Create app.py

app.py

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+import gradio as gr, os
+from bootstrap_upstream import fetch_upstream
+from pipeline.rules import zone_lus_score, ZoneFeatures
+from pipeline import rtlucassen_wrapper as rtl
+from pipeline import medivlad_wrapper as mvl
+
+def setup_check():
+    results = fetch_upstream()
+    folders = {
+        "B-line_detection": os.path.isdir("external/B-line_detection"),
+        "LUS-Segmentation-RT": os.path.isdir("external/LUS-Segmentation-RT"),
+        "MeDiVLAD": os.path.isdir("external/MeDiVLAD"),
+        "weights_rtlucassen": os.path.isdir("models/rtlucassen"),
+        "weights_medivlad": os.path.isdir("models/medivlad"),
+    }
+    return {"fetched": results, "folders": folders}
+
+def score_demo_zone(_clip):
+    try:
+        b_counts, _ = rtl.run_bline_detector(_clip)
+        median_b = sorted(b_counts)[len(b_counts)//2] if b_counts else 0
+        z = ZoneFeatures(b_count=median_b, b_area_frac=0.0, has_consolidation=False)
+        return {"zone_score": zone_lus_score(z), "median_b": median_b}
+    except Exception as e:
+        return {"error": str(e)}
+
+def run_medivlad(_clip):
+    try:
+        return mvl.run_video_severity(_clip)
+    except Exception as e:
+        return {"error": str(e)}
+
+with gr.Blocks() as demo:
+    gr.Markdown("## NeoLUS — Original Repos (private research)\n**Note:** Upload authorized weights to `/models/*`. Not for clinical use.")
+    with gr.Accordion("Setup / Status", open=True):
+        gr.Button("Run setup check").click(setup_check, None, gr.JSON())
+
+    with gr.Tab("Zone Scoring"):
+        clip = gr.Video(label="Zone clip")
+        go = gr.Button("Analyze zone")
+        out = gr.JSON()
+        go.click(score_demo_zone, clip, out)
+
+    with gr.Tab("Video Severity (MeDiVLAD)"):
+        clip2 = gr.Video()
+        go2 = gr.Button("Run")
+        out2 = gr.JSON()
+        go2.click(run_medivlad, clip2, out2)
+
+if __name__ == "__main__":
+    demo.launch()