app.py

import gradio as gr
import tempfile
from pathlib import Path
from wrapper import run_pipeline_on_image
from PIL import Image


def process(file_path):
    if not file_path:
        return None, None, None, None, None, [], ""

    with tempfile.TemporaryDirectory() as tmpdir:
        src = Path(file_path)
        ext = src.suffix.lstrip('.') or 'tif'
        img_path = Path(tmpdir) / f"input.{ext}"

        try:
            # Copy raw uploaded bytes
            img_bytes = src.read_bytes()
            img_path.write_bytes(img_bytes)
        except Exception:
            # Fallback: save via PIL if direct copy fails
            Image.open(src).save(img_path)

        # Run the full sorghum pipeline
        outputs = run_pipeline_on_image(str(img_path), tmpdir, save_artifacts=True)

        def load_pil(path_str):
            try:
                if not path_str:
                    return None
                im = Image.open(path_str)
                copied = im.copy()
                im.close()
                return copied
            except Exception:
                return None

        composite = load_pil(outputs.get('Composite'))
        overlay = load_pil(outputs.get('Overlay'))
        mask = load_pil(outputs.get('Mask'))
        size_img = load_pil(str(Path(tmpdir) / 'results/size.size_analysis.png'))

        # Texture LBP green path
        lbp_path = Path(tmpdir) / 'texture_output/lbp_green.png'
        texture_img = load_pil(str(lbp_path)) if lbp_path.exists() else None

        # Vegetation indices
        order = ['NDVI', 'GNDVI', 'SAVI']
        gallery_items = [load_pil(outputs[k]) for k in order if k in outputs]

        stats_text = outputs.get('StatsText', '')

        return size_img, composite, mask, overlay, texture_img, gallery_items, stats_text


with gr.Blocks() as demo:
    gr.Markdown("# 🌿 Automated Plant Analysis Demo")
    gr.Markdown("Upload a sorghum plant image (TIFF preferred) to compute and visualize composite, mask, overlay, texture (LBP), vegetation indices, and statistics.")

    with gr.Row():
        with gr.Column():
            # Use File input to preserve raw TIFFs
            inp = gr.File(
                type="filepath",
                file_types=[".tif", ".tiff", ".png", ".jpg"],
                label="Upload Image"
            )
            run = gr.Button("Run Pipeline", variant="primary")

    with gr.Row():
        size_img = gr.Image(type="pil", label="Morphology Size", interactive=False)
        composite_img = gr.Image(type="pil", label="Composite (Segmentation Input)", interactive=False)
        mask_img = gr.Image(type="pil", label="Mask", interactive=False)
        overlay_img = gr.Image(type="pil", label="Segmentation Overlay", interactive=False)

    with gr.Row():
        texture_img = gr.Image(type="pil", label="Texture LBP (Green Band)", interactive=False)

    gallery = gr.Gallery(label="Vegetation Indices", columns=3, height="auto")
    stats = gr.Textbox(label="Statistics", lines=4)

    run.click(
        process,
        inputs=inp,
        outputs=[size_img, composite_img, mask_img, overlay_img, texture_img, gallery, stats]
    )

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