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app.py

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+import joblib
+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"])
+
+# ---------------- Output formatting ----------------
+def format_output(lang, label, proba):
+    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।"
+        )
+    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([{
+        "Age": float(age),
+        "Gender": gender,
+        "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),
+        "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)")
+
+    cycle = gr.Dropdown(["G", "H", "I", "J"], value="J", label="NHANES Cycle")
+
+    btn = gr.Button("Predict / ফলাফল দেখুন")
+    out_md = gr.Markdown()
+    out_speak = gr.HTML()
+
+    btn.click(
+        predict,
+        inputs=[lang, age, gender, bmi, bp_sys, bp_dia, phys, dpq, smoking, alcohol, diabetes, cycle],
+        outputs=[out_md, out_speak]
+    )
+
+demo.launch()

best_model_v2_calibrated.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:53ef30d0d2726ffdee40a15bbd4ac1f412f724010442586e119094491ca8b0c9
+size 3936773

requirements.txt

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+gradio
+pandas
+numpy
+scikit-learn
+joblib
+xgboost