import gradio as gr
from PIL import Image
from typing import Tuple, Optional

# -------------------------------------------------------------------
# A360 Croptool – Model-Agnostic Cropping Test Harness
#
# NOTE:
# - This version only stubs the model calls.
# - I will later plug in:
#     • RetinaFace / InsightFace (face + landmarks)
#     • YOLOv8/YOLOv9 (face/body/region detection)
#     • Face Alignment nets
#     • SAM / SAM-HQ
#     • BiSeNet (face parsing)
#     • CLIPSeg (text-guided cropping)
# -------------------------------------------------------------------

FACE_MODEL_CHOICES = [
    "RetinaFace (InsightFace)",
    "InsightFace 2D Landmarks (2d106)",
    "Face-Alignment (1adrianb)",
]

BODY_MODEL_CHOICES = [
    "YOLOv8 Body Detector",
    "YOLOv9 Body / Part Detector",
    "Human Pose (OpenPose-style)",
]

SEGMENTATION_MODEL_CHOICES = [
    "SAM / SAM-HQ",
    "BiSeNet Face Parsing",
    "CLIPSeg (text-guided)",
]

CROP_TARGET_CHOICES = [
    "Full Face",
    "Eyes / Upper Face",
    "Lips / Lower Face",
    "Jawline / Chin",
    "Neck",
    "Chest / Breasts",
    "Abdomen",
    "Waist / Hips",
    "Arms",
    "Thighs / Legs",
    "Custom Region",
]


def stub_crop(
    image: Optional[Image.Image],
    crop_target: str,
    face_model: str,
    body_model: str,
    seg_model: str,
    text_prompt: str,
) -> Tuple[Image.Image, str]:
    """
    Placeholder cropping callback.

    For now, simply returns the original image and a text summary of
    the options the user selected. I'll replace this with real model
    calls (RetinaFace / YOLO / SAM / CLIPSeg) later.
    """
    if image is None:
        # Gradio will show this as an error toast
        raise gr.Error("Please upload an image first.")

    summary_lines = [
        "Cropping request received:",
        f"• Target region: {crop_target}",
        f"• Face model: {face_model or 'None selected'}",
        f"• Body model: {body_model or 'None selected'}",
        f"• Segmentation model: {seg_model or 'None selected'}",
    ]
    if text_prompt.strip():
        summary_lines.append(f"• Text prompt (for CLIPSeg / SAM): {text_prompt.strip()}")

    summary_lines.append("")
    summary_lines.append("NOTE: This is a stub implementation. "
                          "Model hooks are ready; image is returned unmodified for now.")

    return image, "\n".join(summary_lines)


def create_app() -> gr.Blocks:
    with gr.Blocks(theme="gradio/soft", css="""
        .a360-header { font-size: 1.8rem; font-weight: 700; }
        .a360-subtitle { opacity: 0.8; }
    """) as demo:
        gr.Markdown(
            "<div class='a360-header'>A360 Croptool 🧬</div>"
            "<div class='a360-subtitle'>"
            "Test and prototype clinical cropping models for faces, bodies, and regions of interest."
            "</div>"
        )

        with gr.Row():
            with gr.Column(scale=1):
                input_image = gr.Image(
                    label="Input Image",
                    type="pil",
                    height=480,
                    elem_id="input_image",
                )

                crop_target = gr.Dropdown(
                    CROP_TARGET_CHOICES,
                    value="Full Face",
                    label="Target Region",
                    info="What do you want to crop to?",
                )

                gr.Markdown("### Model Stack (configuration only – models wired later)")

                face_model = gr.Radio(
                    FACE_MODEL_CHOICES,
                    value="RetinaFace (InsightFace)",
                    label="Face / Landmark Model",
                )

                body_model = gr.Radio(
                    BODY_MODEL_CHOICES,
                    value="YOLOv8 Body Detector",
                    label="Body / Region Model",
                )

                seg_model = gr.Radio(
                    SEGMENTATION_MODEL_CHOICES,
                    value="SAM / SAM-HQ",
                    label="Segmentation / Mask Model",
                )

                text_prompt = gr.Textbox(
                    label="Optional Text Prompt (for CLIPSeg / SAM)",
                    placeholder="e.g., 'crop to lips', 'crop to abdomen', 'crop to jawline'",
                    lines=2,
                )

                run_btn = gr.Button("Run Cropping Prototype", variant="primary")

            with gr.Column(scale=1):
                output_image = gr.Image(
                    label="Cropped Output (stub – currently same as input)",
                    height=480,
                )
                debug_text = gr.Markdown(label="Cropping Summary")

        run_btn.click(
            stub_crop,
            inputs=[input_image, crop_target, face_model, body_model, seg_model, text_prompt],
            outputs=[output_image, debug_text],
        )

        return demo


app = create_app()

if __name__ == "__main__":
    app.launch()