner('aj_classifier.pkl')

def classify_image(img):
    """
    Function to classify an uploaded image using the trained model.
    
    Args:
    - img: The image uploaded by the user, received as a PILImage.
    
    Returns:
    - A string with the prediction and the probability of that prediction.
    """
    # Use the model to predict the class of the image
    # 'predict' method returns three values: the predicted class, its index, and the probabilities of all classes.
    pred, pred_idx, probs = learner.predict(img)
    
    # Format the prediction and its probability as a string to show to the user
    return f"This is an Air Jordan {pred}; {(probs[pred_idx]* 100):.02f}% accurate"

# Create a Gradio interface
# This part sets up the Gradio web interface, specifying the function to call for predictions,
# the type of input it expects (an image), and the type of output (text).

examples = ['aj1.jpeg', 
            'aj4.jpeg',
            'aj5.jpeg',
            'aj11.png',
            'aj13.png']

iface = gr.Interface(fn=classify_image,
                     inputs=gr.Image(type='pil'),  # Specifies that the input should be an image, automatically converted to PILImage
                     outputs="text",  # Specifies that the output is text (the prediction and probability)
                     title="Air Jordan Model Classifier",  # Title of the web interface
                     description="Upload an image of Air Jordan sneakers, and the classifier will predict the model.",
                     examples=examples) #Add examples images  

# This condition checks if the script is being run as the main program and launches the Gradio interface.
# It ensures that the Gradio server starts only when this script is executed directly, not when imported as a module.
if __name__ == "__main__":
    iface.launch(share=True)  # Starts the Gradio interface