"""
app.py - Hugging Face Spaces entry point.
Automatically downloads model checkpoints on first run and
serves a Gradio interface for AI object removal.
"""

import os
import sys

# Ensure project root is on the path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

# -- Step 1: Download checkpoints if needed ----------------------------------
print("Checking model checkpoints...")
from download_checkpoints import main as download_checkpoints
download_checkpoints()

# -- Step 2: Load the pipeline -----------------------------------------------
import gradio as gr
from pipeline import ObjectRemovalPipeline
from config import CLIP_SIMILARITY_THRESHOLD

print("Initializing AI models (GroundingDINO, SAM, CLIP)...")
pipeline = ObjectRemovalPipeline()
print("All models loaded. Ready!")

# -- Step 3: Define Gradio callback ------------------------------------------
def predict(scene_img_path, object_img_paths, threshold):
    if scene_img_path is None:
        return None, "Error: Please upload a scene image."
    if not object_img_paths:
        return None, "Error: Please upload at least one object reference image."

    # object_img_paths is a list of file paths from gr.File
    if isinstance(object_img_paths, str):
        object_img_paths = [object_img_paths]

    try:
        result_pil = pipeline.run(
            scene_path=scene_img_path,
            object_paths=object_img_paths,
            threshold=threshold,
            save_debug=False  # avoid cluttering cloud storage
        )
        return result_pil, "Done! Object(s) removed successfully."
    except Exception as e:
        import traceback
        error_trace = traceback.format_exc()
        return None, f"Error:\n{error_trace}"

# -- Step 4: Build Gradio UI -------------------------------------------------
with gr.Blocks(title="AI Object Eraser") as demo:
    gr.Markdown("# AI Object Eraser")
    gr.Markdown(
        "Upload a **scene photo** and one or more **reference photos** of the objects "
        "you want to remove. The AI will detect and erase them for you."
    )

    with gr.Row():
        with gr.Column():
            scene_input = gr.Image(
                label="Scene Image",
                type="filepath"
            )
            object_input = gr.File(
                label="Object Reference(s) to Remove",
                file_count="multiple"
            )
            threshold_slider = gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=CLIP_SIMILARITY_THRESHOLD,
                step=0.05,
                label="Detection Sensitivity (lower = more detections)"
            )
            run_btn = gr.Button("Remove Objects", variant="primary")

        with gr.Column():
            output_img = gr.Image(label="Result")
            status_box = gr.Textbox(label="Status")

    run_btn.click(
        fn=predict,
        inputs=[scene_input, object_input, threshold_slider],
        outputs=[output_img, status_box]
    )

# -- Step 5: Launch ----------------------------------------------------------
if __name__ == "__main__":
    # Port 7860 is mandatory for Hugging Face Spaces
    demo.launch(server_name="0.0.0.0", server_port=7860)