Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import traceback
+
+from inference import (
+    FEATURE_NAMES,
+    REPORTING_OUTCOMES,
+    OUTCOME_DESCRIPTIONS,
+    OUTCOMES,
+    SHAP_OUTCOMES,
+    predict_with_comparison,
+    create_all_shap_plots,
+    create_all_pie_charts,
+)
+
+
+# ---------------------------------------------------------------------------
+# Choice lists
+# ---------------------------------------------------------------------------
+
+AGEGPFF_CHOICES   = ["<=10", "11-17", "18-29", "30-49", ">=50"]
+SEX_CHOICES       = ["Male", "Female"]
+KPS_CHOICES       = ["<90", "≥ 90"]
+DONORF_CHOICES    = [
+    "HLA identical sibling",
+    "HLA mismatch relative",
+    "Matched unrelated donor",
+    "Mismatched unrelated donor or cord blood",
+]
+GRAFTYPE_CHOICES  = ["Bone marrow", "Peripheral blood", "Cord blood"]
+CONDGRPF_CHOICES  = ["MAC", "RIC", "NMA"]
+CONDGRP_FINAL_CHOICES = [
+    "TBI/Cy", "TBI/Cy/Flu", "TBI/Cy/Flu/TT", "TBI/Mel", "TBI/Flu",
+    "TBI alone (300/400/600cGy)", "Bu/Cy", "Bu/Mel", "Flu/Bu/TT",
+    "Flu/Bu", "Flu/Mel/TT", "Flu/Mel", "Cy/Flu", "Treosulfan",
+    "Cy alone", "Flud", "TLI",
+]
+ATGF_CHOICES      = ["ATG", "Alemtuzumab", "None"]
+GVHD_FINAL_CHOICES = [
+    "Ex-vivo T-cell depletion", "CD34 selection", "Post-CY + siro +- MMF",
+    "Post-CY + MMF + CNI", "CNI + MMF", "CNI + MTX", "CNI alone",
+    "CNI + siro", "Siro alone", "MMF + MTX", "MMF + siro", "MMF alone",
+    "MTX alone", "MTX + siro",
+]
+HLA_FINAL_CHOICES = ["8/8", "7/8", "≤ 6/8"]
+RCMVPR_CHOICES    = ["Negative", "Positive"]
+EXCHTFPR_CHOICES  = ["No", "Yes"]
+VOC2YPR_CHOICES   = ["No", "Yes"]
+VOCFRQPR_CHOICES  = ["< 3/yr", "≥ 3/yr"]
+SCATXRSN_CHOICES  = [
+    "CNS event", "Acute chest Syndrome", "Recurrent vaso-occlusive pain",
+    "Recurrent priapism", "Excessive transfusion requirements/iron overload",
+    "Cardio-pulmonary", "Chronic transfusion", "Asymptomatic",
+    "Renal insufficiency", "Splenic sequestration", "Avascular necrosis",
+    "Hodgkin lymphoma",
+]
+
+
+# ---------------------------------------------------------------------------
+# Grouped published-regimen dropdown
+# ---------------------------------------------------------------------------
+
+GROUPED_REGIMEN_CHOICES = [
+    ("── HLA IDENTICAL ──",                        "__header_hla_identical__"),
+    ("Hsieh et al 2014",                           "Hsieh et al 2014"),
+    ("Krishnamurti et al 2019",                    "Krishnamurti et al 2019"),
+    ("King et al 2015",                            "King et al 2015"),
+    ("Walters et al 1996",                         "Walters et al 1996"),
+    ("── HLA MISMATCHED ──",                       "__header_hla_mismatched__"),
+    ("Bolanos-Meade et al 2022 (HLA Mismatch)",    "Bolanos-Meade et al 2022 (HLA Mismatch)"),
+    ("Patel et al 2020 (HLA Mismatch)",            "Patel et al 2020 (HLA Mismatch)"),
+    ("── MATCHED UNRELATED ──",                    "__header_matched_unrelated__"),
+    ("L Krishnamurti et al 2019",                  "L Krishnamurti et al 2019"),
+    ("Shenoy et al 2016",                          "Shenoy et al 2016"),
+    ("── MISMATCHED UNRELATED / CORD BLOOD ──",    "__header_mismatched_cord__"),
