Update app.py

app.py

@@ -7,7 +7,8 @@ import gradio as gr
 # ==========================================
 # CARPL Multi-Use-Case ROI Calculators (MMG / FFR-CT / MSK AI)
 # Shared UI/UX: Inter font, white cards, pill header, Overall + Tabs + Waterfall + Evidence + CTA
-# Extras: card-style use case chooser, MMG vendor presets, CSV export (fixed)
+# Extras: card-style use case chooser, MMG vendor presets, CSV export
+# MSK updates: Overall Impact shows Financial + Clinical + Operational; hides ROI/Payback if N/A
 # ==========================================
 
 USE_CASES = ["Mammography AI (MMG)", "FFR-CT AI", "MSK AI (ER/Trauma)"]
@@ -75,7 +76,6 @@ def write_csv(rows, title: str = "roi_results") -> str:
     with open(path, "w", encoding="utf-8") as f:
         f.write("Metric,Value\n")
         for lab, val in rows:
-            # strip any HTML tags if present
             val = str(val).replace("<b>", "").replace("</b>", "")
             f.write(f"\"{lab}\",\"{val}\"\n")
     return str(path)
@@ -192,10 +192,10 @@ def compute_mmg(
                 ("Effective capacity increase", pct(capacity_increase_pct)),
             ]
         },
-        "waterfall": [("Incremental revenue", addl_followup_revenue_month), ("Incremental costs", - (vendor_cost_month + platform_cost_month + other_costs_month)), ("Operational value", ops_value_month)],
+        "waterfall": [("Incremental revenue", incr_revenue_month), ("Incremental costs", -incr_costs_month), ("Operational value", ops_value_month)],
         "annual_card": {
-            "incr_rev": addl_followup_revenue_month * 12.0,
-            "incr_costs": (vendor_cost_month + platform_cost_month + other_costs_month) * 12.0,
+            "incr_rev": incr_revenue_month * 12.0,
+            "incr_costs": incr_costs_month * 12.0,
             "ops_value": ops_value_month * 12.0,
             "net": net_impact_month * 12.0,
             "roi_pct": roi_pct_annual,
@@ -232,7 +232,6 @@ def compute_ffrct(
 
     pct_ai_qpa     = max(0.0, min(1.0, pct_billed_ai_qpa/100.0))
     one_test_dx    = max(0.0, min(1.0, one_test_dx_pct/100.0))
-    need_addl_with_ai = 1.0 - one_test_dx
     dec_unnec_ica  = max(0.0, min(1.0, dec_unnec_ica_pct/100.0 * max(0.0, min(1.0, sens_dec_unnec_ica_factor_pct/100.0))))
     more_likely_revasc = max(0.0, min(1.0, more_likely_revasc_pct/100.0))
     revasc_prev    = max(0.0, min(1.0, revasc_prevalence_pct/100.0))
@@ -341,7 +340,7 @@ def compute_ffrct(
         "evidence": evidence,
     }
 
-# ---------- MSK ----------
+# ---------- MSK (ER/Trauma) ----------
 def compute_msk(
     scans_per_day: float,
     reading_time_min: float,
@@ -349,17 +348,28 @@ def compute_msk(
     radiologist_hourly_cost: float = 180.0,
 ):
     scans_per_month = clamp_nonneg(scans_per_day) * 30.0
-    errors_reduced_per_month   = int(scans_per_month * 0.05 * 0.20)
-    discrepant_cases_flagged   = int(scans_per_month * 0.05)
-    hrs_saved_per_visit        = max(0.0, er_time_to_treatment_min) * 0.50 / 60.0
-    time_saved_per_scan_min    = max(0.0, reading_time_min) * 0.30
-    total_time_saved_hours     = (scans_per_month * time_saved_per_scan_min) / 60.0
-    value_time_saved_month     = total_time_saved_hours * radiologist_hourly_cost
-    radiologist_cost_savings   = scans_per_month * 4.0
+
+    # Clinical volumes
+    errors_reduced_per_month = int(scans_per_month * 0.05 * 0.20)   # example incidence × improvement
+    discrepant_cases_flagged = int(scans_per_month * 0.05)
+    hrs_saved_per_visit      = max(0.0, er_time_to_treatment_min) * 0.50 / 60.0
+
+    # Operational value
+    time_saved_per_scan_min  = max(0.0, reading_time_min) * 0.30
+    total_time_saved_hours   = (scans_per_month * time_saved_per_scan_min) / 60.0
+    value_time_saved_month   = total_time_saved_hours * radiologist_hourly_cost
+    radiologist_cost_savings = scans_per_month * 4.0  # proxy from earlier MSK model
+    ops_value_month          = value_time_saved_month + radiologist_cost_savings
+
+    # Financial (FFS-style): no extra revenue/costs modeled here
     incr_revenue_month = 0.0
     incr_costs_month   = 0.0
-    ops_value_month    = value_time_saved_month + radiologist_cost_savings
-    net_impact_month   = ops_value_month
+
+    # Clinical value ($) — placeholder (0); wire a $/event later if desired
+    clinical_value_month = 0.0
+
+    # Net impact
+    net_impact_month = incr_revenue_month - incr_costs_month + ops_value_month + clinical_value_month
 
