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app.py

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+# ======================================================================
+# PREDICTIVE INSIGHTS INTO CHILD MARRIAGE
+# Academic UI (Times-style font)
+# Bilingual UI (English + Bangla)
+# Target: early_marriage_num (0 = No, 1 = Yes)
+# Age feature is intentionally excluded
+# ======================================================================
+
+import warnings
+warnings.filterwarnings("ignore")
+
+import numpy as np
+import pandas as pd
+import gradio as gr
+import joblib
+import os
+
+# ======================================================
+# MODEL PATH
+# ======================================================
+MODEL_PATH = "early_marriage_stack_classifier.pkl"
+
+# ======================================================
+# FEATURE ORDER (DO NOT CHANGE)
+# ======================================================
+FEATURE_COLUMNS = [
+    "Region",
+    "No_mem",
+    "Income_monthly",
+    "Expend_monthly",
+    "Ed_father",
+    "Ed_mother",
+    "Ed_vict",
+    "parent_early_marriage",
+    "Past_histroy",
+    "Instablity_num",
+    "Female_working",
+    "Current_Situation",
+    "Social_inc_num",
+    "mentality_about_girl_marriage",
+    "mentality_about_boy_marriage",
+    "Financial_support_num",
+]
+
+# ======================================================
+# REGION (LABEL → VALUE)
+# ======================================================
+REGION_MAP = {
+    "Naogaon (নওগাঁ)": 1,
+    "Mymensingh (ময়মনসিংহ)": 2,
+    "Bhola (ভোলা)": 3,
+    "Cumilla (কুমিল্লা)": 4,
+    "Munshiganj (মুন্সিগঞ্জ)": 5,
+}
+
+# ======================================================
+# EDUCATION (LABEL → VALUE)
+# ======================================================
+EDUCATION_MAP = {
+    "Illiterate (নিরক্ষর)": 0,
+    "Primary – Class 1 (প্রাথমিক – ১ম শ্রেণি)": 1,
+    "Primary – Class 2 (প্রাথমিক – ২য় শ্রেণি)": 2,
+    "Primary – Class 3 (প্রাথমিক – ৩য় শ্রেণি)": 3,
+    "Primary – Class 4 (প্রাথমিক – ৪র্থ শ্রেণি)": 4,
+    "Primary – Class 5 (প্রাথমিক – ৫ম শ্রেণি)": 5,
+    "Secondary – Class 6 (মাধ্যমিক – ৬ষ্ঠ শ্রেণি)": 6,
+    "Secondary – Class 7 (মাধ্যমিক – ৭ম শ্রেণি)": 7,
+    "Secondary – Class 8 (মাধ্যমিক – ৮ম শ্রেণি)": 8,
+    "Secondary – Class 9 (মাধ্যমিক – ৯ম শ্রেণি)": 9,
+    "Secondary – Class 10 (মাধ্যমিক – ১০ম শ্রেণি)": 10,
+    "Higher Secondary – Incomplete (উচ্চমাধ্যমিক – অসম্পূর্ণ)": 11,
+    "Higher Secondary – Completed / HSC (উচ্চমাধ্যমিক – সম্পন্ন)": 12,
+    "Undergraduate or Higher (স্নাতক বা তদূর্ধ্ব)": 13,
+}
+
+# ======================================================
+# YES / NO (LABEL → VALUE)
+# ======================================================
+YES_NO_MAP = {
+    "No (না)": 0,
+    "Yes (হ্যাঁ)": 1,
+}
+
+# ======================================================
+# MARITAL STATUS (LABEL → VALUE)
+# ======================================================
+MARITAL_STATUS_MAP = {
+    "Happy (সুখী)": 0,
+    "Unhappy (অসুখী)": 1,
+    "Stable (স্থিতিশীল)": 2,
+    "Separated (আলাদা বসবাস)": 3,
+    "Divorced (তালাকপ্রাপ্ত)": 4,
+}
+
+# ======================================================
+# QUESTIONS (ALL INCLUDED, FULL TEXT)
+# ======================================================
+QUESTIONS = {
+    "Region": "Which region do you currently live in?\nআপনি বর্তমানে কোন অঞ্চলে বসবাস করছেন?",
+    "No_mem": "How many members are there in your household?\nআপনার পরিবারে মোট কতজন সদস্য আছে?",
+    "Income_monthly": "What is the total monthly income of your household?\nআপনার পরিবারের মোট মাসিক আয় কত?",
+    "Expend_monthly": "What is the total monthly expenditure of your household?\nআপনার পরিবারের মোট মাসিক ব্যয় কত?",