+    ("Bolanos-Meade et al 2022 (Mismatched/Cord)", "Bolanos-Meade et al 2022 (Mismatched/Cord)"),
+    ("Patel et al 2020 (Mismatched/Cord)",         "Patel et al 2020 (Mismatched/Cord)"),
+]
+
+HEADER_VALUES = {v for _, v in GROUPED_REGIMEN_CHOICES if v.startswith("__header_")}
+
+PUBLISHED_PRESETS = {
+    # HLA Identical Sibling
+    "Hsieh et al 2014": {
+        "CONDGRPF": "NMA", "CONDGRP_FINAL": "TBI alone (300/400/600cGy)",
+        "ATGF": "Alemtuzumab", "GVHD_FINAL": "Siro alone",
+        "HLA_FINAL": "8/8", "DONORF": "HLA identical sibling",
+    },
+    "Krishnamurti et al 2019": {
+        "CONDGRPF": "MAC", "CONDGRP_FINAL": "Flu/Bu",
+        "ATGF": "ATG", "GVHD_FINAL": "CNI + MTX",
+        "HLA_FINAL": "8/8", "DONORF": "HLA identical sibling",
+    },
+    "King et al 2015": {
+        "CONDGRPF": "RIC", "CONDGRP_FINAL": "Flu/Mel",
+        "ATGF": "Alemtuzumab", "GVHD_FINAL": "CNI + MTX",
+        "HLA_FINAL": "8/8", "DONORF": "HLA identical sibling",
+    },
+    "Walters et al 1996": {
+        "CONDGRPF": "MAC", "CONDGRP_FINAL": "Bu/Cy",
+        "ATGF": "ATG", "GVHD_FINAL": "CNI + MTX",
+        "HLA_FINAL": "8/8", "DONORF": "HLA identical sibling",
+    },
+    # HLA Mismatch Relative
+    "Bolanos-Meade et al 2022 (HLA Mismatch)": {
+        "CONDGRPF": "NMA", "CONDGRP_FINAL": "TBI/Cy/Flu",
+        "ATGF": "ATG", "GVHD_FINAL": "Post-CY + siro +- MMF",
+        "HLA_FINAL": "7/8", "DONORF": "HLA mismatch relative",
+    },
+    "Patel et al 2020 (HLA Mismatch)": {
+        "CONDGRPF": "NMA", "CONDGRP_FINAL": "TBI/Cy/Flu/TT",
+        "ATGF": "ATG", "GVHD_FINAL": "Post-CY + siro +- MMF",
+        "HLA_FINAL": "7/8", "DONORF": "HLA mismatch relative",
+    },
+    # Matched Unrelated Donor
+    "L Krishnamurti et al 2019": {
+        "CONDGRPF": "MAC", "CONDGRP_FINAL": "Flu/Bu",
+        "ATGF": "ATG", "GVHD_FINAL": "CNI + MTX",
+        "HLA_FINAL": "8/8", "DONORF": "Matched unrelated donor",
+    },
+    "Shenoy et al 2016": {
+        "CONDGRPF": "RIC", "CONDGRP_FINAL": "Flu/Mel",
+        "ATGF": "Alemtuzumab", "GVHD_FINAL": "CNI + MTX",
+        "HLA_FINAL": "8/8", "DONORF": "Matched unrelated donor",
+    },
+    # Mismatched Unrelated Donor or Cord Blood
+    "Bolanos-Meade et al 2022 (Mismatched/Cord)": {
+        "CONDGRPF": "NMA", "CONDGRP_FINAL": "TBI/Cy/Flu",
+        "ATGF": "ATG", "GVHD_FINAL": "Post-CY + siro +- MMF",
+        "HLA_FINAL": "7/8", "DONORF": "Mismatched unrelated donor or cord blood",
+    },
+    "Patel et al 2020 (Mismatched/Cord)": {
+        "CONDGRPF": "NMA", "CONDGRP_FINAL": "TBI/Cy/Flu/TT",
+        "ATGF": "ATG", "GVHD_FINAL": "Post-CY + siro +- MMF",
+        "HLA_FINAL": "7/8", "DONORF": "Mismatched unrelated donor or cord blood",
+    },
+}
+
+
+# ---------------------------------------------------------------------------
+# Feature groupings
+# ---------------------------------------------------------------------------
+
+PATIENT_FEATURES = ["AGE", "AGEGPFF", "SEX", "KPS", "RCMVPR"]
+DONOR_FEATURES   = ["DONORF", "GRAFTYPE", "HLA_FINAL",
+                    "CONDGRPF", "CONDGRP_FINAL", "ATGF", "GVHD_FINAL"]
+DISEASE_FEATURES = ["NACS2YR", "EXCHTFPR", "VOC2YPR", "VOCFRQPR", "SCATXRSN"]
+ALL_FEATURES     = PATIENT_FEATURES + DONOR_FEATURES + DISEASE_FEATURES
+
+
