from fastai.vision.all import *
import torch
import gradio as gr

learn = load_learner("model_tvdesktop.pkl")
labels = learn.dls.vocab

def classify_image(img):
    img = PILImage.create(img)
    pred, idx, probs = learn.predict(img)
    output = dict(zip(labels, map(float, probs)))
    print(probs[0].item()*100)
    count = 0
    for out in output:
        val = output[out]
        if val*100 < 60:
            count += 1
        if count == 2:
            return {"Not sure/Others": 0}
    return output

image = gr.inputs.Image(shape=(224, 224))
label = gr.outputs.Label()

title = "CRT TV and Desktop Monitor Classifier"
description = "A simple image classifier."

intf = gr.Interface(
    fn=classify_image,
    inputs=image,
    outputs=label,
    title=title,
    description=description
)

intf.launch(inline=False)