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import gradio as gr
import tempfile
from pathlib import Path
from wrapper import run_pipeline_on_image
import numpy as np
from PIL import Image
from itertools import product
def show_preview(image):
"""Show uploaded image as-is."""
return image
def process(image):
if image is None:
return None, None, [], ""
with tempfile.TemporaryDirectory() as tmpdir:
# Save PIL image preserving original format
ext = image.format.lower() if image.format else 'png'
img_path = Path(tmpdir) / f"input.{ext}"
image.save(img_path)
outputs = run_pipeline_on_image(str(img_path), tmpdir, save_artifacts=True)
# Assemble displays
overlay = outputs.get('Overlay')
mask = outputs.get('Mask')
order = ['NDVI', 'ARI', 'GNDVI']
gallery_items = [outputs[k] for k in order if k in outputs]
stats_text = outputs.get('StatsText', '')
return overlay, mask, gallery_items, stats_text
with gr.Blocks() as demo:
gr.Markdown("# 🌿 Sorghum Plant Analysis Demo")
gr.Markdown("Upload a sorghum plant image to analyze vegetation indices, segmentation overlay, and stats.")
with gr.Row():
with gr.Column():
inp = gr.Image(type="pil", label="Upload Image")
run = gr.Button("Run Pipeline", variant="primary")
with gr.Column():
preview = gr.Image(type="pil", label="Uploaded Image Preview", interactive=False)
with gr.Row():
overlay_img = gr.Image(type="filepath", label="Segmentation Overlay", interactive=False)
mask_img = gr.Image(type="filepath", label="Mask", interactive=False)
gallery = gr.Gallery(label="Vegetation Indices", columns=3, height="auto")
stats = gr.Textbox(label="Statistics", lines=4)
# Update preview when image is uploaded
inp.change(fn=show_preview, inputs=inp, outputs=preview)
run.click(process, inputs=inp, outputs=[overlay_img, mask_img, gallery, stats])
if __name__ == "__main__":
demo.launch() |