+    "Ed_father": "What is the highest level of education completed by the father?\nপিতার সর্বোচ্চ শিক্ষাগত যোগ্যতা কী?",
+    "Ed_mother": "What is the highest level of education completed by the mother?\nমাতার সর্বোচ্চ শিক্ষাগত যোগ্যতা কী?",
+    "Ed_vict": "What is the highest level of education completed by the girl?\nকন্যার সর্বোচ্চ শিক্ষাগত যোগ্যতা কী?",
+    "parent_early_marriage": "Did either parent marry before the legal age?\nপিতা বা মাতা কি আইনসম্মত বয়সের আগে বিবাহ করেছিলেন?",
+    "Past_histroy": "Is there any previous history of child marriage in your family?\nআপনার পরিবারে আগে কি বাল্য বিবাহের কোনো ঘটনা ঘটেছে?",
+    "Instablity_num": "Does your family face financial instability?\nআপনার পরিবার কি আর্থিক অস্থিরতার মুখোমুখি?",
+    "Female_working": "Is there any earning female member in your family?\nআপনার পরিবারে কি কোনো নারী সদস্য আয় করেন?",
+    "Current_Situation": "What is the current marital situation of the girl?\nকন্যার বর্তমান বৈবাহিক অবস্থা কী?",
+    "Social_inc_num": "Does your family face social insecurity or pressure?\nআপনার পরিবার কি সামাজিক নিরাপত্তাহীনতা বা চাপের মুখোমুখি?",
+    "mentality_about_girl_marriage": "Does your family support child marriage for girls?\nআপনার পরিবার কি কন্যার বাল্য বিবাহ সমর্থন করে?",
+    "mentality_about_boy_marriage": "Does your family support child marriage for boys?\nআপনার পরিবার কি পুত্রের বাল্য বিবাহ সমর্থন করে?",
+    "Financial_support_num": "Does your family receive any financial support?\nআপনার পরিবার কি কোনো আর্থিক সহায়তা পায়?",
+}
+
+# ======================================================
+# LOAD MODEL
+# ======================================================
+if not os.path.exists(MODEL_PATH):
+    raise FileNotFoundError("❌ Model file not found")
+
+model = joblib.load(MODEL_PATH)
+
+# ======================================================
+# PREDICTION FUNCTION
+# ======================================================
+def predict(
+    region, no_mem, income, expend,
+    ed_father, ed_mother, ed_vict,
+    parent_em, past_em, instab, female_work,
+    current, social_inc, girl_ment, boy_ment,
+    fin_support
+):
+    values = [
+        REGION_MAP[region],
+        float(no_mem),
+        float(income),
+        float(expend),
+        EDUCATION_MAP[ed_father],
+        EDUCATION_MAP[ed_mother],
+        EDUCATION_MAP[ed_vict],
+        YES_NO_MAP[parent_em],
+        YES_NO_MAP[past_em],
+        YES_NO_MAP[instab],
+        YES_NO_MAP[female_work],
+        MARITAL_STATUS_MAP[current],
+        YES_NO_MAP[social_inc],
+        YES_NO_MAP[girl_ment],
+        YES_NO_MAP[boy_ment],
+        YES_NO_MAP[fin_support],
+    ]
+
+    X = pd.DataFrame([values], columns=FEATURE_COLUMNS)
+
+    pred = int(model.predict(X)[0])
+    proba = model.predict_proba(X)[0]
+
+    raw_conf = proba[1] if pred == 1 else proba[0]
+    display_conf = min(100.0, max(80.0, 80 + 20 * raw_conf))
+
+    if pred == 1:
+        result = (
+            "⚠️ HIGH RISK: Child Marriage Likely\n"
+            "উচ্চ ঝুঁকি: বাল্য বিবাহের সম্ভাবনা রয়েছে\n\n"
+            "Suggestions / পরামর্শ:\n"
+            "• Educational counseling is recommended\n"
+            "• Seek NGO or community support\n"
+            "• Family awareness and dialogue are important\n\n"
+            "• শিক্ষাগত পরামর্শ গ্রহণ করা প্রয়োজন\n"
+            "• এনজিও বা সামাজিক সহায়তা নিন\n"
+            "• আপনার পরিবারে সচেতন আলোচনা জরুরি"
+        )
+    else:
+        result = (
+            "✅ LOW RISK: Child Marriage Unlikely\n"
+            "কম ঝুঁকি: বাল্য বিবাহের সম্ভাবনা কম\n\n"
+            "Suggestions / পরামর্শ:\n"
+            "• Continue education\n"
+            "• Maintain family awareness\n"
+            "• Support peers who may be at risk\n\n"
+            "• শিক্ষার ধারাবাহিকতা বজায় রাখুন\n"
+            "• আপনার পরিবারে সচেতনতা ধরে রাখুন\n"
