# https://www.gradio.app/guides/sharing-your-app#mounting-within-another-fast-api-app
import gradio as gr
import json
import logging
from PredictService import PredictService

log_format = "[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s"
logging.basicConfig(level=logging.INFO, format=log_format)
logger = logging.getLogger()

svc = PredictService()


def api_classification(url):
    url_to_use = url
    if url_to_use == "exemple":
        url_to_use = "https://images.pexels.com/photos/326900/pexels-photo-326900.jpeg?cs=srgb&dl=pexels-pixabay-326900.jpg&fm=jpg"
    predictions = svc.predict(url_to_use)
    return json.dumps(predictions)


with gr.Blocks() as app:
    with gr.Tab("BioCLIP API"):
        with gr.Row():
            with gr.Column():
                # https://www.gradio.app/guides/key-component-concepts
                gr.Textbox(value="This is a BioCLIP based prediction. You must input a public url of an image and you will get TreeOfLife predictions as result", interactive=False)
                api_input = gr.Textbox(
                    placeholder="Image url here",
                    lines=1,
                    label="Image url",
                    show_label=True,
                    info="Add image url here.",
                    value="https://natureconservancy-h.assetsadobe.com/is/image/content/dam/tnc/nature/en/photos/d/o/Downy-woodpecker-Matt-Williams.jpg?crop=0%2C39%2C3097%2C2322&wid=820&hei=615&scl=3.776829268292683"
                )
                api_classification_btn = gr.Button("API", variant="primary")
            with gr.Column():
                api_classification_output = gr.JSON()  # https://www.gradio.app/docs/gradio/json

    api_classification_btn.click(
        fn=api_classification,
        inputs=[api_input],
        outputs=[api_classification_output],
    )
app.queue(max_size=20)
app.launch()