Update app.py

app.py

@@ -1,32 +1,37 @@
+# Repo layout (create these files in your Space)
+# ├─ app.py  ← paste this whole file
+# ├─ requirements.txt  ← at end of this file
+# └─ assets/msk/Gleamer bone view.png, assets/msk/overlay.png  ← optional demo images
+
 import math
 from pathlib import Path
 import gradio as gr
 import matplotlib.pyplot as plt
 
 # ------------------------------
-# Config: MSK default scenario
+# Config: Gleamer Bone View scenario
 # ------------------------------
 ASSETS = Path("assets")
 MSK_CFG = {
     "description": (
-        "AI for MSK MRI helps detect cartilage/meniscus tears, speeds reporting, "
-        "reduces repeat scans via QA and protocol optimization."
+        "Gleamer Bone View AI assists in detecting fractures on X-rays, speeding reporting, "
+        "reducing missed diagnoses, and improving workflow efficiency."
     ),
-    "sample_images": ["msk/1.png", "msk/overlay.png"],
+    "sample_images": ["msk/Gleamer Bone View.png", "msk/overlay.png"],
     "roi_inputs": {
-        "baseline_volume": 15000,           # studies / year
-        "avg_rev_per_study": 6000,          # currency / study
-        "mins_saved_per_study": 5,          # minutes
-        "cost_per_rad_hour": 5200,          # currency / hour
-        "baseline_repeats": 750,            # repeat scans / year
-        "cost_per_repeat": 6000,            # currency / repeat scan
-        "repeat_reduction_pct": 10,         # % reduction with AI
-        "program_cost_annual": 1200000      # currency / year
+        "baseline_volume": 15000,
+        "avg_rev_per_study": 6000,
+        "mins_saved_per_study": 5,
+        "cost_per_rad_hour": 5200,
+        "baseline_repeats": 750,
+        "cost_per_repeat": 6000,
+        "repeat_reduction_pct": 10,
+        "program_cost_annual": 1200000
     },
     "evidence": [
-        "Time savings on MSK reads reported in multi-center settings.",
-        "Repeat scans reduced through automated QA / protocol selection.",
-        "High agreement with subspecialty MSK radiologists in tear detection."
+        "Time savings in fracture detection reported in multi-center settings.",
+        "Reduced missed fractures through AI-assisted reads.",
+        "High agreement with subspecialty musculoskeletal radiologists in fracture detection."
     ],
     "methodology": (
         "Gross benefit = Efficiency savings + Savings from reduced repeats.\n"
@@ -40,19 +45,10 @@ MSK_CFG = {
 # ------------------------------
 # ROI math
 # ------------------------------
-def compute_roi(
-    period: str,
-    baseline_volume: float,
-    avg_rev_per_study: float,            # kept for future extensions (e.g., throughput-driven revenue)
-    mins_saved_per_study: float,
-    cost_per_rad_hour: float,
-    baseline_repeats: float,
-    cost_per_repeat: float,
-    repeat_reduction_pct: float,
-    program_cost_annual: float,
-):
-    """Compute ROI metrics; normalize internally to annual, then present for selected period."""
-    # annualized inputs
+
+def compute_roi(period, baseline_volume, avg_rev_per_study, mins_saved_per_study,
+                cost_per_rad_hour, baseline_repeats, cost_per_repeat,
+                repeat_reduction_pct, program_cost_annual):
     eff_savings_annual = (mins_saved_per_study / 60.0) * baseline_volume * cost_per_rad_hour
     repeats_avoided_annual = baseline_repeats * (repeat_reduction_pct / 100.0)
     repeat_savings_annual = repeats_avoided_annual * cost_per_repeat
@@ -64,11 +60,9 @@ def compute_roi(
     roi_pct = (net_benefit_annual / program_cost * 100.0) if program_cost else 0.0
     payback_months = (program_cost / (gross_benefit_annual / 12.0)) if gross_benefit_annual > 0 else math.inf
 
-    # simple operational metrics
     hours_saved_annual = (mins_saved_per_study / 60.0) * baseline_volume
-    fte_saved_eq = hours_saved_annual / 1920.0  # ~1920 working hours/year
+    fte_saved_eq = hours_saved_annual / 1920.0
 
-    # scale for period view
     factor = 1 if period == "Annual" else 1/12
 
     metrics = {
@@ -81,29 +75,24 @@ def compute_roi(
         "Payback (months)": (round(payback_months, 1) if payback_months != math.inf else float("inf")),
         "Hours saved / year": round(hours_saved_annual, 1),
         "FTE saved (≈1920h/yr)": round(fte_saved_eq, 2),
-        # Keeping avg_rev_per_study visible for future throughput revenue logic
         "Avg revenue per study (input)": avg_rev_per_study,
     }
     return metrics
 
 
 def waterfall_plot(metrics: dict):
-    """Return a simple matplotlib waterfall figure using current metrics."""
     fig = plt.figure()
     components = ["Efficiency savings", "Repeat savings", "Program cost"]
     deltas = [metrics[c] for c in components]
-    # Program cost is a negative contributor
     deltas[2] = -abs(deltas[2])
 
     running = 0
-    x_positions = range(len(components) + 1)
     cumulative = [0]
     for d in deltas:
         running += d
         cumulative.append(running)
 
