import os
import pip
import gradio as gr
from PIL import Image

from backend import Infer


DEBUG = False

infer = Infer(DEBUG)
example_image_path = ["assets/example_1.jpg", "assets/example_2.jpg", "assets/example_3.jpg"]

outputs = [
    gr.Image(label="Thumb"),
    gr.Number(label="DeepNAPSI Thumb", precision=0),
    gr.Image(label="Index"),
    gr.Number(label="DeepNAPSI Index", precision=0),
    gr.Image(label="Middle"),
    gr.Number(label="DeepNAPSI Middle", precision=0),
    gr.Image(label="Ring"),
    gr.Number(label="DeepNAPSI Ring", precision=0),
    gr.Image(label="Pinky"),
    gr.Number(label="DeepNAPSI Pinky", precision=0),
    gr.Number(label="DeepNAPSI Sum", precision=0),
]

with gr.Blocks(analytics_enabled=False, title="DeepNAPSI") as demo:
    with gr.Column():
        gr.Markdown("## Welcome to the DeepNAPSI application!")
        gr.Markdown("Upload an image of the one hand and click **Predict NAPSI** to see the output.")
        gr.Markdown("*Note*: Make sure there are no identifying information present in the image. The prediction can take up to 4.5 minutes." )
        gr.Markdown("*Note*: This is not a medical product and cannot be used for a patient diagnosis in any way.")
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        image_input = gr.Image()
                        example_images = gr.Examples(example_image_path, image_input, outputs,
                                    fn=infer.predict, cache_examples=True)
                    with gr.Row():
                        image_button = gr.Button("Predict NAPSI")
                    with gr.Row():
                        with gr.Column():
                            outputs[0].render()
                            outputs[1].render()
                        with gr.Column():
                            outputs[2].render()
                            outputs[3].render()
                        with gr.Column():
                            outputs[4].render()
                            outputs[5].render()
                        with gr.Column():
                            outputs[6].render()
                            outputs[7].render()
                        with gr.Column():
                            outputs[8].render()
                            outputs[9].render()
                        outputs[10].render()
        image_button.click(infer.predict, inputs=image_input, outputs=outputs)

demo.launch(share=True if DEBUG else False, enable_queue=True, favicon_path="assets/favicon-32x32.png")