+# ---------------------------------------------------------------------------
+# Utility callbacks
+# ---------------------------------------------------------------------------
+
+def get_age_group(age):
+    if age is None or age == "":
+        return ""
+    try:
+        age = float(age)
+        if age <= 10:
+            return "<=10"
+        elif age <= 17:
+            return "11-17"
+        elif age <= 29:
+            return "18-29"
+        elif age <= 49:
+            return "30-49"
+        else:
+            return ">=50"
+    except (ValueError, TypeError):
+        return ""
+
+
+def vocfrqpr_from_voc2ypr(voc_status):
+    if voc_status == "No":
+        return gr.update(value="< 3/yr", interactive=False)
+    else:
+        return gr.update(value=None, interactive=True)
+
+
+def apply_grouped_preset(selected_value):
+    # Header row clicked — reset dropdown, leave all fields unchanged
+    if not selected_value or selected_value in HEADER_VALUES:
+        return [gr.update(value=None)] + [gr.update()] * 6
+
+    preset = PUBLISHED_PRESETS.get(selected_value)
+    if not preset:
+        return [gr.update()] * 7
+
+    return [
+        gr.update(),                             # leave dropdown showing selection
+        gr.update(value=preset["DONORF"]),
+        gr.update(value=preset["CONDGRPF"]),
+        gr.update(value=preset["CONDGRP_FINAL"]),
+        gr.update(value=preset["ATGF"]),
+        gr.update(value=preset["GVHD_FINAL"]),
+        gr.update(value=preset["HLA_FINAL"]),
+    ]
+
+
+# ---------------------------------------------------------------------------
+# Component factory
+# ---------------------------------------------------------------------------
+
+def make_component(name: str):
+    if name == "AGE":
+        return gr.Number(label="Age at transplant (years)", minimum=0, maximum=120)
+    elif name == "AGEGPFF":
+        return gr.Textbox(label="Age group (Auto-filled)", interactive=False)
+    elif name == "NACS2YR":
+        return gr.Number(
+            label="Number of Acute Chest Syndromes within 2 years pre-HCT",
+            minimum=0,
+        )
+    elif name == "SEX":
+        return gr.Dropdown(SEX_CHOICES, label="Sex")
+    elif name == "KPS":
+        return gr.Dropdown(KPS_CHOICES, label="Karnofsky/Lansky Performance Score at HCT")
+    elif name == "DONORF":
+        return gr.Dropdown(DONORF_CHOICES, label="Donor type")
+    elif name == "GRAFTYPE":
+        return gr.Dropdown(GRAFTYPE_CHOICES, label="Graft type")
+    elif name == "CONDGRPF":
+        return gr.Dropdown(CONDGRPF_CHOICES, label="Conditioning intensity")
+    elif name == "CONDGRP_FINAL":
+        return gr.Dropdown(CONDGRP_FINAL_CHOICES, label="Conditioning Regimen")
+    elif name == "ATGF":
+        return gr.Dropdown(ATGF_CHOICES, label="Serotherapy")
+    elif name == "GVHD_FINAL":
+        return gr.Dropdown(GVHD_FINAL_CHOICES, label="GVHD Prophylaxis")
+    elif name == "HLA_FINAL":
+        return gr.Dropdown(HLA_FINAL_CHOICES, label="Donor-Recipient HLA Matching")
+    elif name == "RCMVPR":
+        return gr.Dropdown(RCMVPR_CHOICES, label="Recipient CMV serostatus")
+    elif name == "EXCHTFPR":
+        return gr.Dropdown(EXCHTFPR_CHOICES, label="Exchange transfusion required?")
