from  hubconf import custom
model = custom(path_or_model='best.pt')  # custom example
# model = create(name='yolov7', pretrained=True, channels=3, classes=80, autoshape=True)  # pretrained example

# Verify inference
import numpy as np
from PIL import Image
import gradio as gr

# imgs = [np.zeros((640, 480, 3))]
# imgs = 'inference/images/meal.jpg'

# results = model(imgs)  # batched inference
# results.print()
# results.save()
def predict(input_image):
    """
    Predict model output
    """
    results = model(input_image)
    output_image = results.render()[0]
    price = "0"

    # Return the output image and price
    return [output_image, price]
    # return [input_image, price]



# gr.Interface(inputs=["image"],outputs=["image"],fn=lambda img:model(img).render()[0]).launch()
gr.Interface(inputs=["image"], outputs=["image", "text"], fn=predict).launch()