     evidence = """
     <ul class='evidence'>
@@ -369,33 +379,75 @@ def compute_msk(
     </ul>
     """
 
-    return {
+    return:
+        # Summary line
+        {
         "summary": f"For your ED with ~{int(scans_per_month):,} MSK scans/month, modeled net benefit is {usd(net_impact_month)} per month.",
+        # Financial card (only what matters; no NaNs)
         "financial": {
             "rows": [
-                ("Value of radiologist time saved (mo)", usd(value_time_saved_month)),
-                ("Radiologist cost proxy savings (mo)", usd(radiologist_cost_savings)),
+                ("Incremental revenue (mo)", usd(incr_revenue_month)),
+                ("Incremental costs (mo)", usd(incr_costs_month)),
                 ("Net impact (mo)", f"<b>{usd(net_impact_month)}</b>"),
             ]
         },
+        # Clinical card
         "clinical": {
             "rows": [
                 ("Errors reduced (est.)", f"{errors_reduced_per_month} /mo"),
                 ("Discrepant cases flagged (est.)", f"{discrepant_cases_flagged} /mo"),
                 ("Hours saved per ED visit (modeled)", f"{hrs_saved_per_visit:.2f}"),
             ],
-            "bars": [("Touch-time reduction", 0.30)]
+            "bars": [("Touch-time reduction", 0.30)],
+        },
+        # Operational card
+        "operational": {
+            "rows": [
+                ("Radiologist hours saved / month", f"{total_time_saved_hours:.1f}"),
+                ("Value of radiologist time saved (mo)", usd(value_time_saved_month)),
+                ("Radiologist cost proxy savings (mo)", usd(radiologist_cost_savings)),
+            ]
+        },
+        # Waterfall
+        "waterfall": [
+            ("Incremental revenue", incr_revenue_month),
+            ("Incremental costs", -incr_costs_month),
+            ("Operational value", ops_value_month),
+            # If you later monetize clinical value, add:
+            # ("Clinical value", clinical_value_month),
+        ],
+        # Annual metrics for Overall Impact (no NaNs)
+        "annual_card": {
+            "incr_rev": incr_revenue_month * 12.0,
+            "incr_costs": incr_costs_month * 12.0,
+            "clinical_value": clinical_value_month * 12.0,
+            "ops_value": ops_value_month * 12.0,
+            "net": net_impact_month * 12.0,
+            "roi_pct": None,     # hidden in Overall Impact
+            "payback": None,     # hidden in Overall Impact
         },
-        "operational": {"rows": [("Radiologist hours saved / month", f"{total_time_saved_hours:.1f}")]},
-        "waterfall": [("Incremental revenue", 0.0), ("Incremental costs", 0.0), ("Operational value", ops_value_month)],
-        "annual_card": {"incr_rev": 0.0, "incr_costs": 0.0, "ops_value": ops_value_month * 12.0, "net": net_impact_month * 12.0, "roi_pct": float("nan"), "payback": float("nan")},
         "evidence": evidence,
     }
 