+    elif name == "VOC2YPR":
+        return gr.Dropdown(
+            VOC2YPR_CHOICES,
+            label="VOC requiring hospitalization within 2 years pre-HCT?",
+        )
+    elif name == "VOCFRQPR":
+        return gr.Dropdown(VOCFRQPR_CHOICES, label="Frequency of VOC hospitalizations")
+    elif name == "SCATXRSN":
+        return gr.Dropdown(SCATXRSN_CHOICES, label="Reason for Transplant")
+    else:
+        return gr.Textbox(label=name)
+
+
+# ---------------------------------------------------------------------------
+# Prediction callback
+# ---------------------------------------------------------------------------
+
+def predict_gradio(*values):
+    try:
+        user_vals = {f: v for f, v in zip(ALL_FEATURES, values)}
+
+        missing = []
+        for f, v in user_vals.items():
+            if v is None or v == "" or (isinstance(v, float) and pd.isna(v)):
+                missing.append(f)
+        if missing:
+            raise ValueError(
+                f"Please fill in all fields before predicting.\nMissing: {', '.join(missing)}"
+            )
+
+        calibrated, uncalibrated = predict_with_comparison(user_vals)
+        calibrated_probs, calibrated_intervals = calibrated
+
+        rows = []
+        for outcome in REPORTING_OUTCOMES:
+            desc       = OUTCOME_DESCRIPTIONS[outcome]
+            calib_prob = calibrated_probs[outcome]
+            ci_low_c, ci_high_c = calibrated_intervals[outcome]
+            rows.append({
+                "Outcome":     desc,
+                "Probability": f"{calib_prob * 100:.1f}%",
+                "95% CI":      f"[{ci_low_c * 100:.1f}% - {ci_high_c * 100:.1f}%]",
+            })
+        df = pd.DataFrame(rows)
+
+        shap_plots = create_all_shap_plots(user_vals, max_display=10)
+        pie_charts = create_all_pie_charts(calibrated_probs)
+
+        return (
+            df,
+            pie_charts["DEAD"],
+            pie_charts["GF"],
+            pie_charts["AGVHD"],
+            pie_charts["CGVHD"],
+            pie_charts["VOCPSHI"],
+            pie_charts["STROKEHI"],
+            shap_plots["DEAD"],
+            shap_plots["GF"],
+            shap_plots["AGVHD"],
+            shap_plots["CGVHD"],
+            shap_plots["VOCPSHI"],
+            shap_plots["EFS"],
+            shap_plots["STROKEHI"],
+            shap_plots["OS"],
+        )
+
+    except Exception as e:
+        tb = traceback.format_exc()
+        print("=" * 60)
+        print("ERROR IN predict_gradio:")
+        print(tb)
+        print("=" * 60)
+        raise gr.Error(f"{type(e).__name__}: {str(e)}\n\nSee terminal for full traceback.")
+
+
+# ---------------------------------------------------------------------------
+# CSS
+# ---------------------------------------------------------------------------
+
+custom_css = """
+.predict-button {
+    background: linear-gradient(to right, #ff6b35, #ff8c42) !important;
+    border: none !important;
+    color: white !important;
+    font-weight: bold !important;
+    font-size: 16px !important;
+    padding: 12px !important;
+}
+.predict-button:hover {
+    background: linear-gradient(to right, #ff5722, #ff7b29) !important;
+}
+"""
+
+# ---------------------------------------------------------------------------
+# Gradio UI
+# ---------------------------------------------------------------------------
+
+with gr.Blocks(title="HCT Outcome Prediction Model") as demo:
+    gr.Markdown(
+        """
+        # HCT Outcome Prediction Model
+
+        Enter patient, transplant, and disease characteristics to predict outcomes.
