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app.py

@@ -1,106 +1,184 @@
 import joblib
+import numpy as np
 import pandas as pd
-import gradio as gr
-
-# ---------------- Load model bundle ----------------
-bundle = joblib.load("best_model_v2_calibrated.joblib")
-model = bundle["model"]
-threshold = float(bundle["threshold"])
+import streamlit as st
+
+# =========================
+# CONFIG / CONSTANTS
+# =========================
+
+# These thresholds decide the UI label.
+# You can change them anytime.
+TH_LOW = 0.20   # below this => Low Risk
+TH_HIGH = 0.40  # above this => High Risk
+# between TH_LOW and TH_HIGH => Moderate Risk
+
+MODEL_PATH = "best_model_v2_calibrated.joblib"
+
+FEATURE_ORDER = [
+    "Age",
+    "Gender",
+    "BMI",
+    "BP_SYS",
+    "BP_DIA",
+    "Phys_Activity_Days",
+    "DPQ_Score",
+    "Smoking_Indicator",
+    "Alcohol_Feature",
+    "Diabetes_Indicator",
+    "Cycle",
+]
+
+# =========================
+# HELPERS
+# =========================
+
+def label_from_probability(prob: float) -> str:
+    """3-level risk label based on thresholds."""
+    if prob < TH_LOW:
+        return "Low Risk"
+    elif prob < TH_HIGH:
+        return "Moderate Risk"
+    else:
+        return "High Risk"
 
