update

app.py

@@ -12,36 +12,43 @@ def show_preview(image):
 
 def process(image):
     if image is None:
-        return []
+        return None, None, [], ""
     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 vegetation indices only for now
-        order = [
-            'NDVI', 'ARI', 'GNDVI'  # , 'LBP', 'HOG', 'Lacunarity', 'SizeAnalysis'
-        ]
-        return [outputs[k] for k in order if k in outputs]
+
+        # 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, texture features, and morphology.")
-    
+    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)
-    
-    gallery = gr.Gallery(label="Analysis Results", columns=3, height="auto")
-    
+
+    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=gallery)
+    run.click(process, inputs=inp, outputs=[overlay_img, mask_img, gallery, stats])
 
 if __name__ == "__main__":
     demo.launch()

wrapper.py

@@ -21,6 +21,10 @@ def run_pipeline_on_image(input_image_path: str, work_dir: str, save_artifacts:
     # Use input path directly (already in work_dir from app.py)
     input_path = Path(input_image_path)
 
+    # Ensure demo env vars are set before pipeline construction
+    os.environ['MINIMAL_DEMO'] = '1'
+    os.environ['FAST_OUTPUT'] = '1'
+
     # Build in-memory config pointing input/output to the working directory
     cfg = Config()
     cfg.paths = Paths(
@@ -31,8 +35,6 @@ def run_pipeline_on_image(input_image_path: str, work_dir: str, save_artifacts:
     pipeline = SorghumPipeline(config=cfg)
 
     # Run the pipeline (single image minimal demo)
-    os.environ['MINIMAL_DEMO'] = '1'
-    os.environ['FAST_OUTPUT'] = '1'
     results = pipeline.run(single_image_path=str(input_path))
 
     # Collect outputs
@@ -55,4 +57,29 @@ def run_pipeline_on_image(input_image_path: str, work_dir: str, save_artifacts:
         if path.exists():
             outputs[label] = str(path)
 
+    # Also include overlay and mask if present
+    overlay_path = work / 'results/overlay.png'
+    mask_path = work / 'results/mask.png'
+    if overlay_path.exists():
+        outputs['Overlay'] = str(overlay_path)
+    if mask_path.exists():
+        outputs['Mask'] = str(mask_path)
+
+    # Extract simple stats for display if present in pipeline results
+    try:
+        plants = results.get('plants', {}) if isinstance(results, dict) else {}
+        if plants:
+            _, pdata = next(iter(plants.items()))
+            veg = pdata.get('vegetation_indices', {})
+            stats_lines = []
+            for name in ['NDVI', 'ARI', 'GNDVI']:
+                entry = veg.get(name, {})
+                st = entry.get('statistics', {}) if isinstance(entry, dict) else {}
+                if st:
+                    stats_lines.append(f"{name}: mean={st.get('mean', 0):.3f}, std={st.get('std', 0):.3f}")
+            if stats_lines:
+                outputs['StatsText'] = "\n".join(stats_lines)
+    except Exception:
+        pass
+
     return outputs