+        """
+    )
+
+    inputs_dict = {}
+
+    with gr.Row():
+        # ── Patient Characteristics ──────────────────────────────────────
+        with gr.Column(scale=1):
+            gr.Markdown("### Patient Characteristics")
+            for f in PATIENT_FEATURES:
+                inputs_dict[f] = make_component(f)
+
+        # ── Transplant Characteristics ───────────────────────────────────
+        with gr.Column(scale=1):
+            gr.Markdown("### Transplant Characteristics")
+
+            grouped_regimen_dropdown = gr.Dropdown(
+                choices=GROUPED_REGIMEN_CHOICES,
+                value=None,
+                label="Published conditioning regimen",
+                info="Auto-fills Donor Type, Conditioning Intensity, Conditioning Regimen, "
+                     "Serotherapy and GVHD Prophylaxis",
+            )
+
+            donorf_comp   = inputs_dict["DONORF"]        = make_component("DONORF")
+            inputs_dict["GRAFTYPE"]                      = make_component("GRAFTYPE")
+            condgrpf      = inputs_dict["CONDGRPF"]      = make_component("CONDGRPF")
+            condgrp_final = inputs_dict["CONDGRP_FINAL"] = make_component("CONDGRP_FINAL")
+            atgf          = inputs_dict["ATGF"]          = make_component("ATGF")
+            gvhd_final    = inputs_dict["GVHD_FINAL"]    = make_component("GVHD_FINAL")
+            hla_final     = inputs_dict["HLA_FINAL"]     = make_component("HLA_FINAL")
+
+        # ── Disease Characteristics ──────────────────────────────────────
+        with gr.Column(scale=1):
+            gr.Markdown("### Disease Characteristics")
+            for f in DISEASE_FEATURES:
+                inputs_dict[f] = make_component(f)
+
+    # ── Reactive callbacks ───────────────────────────────────────────────
+    inputs_dict["AGE"].change(
+        fn=get_age_group,
+        inputs=inputs_dict["AGE"],
+        outputs=inputs_dict["AGEGPFF"],
+    )
+
+    inputs_dict["VOC2YPR"].change(
+        fn=vocfrqpr_from_voc2ypr,
+        inputs=inputs_dict["VOC2YPR"],
+        outputs=inputs_dict["VOCFRQPR"],
+    )
+
+    # outputs[0] is the dropdown itself so clicking a header resets it to None
+    grouped_regimen_dropdown.change(
+        fn=apply_grouped_preset,
+        inputs=grouped_regimen_dropdown,
+        outputs=[
+            grouped_regimen_dropdown,
+            donorf_comp, condgrpf, condgrp_final, atgf, gvhd_final, hla_final,
+        ],
+    )
+
+    inputs_list = [inputs_dict[f] for f in ALL_FEATURES]
+
+    btn = gr.Button("Predict", elem_classes="predict-button", size="lg")
+
+    gr.Markdown("---")
+    gr.Markdown("## Prediction Results")
+    gr.Markdown("### Predicted Outcomes")
+
+    with gr.Column():
+        output_table = gr.Dataframe(
+            headers=["Outcome", "Probability", "95% CI"],
+            label="",
+            row_count=(len(REPORTING_OUTCOMES), "dynamic"),
+            col_count=(3, "fixed"),
+        )
+
+    gr.Markdown("---")
+    gr.Markdown("## Pie Charts")
+
+    with gr.Row():
+        with gr.Column():
+            pie_dead    = gr.Plot(label="Death")
+        with gr.Column():
+            pie_gf      = gr.Plot(label="Graft Failure")
+        with gr.Column():
+            pie_agvhd   = gr.Plot(label="Acute Graft-versus-Host Disease")
+
+    with gr.Row():
+        with gr.Column():
+            pie_cgvhd   = gr.Plot(label="Chronic Graft-versus-Host Disease")
+        with gr.Column():
+            pie_vocpshi = gr.Plot(label="Vaso-Occlusive Crisis Post-HCT")
+        with gr.Column():
+            pie_stroke  = gr.Plot(label="Stroke Post-HCT")
+
+    gr.Markdown("---")
+    gr.Markdown("## SHAP - Feature Importance")
+
+    with gr.Row():
+        with gr.Column():
+            shap_dead   = gr.Plot(label="Death")
+        with gr.Column():
+            shap_gf     = gr.Plot(label="Graft Failure")
+        with gr.Column():
+            shap_agvhd  = gr.Plot(label="Acute Graft-versus-Host Disease")
+        with gr.Column():
+            shap_cgvhd  = gr.Plot(label="Chronic Graft-versus-Host Disease")
+
+    with gr.Row():
+        with gr.Column():
+            shap_vocpshi = gr.Plot(label="Vaso-Occlusive Crisis Post-HCT")
+        with gr.Column():
+            shap_efs     = gr.Plot(label="Event-Free Survival")
+        with gr.Column():
+            shap_stroke  = gr.Plot(label="Stroke Post-HCT")
+        with gr.Column():
+            shap_os      = gr.Plot(label="Overall Survival")
+
+    btn.click(
+        fn=predict_gradio,
+        inputs=inputs_list,
+        outputs=[
+            output_table,
+            pie_dead, pie_gf, pie_agvhd, pie_cgvhd, pie_vocpshi, pie_stroke,
+            shap_dead, shap_gf, shap_agvhd, shap_cgvhd,
+            shap_vocpshi, shap_efs, shap_stroke, shap_os,
+        ],
+    )
+
+
+if __name__ == "__main__":
+    demo.launch(ssr_mode=False, css=custom_css)