-# ---------------- Output formatting ----------------
-def format_output(lang, label, proba):
+def get_texts(lang: str):
+    """Simple bilingual text dictionary."""
     if lang == "বাংলা":
-        risk_text = "উচ্চ ঝুঁকি" if label == "High Risk" else "কম ঝুঁকি"
-        return (
-            f"### ফলাফল: **{risk_text}**\n"
-            f"- ঝুঁকির সম্ভাবনা (Probability): **{proba:.3f}**\n"
-            f"- Threshold: **{threshold:.2f}**\n\n"
-            "⚠️ এটি একটি **স্ক্রিনিং টুল**, চিকিৎসা নির্ণয় নয়। সমস্যা থাকলে ডাক্তারের পরামর্শ নিন।"
-        )
+        return {
+            "title": "SleepGuardAI – Sleep Risk Screening (NHANES-based)",
+            "subtitle": "ফর্ম পূরণ করুন → ঝুঁকির স্কোর দেখুন। (এটি মেডিকেল ডায়াগনসিস নয়)",
+            "predict_btn": "Predict / ফলাফল দেখুন",
+            "result": "ফলাফল",
+            "prob": "ঝুঁকি সম্ভাবনা",
+            "thresholds": "থ্রেশহোল্ড",
+            "warning": "⚠️ এটি একটি স্ক্রিনিং টুল, মেডিকেল ডায়াগনসিস নয়। লক্ষণ থাকলে ডাক্তার দেখান।",
+            "low": "কম ঝুঁকি",
+            "mod": "মাঝারি ঝুঁকি",
+            "high": "উচ্চ ঝুঁকি",
+        }
     else:
-        return (
-            f"### Result: **{label}**\n"
-            f"- Risk probability: **{proba:.3f}**\n"
-            f"- Threshold: **{threshold:.2f}**\n\n"
-            "⚠️ This is a **screening tool**, not a medical diagnosis. If you have symptoms, consult a clinician."
-        )
-
-def web_speak_html(text, lang):
-    voice_lang = "bn-BD" if lang == "বাংলা" else "en-US"
-    safe = text.replace("`", "").replace("\\", "\\\\").replace("'", "\\'")
-    return f"""
-    <div style="display:flex;gap:10px;align-items:center;">
-      <button onclick="(function(){{
-        const msg = new SpeechSynthesisUtterance('{safe}');
-        msg.lang = '{voice_lang}';
-        window.speechSynthesis.cancel();
-        window.speechSynthesis.speak(msg);
-      }})()" style="padding:10px 14px;border-radius:10px;border:1px solid #ccc;cursor:pointer;">
-        🔊 Speak / শোনান
-      </button>
-      <span style="opacity:0.7;">(Voice depends on browser installed voices)</span>
-    </div>
-    """
-
-# ---------------- Prediction ----------------
-def predict(lang, age, gender, bmi, bp_sys, bp_dia, phys_days, dpq, smoking, alcohol, diabetes, cycle):
-    sample = pd.DataFrame([{
+        return {
+            "title": "SleepGuardAI – Sleep Risk Screening (NHANES-based)",
+            "subtitle": "Fill the form → get a risk score. (This is NOT a medical diagnosis)",
+            "predict_btn": "Predict / ফলাফল দেখুন",
+            "result": "Result",
+            "prob": "Risk probability",
+            "thresholds": "Thresholds",
+            "warning": "⚠️ This is a screening tool, not a medical diagnosis. If you have symptoms, consult a clinician.",
+            "low": "Low Risk",
+            "mod": "Moderate Risk",
+            "high": "High Risk",
+        }
+
+@st.cache_resource
+def load_model():
+    return joblib.load(MODEL_PATH)
+
+def build_feature_row(
+    age, gender, bmi, bp_sys, bp_dia, phys_days, dpq, smoking, alcohol, diabetes, cycle
+) -> pd.DataFrame:
+    """Build one-row DataFrame in the exact feature order used by training."""
+    row = {
         "Age": float(age),
-        "Gender": gender,
+        "Gender": str(gender),  # keep as string (your pipeline encodes it)
         "BMI": float(bmi),
         "BP_SYS": float(bp_sys),
         "BP_DIA": float(bp_dia),
         "Phys_Activity_Days": float(phys_days),
         "DPQ_Score": float(dpq),
-        "Smoking_Indicator": float(smoking),
+        "Smoking_Indicator": int(smoking),
         "Alcohol_Feature": float(alcohol),
-        "Diabetes_Indicator": float(diabetes),
-        "Cycle": cycle
-    }])
-
-    proba = float(model.predict_proba(sample)[:, 1][0])
-    pred = int(proba >= threshold)
-    label = "High Risk" if pred == 1 else "Low Risk"
-
-    md = format_output(lang, label, proba)
-    speak = web_speak_html(md, lang)
-    return md, speak
-
-# ---------------- UI ----------------
-with gr.Blocks(title="SleepGuardAI – Sleep Risk Screening") as demo:
-    gr.Markdown("# SleepGuardAI – Sleep Risk Screening (NHANES-based)")
-    gr.Markdown("Fill the form → get risk score. Bilingual output + Speak button included.")
-
-    lang = gr.Radio(["English", "বাংলা"], value="English", label="Language / ভাষা")
-
-    with gr.Row():
-        age = gr.Slider(10, 90, value=30, label="Age / বয়স")
-        gender = gr.Dropdown(["Male", "Female"], value="Male", label="Gender")
-
-    with gr.Row():
-        bmi = gr.Slider(10, 50, value=25, label="BMI")
-        bp_sys = gr.Slider(80, 220, value=120, label="Systolic BP")
-        bp_dia = gr.Slider(40, 140, value=80, label="Diastolic BP")
-
-    phys = gr.Slider(0, 7, value=3, step=1, label="Physical Activity Days/Week")
-
-    gr.Markdown("### Optional (improves accuracy if known)")
-    with gr.Row():
-        dpq = gr.Slider(0, 27, value=0, step=1, label="DPQ Depression Score (0–27)")
-        smoking = gr.Dropdown([1, 2], value=2, label="Smoking (1=Yes, 2=No)")
-        diabetes = gr.Dropdown([1, 2], value=2, label="Diabetes (1=Yes, 2=No)")
-        alcohol = gr.Slider(0, 30, value=0, step=1, label="Alcohol feature (proxy)")
+        "Diabetes_Indicator": int(diabetes),
+        "Cycle": str(cycle),
+    }
+    df = pd.DataFrame([row])[FEATURE_ORDER]
+    return df
+
+# =========================
+# UI
+# =========================
+
+st.set_page_config(page_title="SleepGuardAI", layout="wide")
+
+lang = st.radio("Language / ভাষা", ["English", "বাংলা"], horizontal=True)
+T = get_texts(lang)
+
+st.title(T["title"])
+st.caption(T["subtitle"])
+
+# Load model
+try:
+    bundle = load_model()
+except FileNotFoundError:
+    st.error(
+        f"Model file not found: '{MODEL_PATH}'. Put it in the same folder as app.py."
+    )
+    st.stop()
+
+# We support two possible save formats:
+# 1) bundle is a dict with {"model":..., "calibrator":..., ...}
+# 2) bundle is directly a sklearn Pipeline/Calibrated model with predict_proba
+model = bundle.get("model") if isinstance(bundle, dict) and "model" in bundle else bundle
+calibrator = bundle.get("calibrator") if isinstance(bundle, dict) and "calibrator" in bundle else None
+
+colA, colB = st.columns(2)
+
+with colA:
+    age = st.slider("Age / বয়স", 10, 90, 30)
+    gender = st.selectbox("Gender", ["Male", "Female"])
+
+    bmi = st.slider("BMI", 10.0, 50.0, 25.0, 0.1)
+    bp_sys = st.slider("Systolic BP", 80, 220, 120)
+    bp_dia = st.slider("Diastolic BP", 40, 140, 80)
+
+    phys_days = st.slider("Physical Activity Days/Week", 0, 7, 3)
+
+with colB:
+    st.markdown("### Optional (improves accuracy if known)")
+    dpq = st.slider("DPQ Depression Score (0–27)", 0, 27, 5)
+    smoking = st.selectbox("Smoking (1=Yes, 2=No)", [2, 1], index=0)
+    diabetes = st.selectbox("Diabetes (1=Yes, 2=No)", [2, 1], index=0)
+    alcohol = st.slider("Alcohol feature (proxy)", 0.0, 30.0, 2.0, 0.5)
+    cycle = st.selectbox("NHANES Cycle", ["G", "H", "I", "J"])
+
+st.divider()
+
+if st.button(T["predict_btn"], use_container_width=True):
+    X = build_feature_row(age, gender, bmi, bp_sys, bp_dia, phys_days, dpq, smoking, alcohol, diabetes, cycle)
+
+    # Get probability
+    # If you saved a separate calibrator, run model -> probs -> calibrator
+    # Otherwise assume model already outputs calibrated proba.
+    try:
+        raw_proba = model.predict_proba(X)[:, 1]
+    except Exception as e:
+        st.error(f"Model could not compute predict_proba. Error: {e}")
+        st.stop()
+
+    if calibrator is not None:
+        # calibrator usually expects 2D array
+        prob = float(calibrator.predict(raw_proba.reshape(-1, 1))[0])
+    else:
+        prob = float(raw_proba[0])
 