-    # Draw bars as thick lines (simple waterfall)
-    for i, d in enumerate(deltas):
+    for i in range(len(deltas)):
         y0, y1 = cumulative[i], cumulative[i+1]
         plt.plot([i, i], [y0, y1], linewidth=14)
 
@@ -115,82 +104,48 @@ def waterfall_plot(metrics: dict):
     return fig
 
 
-# ------------------------------
-# UI callbacks
-# ------------------------------
-
 def init(period):
     cfg = MSK_CFG
     desc = cfg["description"]
-    # only include existing asset files
     gallery = [str(ASSETS / p) for p in cfg["sample_images"] if (ASSETS / p).exists()]
 
     inputs = cfg["roi_inputs"]
-    metrics = compute_roi(
-        period,
-        inputs["baseline_volume"],
-        inputs["avg_rev_per_study"],
-        inputs["mins_saved_per_study"],
-        inputs["cost_per_rad_hour"],
-        inputs["baseline_repeats"],
-        inputs["cost_per_repeat"],
-        inputs["repeat_reduction_pct"],
-        inputs["program_cost_annual"],
-    )
+    metrics = compute_roi(period, **inputs)
     fig = waterfall_plot(metrics)
 
-    # Executive readout
     readout = (
-        f"**MSK AI snapshot** — Net benefit: {metrics['Net benefit']:.0f}; "
+        f"**Gleamer Bone View snapshot** — Net benefit: {metrics['Net benefit']:.0f}; "
         f"ROI (annualized): {metrics['ROI % (annualized)']}%; "
         f"Payback: {metrics['Payback (months)']} months.\n\n"
         f"Operational: {metrics['Hours saved / year']:.0f} hours saved (~{metrics['FTE saved (≈1920h/yr)']:.2f} FTE)."
     )
 
-    ev_md = "\n".join([f"- {e}" for e in cfg.get("evidence", [])]) or "_Add citations_")
+    ev_md = "\n".join([f"- {e}" for e in cfg.get('evidence', [])]) or "_Add citations_"
     meth_md = cfg.get("methodology", "")
 
-    defaults = [
-        inputs["baseline_volume"],
-        inputs["avg_rev_per_study"],
-        inputs["mins_saved_per_study"],
-        inputs["cost_per_rad_hour"],
-        inputs["baseline_repeats"],
-        inputs["cost_per_repeat"],
-        inputs["repeat_reduction_pct"],
-        inputs["program_cost_annual"],
-    ]
-
+    defaults = list(inputs.values())
     return desc, gallery, *defaults, metrics, fig, readout, ev_md, meth_md
 
 
-def recalc(period,
-           baseline_volume, avg_rev_per_study, mins_saved_per_study,
+def recalc(period, baseline_volume, avg_rev_per_study, mins_saved_per_study,
            cost_per_rad_hour, baseline_repeats, cost_per_repeat,
            repeat_reduction_pct, program_cost_annual):
-    metrics = compute_roi(
-        period,
-        baseline_volume, avg_rev_per_study, mins_saved_per_study,
-        cost_per_rad_hour, baseline_repeats, cost_per_repeat,
-        repeat_reduction_pct, program_cost_annual,
-    )
+    metrics = compute_roi(period, baseline_volume, avg_rev_per_study, mins_saved_per_study,
+                          cost_per_rad_hour, baseline_repeats, cost_per_repeat,
+                          repeat_reduction_pct, program_cost_annual)
     fig = waterfall_plot(metrics)
     readout = (
-        f"**MSK AI snapshot** — Net benefit: {metrics['Net benefit']:.0f}; "
+        f"**Gleamer Bone View snapshot** — Net benefit: {metrics['Net benefit']:.0f}; "
         f"ROI (annualized): {metrics['ROI % (annualized)']}%; "
         f"Payback: {metrics['Payback (months)']} months.\n\n"
         f"Operational: {metrics['Hours saved / year']:.0f} hours saved (~{metrics['FTE saved (≈1920h/yr)']:.2f} FTE)."
     )
     return metrics, fig, readout
 