 # ---------- Card / HTML builders ----------
-def build_overall_card(title: str, summary_line: str, annual):
-    roi_txt = "" if (isinstance(annual["roi_pct"], float) and math.isnan(annual["roi_pct"])) else f"{annual['roi_pct']*100:.1f}%"
-    payback_txt = "∞" if (annual["payback"] == math.inf) else f"{annual['payback']:.1f}"
+def build_overall_card(title: str, summary_line: str, annual: dict):
+    """Conditional Overall card: shows Financial + Clinical + Operational; hides ROI/Payback if N/A."""
+    rows = []
+    if "incr_rev" in annual:   rows.append(("Incremental revenue (annual)", f"<b>{usd(annual['incr_rev'])}</b>"))
+    if "incr_costs" in annual: rows.append(("Incremental costs (annual)", f"<b class='neg'>{usd(annual['incr_costs'])}</b>"))
+    if "clinical_value" in annual and isinstance(annual["clinical_value"], (int, float)) and annual["clinical_value"] != 0:
+        rows.append(("Clinical value (annual)", f"<b>{usd(annual['clinical_value'])}</b>"))
+    if "ops_value" in annual:  rows.append(("Operational value (annual)", f"<b>{usd(annual['ops_value'])}</b>"))
+    if "net" in annual:        rows.append(("Net impact (annual)", f"<b>{usd(annual['net'])}</b>"))
+
+    roi = annual.get("roi_pct", None)
+    if isinstance(roi, (int, float)) and not (math.isnan(roi) or math.isinf(roi)):
+        rows.append(("ROI %", f"<b>{roi*100:.1f}%</b>"))
+    payback = annual.get("payback", None)
+    if isinstance(payback, (int, float)) and not (math.isnan(payback) or math.isinf(payback)):
+        rows.append(("Months to payback", f"<b>{payback:.1f}</b>"))
+
+    items = "".join(f"<div>{lab}</div><div>{val}</div>" for lab, val in rows)
     return f"""
     <div class='card'>
       <div style='display:flex;justify-content:space-between;align-items:center;margin-bottom:8px'>
@@ -403,14 +455,7 @@ def build_overall_card(title: str, summary_line: str, annual):
         <div class='pill'>Clinical · Financial · Operational</div>
       </div>
       <div style='margin-bottom:8px'>{summary_line}</div>
-      <div class='kpi-grid'>
-        <div>Incremental revenue (annual)</div><div><b>{usd(annual['incr_rev'])}</b></div>
-        <div>Incremental costs (annual)</div><div class='neg'><b>{usd(annual['incr_costs'])}</b></div>
-        <div>Operational value (annual)</div><div><b>{usd(annual['ops_value'])}</b></div>
-        <div class='sep'>Net impact (annual)</div><div class='sep'><b>{usd(annual['net'])}</b></div>
-        <div title="ROI = Net impact ÷ (annual platform + vendor per-case fees)">ROI %</div><div><b>{roi_txt}</b></div>
-        <div title="(Annual AI program cost ÷ net impact per AI case) ÷ monthly AI cases">Months to payback</div><div><b>{payback_txt}</b></div>
-      </div>
+      <div class='kpi-grid'>{items}</div>
     </div>
     """
 
@@ -479,9 +524,7 @@ def build_ui():
   grid-template-columns: repeat(3, minmax(0,1fr));
   gap: 12px;
 }
-@media (max-width: 820px) {
-  #uc [role="radiogroup"] { grid-template-columns: 1fr; }
-}
+@media (max-width: 820px) { #uc [role="radiogroup"] { grid-template-columns: 1fr; } }
 #uc [role="radiogroup"] label {
   border: 1px solid #eef2f7;
   border-radius: 16px;
@@ -701,7 +744,7 @@ def build_ui():
             evidence = f"<div class='card'><div style='font-weight:700;margin-bottom:6px'>Evidence snapshot</div>{res['evidence']}<div class='small-note'>Neutral claims; update with site citations.</div></div>"
             cta = f"<div class='card cta'><div>Want to see this in your workflow?</div><a class='cta-btn' href='{CTA_URL}' target='_blank' rel='noopener'>{CTA_LABEL}</a></div>"
 
-            # CSV export (flatten current rows) -> write to temp file
+            # CSV export -> write to temp file
             fin_rows = [(lab, val) for lab, val in res["financial"]["rows"]]
             clin_rows = [(lab, val) for lab, val in res["clinical"]["rows"]]
             op_rows   = [(lab, val) for lab, val in res["operational"]["rows"]]