-    cycle = gr.Dropdown(["G", "H", "I", "J"], value="J", label="NHANES Cycle")
+    label = label_from_probability(prob)
 
-    btn = gr.Button("Predict / ফলাফল দেখুন")
-    out_md = gr.Markdown()
-    out_speak = gr.HTML()
+    # Display with bilingual mapping
+    if label == "Low Risk":
+        pretty_label = T["low"]
+        st.success(f"{T['result']}: {pretty_label}")
+    elif label == "Moderate Risk":
+        pretty_label = T["mod"]
+        st.warning(f"{T['result']}: {pretty_label}")
+    else:
+        pretty_label = T["high"]
+        st.error(f"{T['result']}: {pretty_label}")
 
-    btn.click(
-        predict,
-        inputs=[lang, age, gender, bmi, bp_sys, bp_dia, phys, dpq, smoking, alcohol, diabetes, cycle],
-        outputs=[out_md, out_speak]
-    )
+    st.write(f"**{T['prob']}:** {prob:.3f}")
+    st.write(f"**{T['thresholds']}:** Low<{TH_LOW:.2f} | Moderate {TH_LOW:.2f}–{TH_HIGH:.2f} | High≥{TH_HIGH:.2f}")
+    st.caption(T["warning"])
 
-demo.launch()
+    # Debug view if you want to show professor:
+    with st.expander("Show input feature row (for debugging/paper)"):
+        st.dataframe(X)

find_threshold_recall.py

@@ -0,0 +1,79 @@
+import joblib
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import recall_score, fbeta_score, roc_auc_score
+
+MODEL_PATH = "best_model_v2_calibrated.joblib"
+CSV_PATH = "nhanes_sleep_extended_v2.csv"
+
+TARGET = "Sleep_Risk"
+
+FEATURES = [
+    "Age",
+    "Gender",
+    "BMI",
+    "BP_SYS",
+    "BP_DIA",
+    "Phys_Activity_Days",
+    "DPQ_Score",
+    "Smoking_Indicator",
+    "Alcohol_Feature",
+    "Diabetes_Indicator",
+    "Cycle",
+]
+
+def main():
+    df = pd.read_csv(CSV_PATH)
+
+    # drop rows with missing values in required columns
+    df = df.dropna(subset=FEATURES + [TARGET]).copy()
+
+    X = df[FEATURES]
+    y = df[TARGET].astype(int)
+
+    # Train/test split (keep same logic as training if possible)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.20, random_state=42, stratify=y
+    )
+
+    bundle = joblib.load(MODEL_PATH)
+    model = bundle.get("model") if isinstance(bundle, dict) and "model" in bundle else bundle
+    calibrator = bundle.get("calibrator") if isinstance(bundle, dict) and "calibrator" in bundle else None
+
+    # Get probabilities
+    p_test_raw = model.predict_proba(X_test)[:, 1]
+    if calibrator is not None:
+        p_test = calibrator.predict(p_test_raw.reshape(-1, 1))
+    else:
+        p_test = p_test_raw
+
+    # AUC (threshold-free)
+    auc = roc_auc_score(y_test, p_test)
+    print(f"TEST AUC: {auc:.4f}")
+
+    # Sweep thresholds
+    thresholds = np.round(np.arange(0.05, 0.90, 0.01), 2)
+
+    best_f2 = (-1, None)
+    th_recall_085 = None
+
+    for th in thresholds:
+        pred = (p_test >= th).astype(int)
+        rec = recall_score(y_test, pred)
+        f2 = fbeta_score(y_test, pred, beta=2)
+
+        if f2 > best_f2[0]:
+            best_f2 = (f2, th)
+
+        if th_recall_085 is None and rec >= 0.85:
+            th_recall_085 = th
+
+    print(f"Best threshold by F2: {best_f2[1]} (F2={best_f2[0]:.4f})")
+    if th_recall_085 is not None:
+        print(f"Threshold achieving recall ≥ 0.85 : {th_recall_085}")
+    else:
+        print("No threshold achieved recall ≥ 0.85 in tested range.")
+
+if __name__ == "__main__":
+    main()