-
-# ------------------------------
-# Gradio UI
-# ------------------------------
-with gr.Blocks(title="MSK AI ROI – Interactive", fill_height=True) as demo:
+with gr.Blocks(title="Gleamer Bone View ROI – Interactive", fill_height=True) as demo:
     gr.Markdown("""
-    # MSK AI ROI – Interactive
-    **Clinician-first sandbox**: explore a sample MSK MRI case, tweak workflow assumptions, and see financial, operational, and clinical impact instantly.
+    # Gleamer Bone View ROI – Interactive
+    **Clinician-first sandbox**: explore a sample fracture detection case, tweak workflow assumptions, and see financial, operational, and clinical impact instantly.
     """)
 
     period = gr.Radio(["Annual", "Monthly"], value="Annual", label="Time basis")
@@ -198,17 +153,17 @@ with gr.Blocks(title="MSK AI ROI – Interactive", fill_height=True) as demo:
     with gr.Tabs():
         with gr.Tab("Explore"):
             desc = gr.Markdown()
-            gallery = gr.Gallery(label="Sample MSK study", columns=3, height=320, show_label=True)
+            gallery = gr.Gallery(label="Sample case", columns=3, height=320, show_label=True)
 
         with gr.Tab("ROI Simulator"):
             with gr.Row():
                 with gr.Column(scale=1, min_width=360):
-                    baseline_volume = gr.Slider(0, 100000, value=15000, step=50, label="Annual MSK MRI volume")
+                    baseline_volume = gr.Slider(0, 100000, value=15000, step=50, label="Annual volume")
                     avg_rev_per_study = gr.Slider(0, 50000, value=6000, step=50, label="Avg revenue per study (info)")
                     mins_saved_per_study = gr.Slider(0, 60, value=5, step=1, label="Minutes saved per study")
                     cost_per_rad_hour = gr.Slider(0, 20000, value=5200, step=50, label="Radiologist cost/hour")
-                    baseline_repeats = gr.Slider(0, 100000, value=750, step=10, label="Baseline repeat scans / year")
-                    cost_per_repeat = gr.Slider(0, 50000, value=6000, step=50, label="Cost per repeat scan")
+                    baseline_repeats = gr.Slider(0, 100000, value=750, step=10, label="Baseline repeats/year")
+                    cost_per_repeat = gr.Slider(0, 50000, value=6000, step=50, label="Cost per repeat")
                     repeat_reduction_pct = gr.Slider(0, 100, value=10, step=1, label="Repeat reduction with AI (%)")
                     program_cost_annual = gr.Slider(0, 10000000, value=1200000, step=10000, label="Program cost (annual)")
                 with gr.Column(scale=1):
@@ -222,32 +177,26 @@ with gr.Blocks(title="MSK AI ROI – Interactive", fill_height=True) as demo:
         with gr.Tab("Methodology"):
             methodology_md = gr.Markdown()
 
-    # Wire events
-    period.change(
-        init,
-        [period],
-        [desc, gallery, baseline_volume, avg_rev_per_study, mins_saved_per_study,
-         cost_per_rad_hour, baseline_repeats, cost_per_repeat, repeat_reduction_pct, program_cost_annual,
-         metrics, chart, readout, evidence_md, methodology_md]
-    )
+    period.change(init, [period], [desc, gallery, baseline_volume, avg_rev_per_study, mins_saved_per_study,
+                                   cost_per_rad_hour, baseline_repeats, cost_per_repeat, repeat_reduction_pct, program_cost_annual,
+                                   metrics, chart, readout, evidence_md, methodology_md])
 
     for comp in [baseline_volume, avg_rev_per_study, mins_saved_per_study, cost_per_rad_hour,
                  baseline_repeats, cost_per_repeat, repeat_reduction_pct, program_cost_annual]:
-        comp.change(
-            recalc,
-            [period, baseline_volume, avg_rev_per_study, mins_saved_per_study,
-             cost_per_rad_hour, baseline_repeats, cost_per_repeat, repeat_reduction_pct, program_cost_annual],
-            [metrics, chart, readout]
-        )
-
-    gr.on(
-        load=True,
-        fn=init,
-        inputs=[period],
-        outputs=[desc, gallery, baseline_volume, avg_rev_per_study, mins_saved_per_study,
-                 cost_per_rad_hour, baseline_repeats, cost_per_repeat, repeat_reduction_pct, program_cost_annual,
-                 metrics, chart, readout, evidence_md, methodology_md]
-    )
+        comp.change(recalc, [period, baseline_volume, avg_rev_per_study, mins_saved_per_study,
+                             cost_per_rad_hour, baseline_repeats, cost_per_repeat, repeat_reduction_pct, program_cost_annual],
+                    [metrics, chart, readout])
+
+    gr.on(load=True, fn=init, inputs=[period],
+          outputs=[desc, gallery, baseline_volume, avg_rev_per_study, mins_saved_per_study,
+                   cost_per_rad_hour, baseline_repeats, cost_per_repeat, repeat_reduction_pct, program_cost_annual,
+                   metrics, chart, readout, evidence_md, methodology_md])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()
+
+# ------------------------------
+# requirements.txt
+# ------------------------------
+# gradio>=4.44.0
+# matplotlib