update

app.py

@@ -4,6 +4,9 @@ from pathlib import Path
 from wrapper import run_pipeline_on_image
 from PIL import Image
 
+# Precomputed demo outputs inside the repo (placed by you once)
+PRECOMPUTED_DIR = (Path(__file__).resolve().parent / "precomputed_demo")
+
 ##add the process function
 def process(file_path):
     if not file_path:
@@ -78,6 +81,94 @@ def process(file_path):
             stats_text,
         )
 
+def _load_pil(path: Path):
+    try:
+        if not path or not path.exists():
+            return None
+        im = Image.open(path)
+        out = im.copy()
+        im.close()
+        return out
+    except Exception:
+        return None
+
+def _first_existing(paths):
+    for p in paths:
+        if p and p.exists():
+            return p
+    return None
+
+def load_precomputed():
+    base = PRECOMPUTED_DIR
+    if not base.exists():
+        return None, None, None, None, None, None, None, [], None, None, ""
+
+    # Common subdirs
+    results = base / "results"
+    veg = base / "Vegetation_indices_images"
+    tex = base / "texture_output"
+
+    input_img = _load_pil(_first_existing([
+        base / "input_image.png",
+        base / "input_image.tif",
+        base / "input.png",
+    ]))
+    composite = _load_pil(_first_existing([
+        results / "composite.png",
+    ]))
+    mask = _load_pil(results / "mask.png")
+    overlay = _load_pil(results / "overlay.png")
+
+    texture_img = _load_pil(_first_existing([
+        tex / "lbp_green.png",
+        tex / "lbp.png",
+    ]))
+    hog_img = _load_pil(_first_existing([
+        tex / "hog_green.png",
+        tex / "hog.png",
+    ]))
+    lac1_img = _load_pil(_first_existing([
+        tex / "lac1_green.png",
+        tex / "lacunarity.png",
+    ]))
+
+    # Vegetation gallery in order
+    veg_order = ["ndvi.png", "gndvi.png", "savi.png"]
+    gallery_items = []
+    for fname in veg_order:
+        p = veg / fname
+        img = _load_pil(p)
+        if img is not None:
+            gallery_items.append(img)
+
+    size_img = _load_pil(results / "size.size_analysis.png")
+    yolo_img = _load_pil(results / "yolo_tips.png")
+
+    stats_txt_path = _first_existing([
+        base / "stats.txt",
+        results / "stats.txt",
+    ])
+    stats_text = ""
+    if stats_txt_path and stats_txt_path.exists():
+        try:
+            stats_text = stats_txt_path.read_text()
+        except Exception:
+            stats_text = ""
+
+    return (
+        input_img,
+        composite,
+        mask,
+        overlay,
+        texture_img,
+        hog_img,
+        lac1_img,
+        gallery_items,
+        size_img,
+        yolo_img,
+        stats_text,
+    )
+
 
 with gr.Blocks() as demo:
     gr.Markdown("# 🌿 Automated Plant Analysis Demo")
@@ -138,5 +229,24 @@ with gr.Blocks() as demo:
         ]
     )
 
+    # Preload with precomputed images (no pipeline run on startup)
+    demo.load(
+        load_precomputed,
+        inputs=None,
+        outputs=[
+            input_img,
+            composite_img,
+            mask_img,
+            overlay_img,
+            texture_img,
+            hog_img,
+            lac1_img,
+            gallery,
+            size_img,
+            yolo_img,
+            stats,
+        ],
+    )
+
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -14,3 +14,4 @@ plantcv
 kornia
 timm
 ultralytics
+tifffile