app.py

import gradio as gr
from gradio.mix import Parallel
from fastai.vision.all import load_learner

def classify_image_color(img):
    from fastai.vision.all import load_learner
    learn = load_learner('model-color.pkl')
    categories = learn.dls.vocab
    pred, idx, probs = learn.predict(img)
    return {f"{category}": float(prob) for category, prob in zip(categories, probs)}

def classify_image_shape(img):
    from fastai.vision.all import load_learner
    learn = load_learner('bricks-model.pkl')
    categories = learn.dls.vocab
    pred, idx, probs = learn.predict(img)
    return {f"{category}": float(prob) for category, prob in zip(categories, probs)}

def classify_image(img):
    color_result = classify_image_color(img)
    shape_result = classify_image_shape(img)
    result = {}
    for key in set(color_result.keys()) | set(shape_result.keys()):
        result[key] = color_result.get(key, 0.0) + shape_result.get(key, 0.0)
    return result

def postprocess(prediction):
    sorted_pred = sorted(prediction.items(), key=lambda x: x[1], reverse=True)
    return sorted_pred


image = gr.inputs.Image(shape=(256, 256))
label = gr.outputs.Label()

intf = gr.Interface(
    fn=classify_image, 
    inputs=image, 
    outputs=label, 
    examples="", 
    title="Lego Brick Classifier", 
    layout="vertical"
)
intf.launch()