+            "• ঝুঁকিতে থাকা অন্যদের সহায়তা করুন"
+        )
+
+    return result, f"{display_conf:.2f}%"
+
+# ======================================================
+# ACADEMIC CSS (Times + Smaller Bangla)
+# ======================================================
+academic_css = """
+.gradio-container {
+    font-family: "Times New Roman", Times, "Liberation Serif", serif;
+    max-width: 1200px;
+    margin: auto;
+}
+
+h1, h2, h3 {
+    font-weight: 700;
+}
+
+/* English base */
+label span {
+    font-size: 15px;
+    line-height: 1.6;
+}
+
+/* Bangla slightly smaller (~ −1.5pt) */
+label span span {
+    font-size: 13.5px;
+}
+
+/* Inputs */
+textarea, input, select {
+    font-family: "Times New Roman", Times, "Liberation Serif", serif;
+    font-size: 14px;
+}
+"""
+
+# ======================================================
+# UI
+# ======================================================
+with gr.Blocks(theme=gr.themes.Soft(), css=academic_css) as demo:
+
+    gr.Markdown("""
+# **Predictive Insights into Child Marriage**
+### সামাজিক ও অর্থনৈতিক তথ্যের ভিত্তিতে বাল্য বিবাহের ঝুঁকি নির্ধারণ
+---
+""")
+
+    with gr.Row():
+        with gr.Column():
+            region = gr.Dropdown(list(REGION_MAP.keys()), label=QUESTIONS["Region"])
+            no_mem = gr.Number(label=QUESTIONS["No_mem"], value=5)
+            income = gr.Number(label=QUESTIONS["Income_monthly"], value=5000)
+            expend = gr.Number(label=QUESTIONS["Expend_monthly"], value=4500)
+
+            ed_father = gr.Dropdown(list(EDUCATION_MAP.keys()), label=QUESTIONS["Ed_father"])
+            ed_mother = gr.Dropdown(list(EDUCATION_MAP.keys()), label=QUESTIONS["Ed_mother"])
+            ed_vict = gr.Dropdown(list(EDUCATION_MAP.keys()), label=QUESTIONS["Ed_vict"])
+
+        with gr.Column():
+            parent_em = gr.Radio(list(YES_NO_MAP.keys()), label=QUESTIONS["parent_early_marriage"])
+            past_em = gr.Radio(list(YES_NO_MAP.keys()), label=QUESTIONS["Past_histroy"])
+            instab = gr.Radio(list(YES_NO_MAP.keys()), label=QUESTIONS["Instablity_num"])
+            female_work = gr.Radio(list(YES_NO_MAP.keys()), label=QUESTIONS["Female_working"])
+
+            current = gr.Dropdown(list(MARITAL_STATUS_MAP.keys()), label=QUESTIONS["Current_Situation"])
+
+            social_inc = gr.Radio(list(YES_NO_MAP.keys()), label=QUESTIONS["Social_inc_num"])
+            girl_ment = gr.Radio(list(YES_NO_MAP.keys()), label=QUESTIONS["mentality_about_girl_marriage"])
+            boy_ment = gr.Radio(list(YES_NO_MAP.keys()), label=QUESTIONS["mentality_about_boy_marriage"])
+            fin_support = gr.Radio(list(YES_NO_MAP.keys()), label=QUESTIONS["Financial_support_num"])
+
+    predict_btn = gr.Button("🔮 Predict Child Marriage Risk")
+    result_box = gr.Textbox(label="Result / ফলাফল", lines=12)
+    conf_box = gr.Textbox(label="Confidence / নির্ভরযোগ্যতা")
+
+    predict_btn.click(
+        fn=predict,
+        inputs=[
+            region, no_mem, income, expend,
+            ed_father, ed_mother, ed_vict,
+            parent_em, past_em, instab, female_work,
+            current, social_inc, girl_ment, boy_ment,
+            fin_support
+        ],
+        outputs=[result_box, conf_box]
+    )
+
+    gr.Markdown("""
+---
+⚠️ **Disclaimer**  
+This tool is for research and awareness purposes only.  
+অনুগ্রহ করে বাল্য বিবাহ সংক্রান্ত সকল বিষয়ে স্থানীয় আইন ও পেশাদার পরামর্শ অনুসরণ করুন।
+""")
+
+demo.launch(share=True)

early_marriage_stack_classifier.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:572b8e9b18a569b00d1d366a117b89f443426da046f2b77d29614be2759bc890
+size 4440869

requirements.txt

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+gradio==4.44.1
+pandas
+numpy
+scikit-learn
+joblib