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import gradio as gr
import tempfile
from pathlib import Path
from wrapper import run_pipeline_on_image
def show_preview(image):
"""Show the original uploaded image as-is."""
if image is None:
return None
# Return original image without conversion
return image
def process(image):
if image is None:
return []
with tempfile.TemporaryDirectory() as tmpdir:
# Save PIL image preserving original format
# Determine extension from image 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)
# Keep order consistent: return exactly the 7 images
order = [
'NDVI', 'ARI', 'GNDVI', 'LBP', 'HOG', 'Lacunarity', 'SizeAnalysis'
]
return [outputs[k] for k in order if k in outputs]
with gr.Blocks() as demo:
gr.Markdown("# 🌿 Sorghum Plant Analysis Demo")
gr.Markdown("Upload a sorghum plant image to analyze vegetation indices, texture features, and morphology.")
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)
gallery = gr.Gallery(label="Analysis Results", columns=3, height="auto")
# Update preview when image is uploaded
inp.change(fn=show_preview, inputs=inp, outputs=preview)
run.click(process, inputs=inp, outputs=gallery)
if __name__ == "__main__":
demo.launch() |