import gradio as gr
import supervision as sv
import torch
import spaces

from utils.annotate import annotate_with_boxes
from utils.models import load_models, run_inference, CHECKPOINTS
from utils.tasks import TASK_NAMES, TASKS, OBJECT_DETECTION_TASK_NAME, \
    CAPTION_TASK_NAMES

MARKDOWN = """
# Better Florence-2 Playground 🔥
<div>
    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb">
        <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab" style="display:inline-block;">
    </a>
    <a href="https://blog.roboflow.com/florence-2/">
        <img src="https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg" alt="Roboflow" style="display:inline-block;">
    </a>
    <a href="https://arxiv.org/abs/2311.06242">
        <img src="https://img.shields.io/badge/arXiv-2311.06242-b31b1b.svg" alt="arXiv" style="display:inline-block;">
    </a>
    <a href="https://www.youtube.com/watch?v=i3KjYgxNH6w">
        <img src="https://badges.aleen42.com/src/youtube.svg" alt="YouTube" style="display:inline-block;">
    </a>
</div>
"""

# OBJECT_DETECTION_EXAMPLES = [
#     ["microsoft/Florence-2-large-ft", "Object Detection", "https://media.roboflow.com/notebooks/examples/dog-2.jpeg"]
# ]
# CAPTION_EXAMPLES = [
#     ["microsoft/Florence-2-large-ft", "Caption", "https://media.roboflow.com/notebooks/examples/dog-2.jpeg"]
# ]

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MODELS, PROCESSORS = load_models(DEVICE)


@spaces.GPU
def process(checkpoint_dropdown, task_dropdown, image_input):
    model = MODELS[checkpoint_dropdown]
    processor = PROCESSORS[checkpoint_dropdown]
    task = TASKS[task_dropdown]
    if task_dropdown == OBJECT_DETECTION_TASK_NAME:
        _, response = run_inference(
            model, processor, DEVICE, image_input, task)
        detections = sv.Detections.from_lmm(
            lmm=sv.LMM.FLORENCE_2, result=response, resolution_wh=image_input.size)
        return annotate_with_boxes(image_input, detections)
    elif task_dropdown in CAPTION_TASK_NAMES:
        _, response = run_inference(
            model, processor, DEVICE, image_input, task)
        return response[task]


with gr.Blocks() as demo:
    gr.Markdown(MARKDOWN)
    with gr.Row():
        checkpoint_dropdown_component = gr.Dropdown(
            choices=CHECKPOINTS,
            value=CHECKPOINTS[0],
            label="Model", info="Select a Florence 2 model to use.")
        task_dropdown_component = gr.Dropdown(
            choices=TASK_NAMES,
            value=TASK_NAMES[0],
            label="Task", info="Select a task to perform with the model.")

    with gr.Row():
        with gr.Column():
            image_input_component = gr.Image(type='pil', label='Image Input')
            submit_button_component = gr.Button(value='Submit', variant='primary')

        with gr.Column():
            @gr.render(inputs=task_dropdown_component)
            def show_output(text):
                if text == OBJECT_DETECTION_TASK_NAME:
                    image_output_component = gr.Image(type='pil', label='Image Output')
                    submit_button_component.click(
                        fn=process,
                        inputs=[
                            checkpoint_dropdown_component,
                            task_dropdown_component,
                            image_input_component
                        ],
                        outputs=image_output_component
                    )
                elif text in CAPTION_TASK_NAMES:
                    text_output_component = gr.Textbox(label='Caption Output')
                    submit_button_component.click(
                        fn=process,
                        inputs=[
                            checkpoint_dropdown_component,
                            task_dropdown_component,
                            image_input_component
                        ],
                        outputs=text_output_component
                    )

    # @gr.render(inputs=task_dropdown_component)
    # def show_examples(text):
    #     if text == "Object Detection":
    #         gr.Examples(
    #             fn=process,
    #             examples=OBJECT_DETECTION_EXAMPLES,
    #             inputs=[
    #                 checkpoint_dropdown_component,
    #                 task_dropdown_component,
    #                 image_input_component
    #             ],
    #             outputs=image_output_component
    #         )
    #     elif text == "Caption":
    #         gr.Examples(
    #             fn=process,
    #             examples=CAPTION_EXAMPLES,
    #             inputs=[
    #                 checkpoint_dropdown_component,
    #                 task_dropdown_component,
    #                 image_input_component
    #             ],
    #             outputs=text_output_component
    #         )

demo.launch(debug=False, show_error=True, max_threads=1)