""" Gradio app: D-FINE + SigLIP Classify. """

import os
import gradio as gr
from pathlib import Path

from dfine_jina_pipeline import run_single_image

BASE_DIR = os.path.dirname(os.path.abspath(__file__))

DEFAULT_LABELS = "gun, knife, cigarette, phone"


def run_dfine_classify(image, dfine_threshold, dfine_model_choice, classifier_choice, siglip_threshold, labels_text):
    """D-FINE first, then classify crops with SigLIP.
    Returns (group_crop_gallery, known_crop_gallery, status_message).
    """
    if image is None:
        return [], [], "Upload an image."

    labels = [l.strip() for l in labels_text.split(",") if l.strip()]
    if not labels:
        return [], [], "Enter at least one label."

    dfine_model = dfine_model_choice.strip().lower() if dfine_model_choice else "medium-obj2coco"
    conf_thresh = float(siglip_threshold)
    classifier = classifier_choice.strip() if classifier_choice else "siglip-256"

    group_crops, known_crops, status = run_single_image(
        image,
        dfine_model=dfine_model,
        det_threshold=float(dfine_threshold),
        conf_threshold=conf_thresh,
        gap_threshold=0.0,
        min_side=24,
        crop_dedup_iou=0.4,
        min_display_conf=conf_thresh,
        classifier=classifier,
        labels=labels,
    )

    return [(g, None) for g in (group_crops or [])], [(k, None) for k in (known_crops or [])], status or ""


IMG_HEIGHT = 400


with gr.Blocks(title="Small Object Detection") as app:

    gr.Markdown("# Small Object Detection")

    gr.Markdown(
        "**D-FINE** detects persons/cars, then small-object crops are classified with **SigLIP** (zero-shot). "
        "Choose a D-FINE model and enter comma-separated class labels for SigLIP."
    )

    with gr.Row():

        with gr.Column(scale=1):

            inp_dfine = gr.Image(
                type="pil",
                label="Input image",
                height=IMG_HEIGHT
            )

            dfine_model_radio = gr.Dropdown(
                choices=[
                    "small-obj365", "medium-obj365", "large-obj365",
                    "small-coco", "medium-coco", "large-coco",
                    "small-obj2coco", "medium-obj2coco", "large-obj2coco",
                ],
                value="medium-obj2coco",
                label="D-FINE model",
            )

            classifier_dropdown = gr.Dropdown(
                choices=["siglip-224", "siglip-256", "siglip-384"],
                value="siglip-256",
                label="Classifier model",
            )

            dfine_threshold_slider = gr.Slider(
                minimum=0.05,
                maximum=0.5,
                value=0.15,
                step=0.05,
                label="D-FINE detection threshold",
            )

            def update_dfine_threshold_default(choice):
                if not choice:
                    return gr.update(value=0.15)
                size = choice.strip().lower().split("-")[0]
                defaults = {"large": 0.2, "medium": 0.15, "small": 0.1}
                return gr.update(value=defaults.get(size, 0.15))

            dfine_model_radio.change(
                fn=update_dfine_threshold_default,
                inputs=[dfine_model_radio],
                outputs=[dfine_threshold_slider],
            )

            siglip_threshold_slider = gr.Slider(
                minimum=0.001,
                maximum=0.1,
                value=0.005,
                step=0.001,
                label="SigLIP: min confidence threshold",
            )

            labels_input = gr.Textbox(
                label="Labels (comma-separated)",
                value=DEFAULT_LABELS,
                placeholder="e.g. gun, knife, cigarette, phone",
            )

            btn_dfine = gr.Button(
                "Run D-FINE + Classify",
                variant="primary"
            )

        with gr.Column(scale=1):

            out_gallery_dfine = gr.Gallery(
                label="Person/car crops (all D-FINE objects inside drawn with label + score)",
                height=IMG_HEIGHT,
                columns=2,
                object_fit="contain",
            )

            out_gallery_known = gr.Gallery(
                label="Known objects (class + score above each crop)",
                height=IMG_HEIGHT,
                columns=4,
                object_fit="contain",
            )

            out_status_dfine = gr.Textbox(
                label="Classification details",
                lines=8,
                interactive=False,
            )

    btn_dfine.click(
        fn=run_dfine_classify,
        inputs=[inp_dfine, dfine_threshold_slider, dfine_model_radio, classifier_dropdown, siglip_threshold_slider, labels_input],
        outputs=[out_gallery_dfine, out_gallery_known, out_status_dfine],
        concurrency_limit=1,
    )


app.launch(
    server_name=os.environ.get("GRADIO_SERVER_NAME", "0.0.0.0"),
    server_port=int(
        os.environ.get(
            "PORT",
            os.environ.get("GRADIO_SERVER_PORT", 7860)
        )
    ),
)