import gradio as gr
from fastcore.all import *
from fastai.vision.all import *
from pathlib import Path

plt = platform.system()
if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
    
def classify_image(img):
    learn = load_learner(str(Path('./nihal_model.pkl')))
    pred,pred_idx,probs = learn.predict(img)
    #return both the prediction and the probability either nihal or not and format in perccentage
    return pred, "{:.0%}".format(float(probs[pred_idx])),float(probs[~pred_idx])
    #return f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'


if __name__ == '__main__':
    # Define inputs and outputs for Gradio interface
    inputs = [gr.Image(type="pil", label="Select an image of Nihal")]
    outputs = [
        gr.Label(""),
        gr.Label(label="Confidence"),
    ]

    # Launch the Gradio interface
    interface = gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs)
    interface.launch()