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feature_names.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:417b94f07e2693a9be08a5996fd918d5a84f3f55bcd66f500c34577f24e0151f
+size 444

hugging_face_learner.py

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+# learner_app.py - Streamlit Dashboard with Hugging Face Suggestions (No OpenAI required)
+
+import streamlit as st
+import pandas as pd
+import joblib
+import os
+import requests
+import warnings
+
+# --- Page Configuration ---
+st.set_page_config(
+    page_title="Slow Learner Prediction Tool",
+    page_icon="🎓",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# --- Load Artifacts ---
+@st.cache_resource
+def load_artifacts():
+    try:
+        scaler = joblib.load("scaler.joblib")
+        model = joblib.load("random_forest_model.joblib")
+        features = joblib.load("feature_names.joblib")
+        return scaler, model, features
+    except Exception as e:
+        st.error(f"Error loading model files: {e}")
+        return None, None, None
+
+scaler, model, feature_names = load_artifacts()
+
+# --- Input Configuration ---
+main_inputs = [
+    'study_hours_per_day', 'social_media_hours', 'attendance_percentage',
+    'sleep_hours', 'exam_score', 'mental_health_rating'
+]
+
+categorical_inputs = {
+    'gender': ['Female', 'Male', 'Other'],
+    'part_time_job': ['No', 'Yes'],
+    'diet_quality': ['Average', 'Good', 'Poor'],
+    'parental_education_level': ['Bachelor', 'High School', 'Master', 'Unknown'],
+    'internet_quality': ['Average', 'Good', 'Poor'],
+    'extracurricular_participation': ['No', 'Yes']
+}
+
+# --- Helper Functions ---
+def prepare_input(user_input, cat_input, expected_features):
+    df = pd.DataFrame([user_input])
+    for col, value in cat_input.items():
+        df[col] = value
+    df = pd.get_dummies(df, drop_first=True)
+    for feat in expected_features:
+        if feat not in df.columns:
+            df[feat] = 0
+    df = df[expected_features]
+    return df
+
+def predict_support(df, scaler, model):
+    scaled = scaler.transform(df)
+    pred = model.predict(scaled)[0]
+    prob = model.predict_proba(scaled)[0][1]
+    return pred, prob
+
+def calculate_risk_score(study_hours, exam_score, attendance, participation, sleep_hours, social_media, prob):
+    score = 0
+    if study_hours < 2:
+        score += 25
+    if exam_score < 65:
+        score += 25
+    if attendance < 85:
+        score += 10
+    if participation <= 2:
+        score += 10
+    if sleep_hours < 6:
+        score += 10
+    if social_media > 4:
+        score += 10
+    if prob > 0.35:
+        score += 10
+    return score
+
+def get_remedial_suggestions(score, probability, exam_score,
+                              attendance_percentage, participation_rating,
+                              study_hours, sleep_hours, social_media, part_time, extracurricular,
+                              mental_health_rating):
+    suggestions = []
+    avg_metric = (study_hours * 10 + sleep_hours * 10 + mental_health_rating + exam_score + attendance_percentage + (6 - participation_rating) * 10 + (6 - mental_health_rating)) / 7
+    threshold = 60
+
+    if score >= 50 or avg_metric < threshold:
+        suggestions.append(f"**Student may benefit from additional support (Risk Score: {score}/100)**")
+        suggestions.append("---")
+        suggestions.append("### ✅ General Support Recommendations")
+        suggestions.extend([
+            "* Meet individually to identify challenges and learning preferences.",
+            "* Break down complex topics with step-by-step guidance.",
+            "* Provide more practice in weaker subjects.",
+            "* Incorporate visual learning aids and activities.",
+            "* Encourage active participation in a supportive setting.",
+            "* Recommend mentoring or peer learning sessions."
+        ])
+
+        suggestions.append("\n---")
+        suggestions.append("### 📌 Personalized Observations & Strategies")
+        triggered_specific = False
+
+        if exam_score < 45:
+            suggestions.append("* *Observation:* Very Low Standardized Test Score → Focus on core concepts and regular practice.")
+            triggered_specific = True
+
+        if attendance_percentage < 75:
+            suggestions.append("* *Observation:* Low Attendance → Discuss reasons and promote regular class routines.")
+            triggered_specific = True
+
+        if participation_rating <= 3:
+            suggestions.append("* *Observation:* Low Class Participation → Offer incentives and create non-judgmental space.")
+            triggered_specific = True
+
+        if study_hours < 2:
+            suggestions.append("* *Observation:* Insufficient Study Time → Design time tables with short, focused sessions.")
+            triggered_specific = True
+
+        if sleep_hours < 6:
+            suggestions.append("* *Observation:* Poor Sleep Habits → Promote healthy sleep routines (7-8 hrs/night).")
+            triggered_specific = True
+
+        if social_media > 4:
+            suggestions.append("* *Observation:* High Screen Time → Introduce productivity tools and digital detox plans.")
+            triggered_specific = True
+
+        if part_time == "Yes" and study_hours < 2:
+            suggestions.append("* *Observation:* Work-Study Conflict → Balance workload and offer weekend sessions.")
+            triggered_specific = True
+
+        if extracurricular == "Yes" and exam_score < 60:
+            suggestions.append("* *Observation:* Over-scheduled with Activities → Temporarily reduce extracurricular load.")
+            triggered_specific = True
+
+        if not triggered_specific:
+            suggestions.append("* No individual issues flagged. Support focus, motivation, and consistency.")
+
+        suggestions.append("\n---")
+        suggestions.append("**🔁 Follow-Up:** Review student progress weekly and adjust interventions as needed.")
+
+    return suggestions
+
+def get_hf_suggestions(student_profile_dict):
+    prompt = f"""
+    A student is showing signs of struggling in academics. Their profile is:
+    {student_profile_dict}
+
+    Based on this, generate 3 short, practical, and personalized remedial strategies.
+    """
+    headers = {
+        "Authorization": f"Bearer {st.secrets['HF_API_KEY']}"
+    }
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "temperature": 0.7,
+            "max_length": 250
+        }
+    }
+    model_url = "https://api-inference.huggingface.co/models/google/flan-t5-base"
+    try:
+        response = requests.post(model_url, headers=headers, json=payload)
+        result = response.json()
+        if isinstance(result, dict) and "error" in result:
+            return f"❌  API Error: {result['error']}"
+        return result[0]['generated_text']
+    except Exception as e:
+        return f"⚠️ Error getting  suggestions: {e}"
+
+# --- UI ---
+st.title("🎓 Slow Learner Prediction Tool")
+st.subheader("Identify students who might need additional support")
+
+with st.sidebar.form("student_form"):
+    st.markdown("### 🎯 Core Student Features")
+
+    study_hours = st.number_input("Study Hours Per Day", 0.0, 24.0, 2.0, 0.5)
+    social_media = st.number_input("Social Media Hours", 0.0, 24.0, 1.0, 0.5)
+    attendance = st.number_input("Attendance Percentage", 0.0, 100.0, 90.0, 0.5)
+    sleep_hours = st.number_input("Sleep Hours Per Day", 0.0, 24.0, 7.0, 0.5)
+    exam_score = st.number_input("Exam Score", 0.0, 100.0, 60.0, 0.5)
+    mental_health_rating = st.slider("Mental Health Rating", 1, 5, 3)
+    participation = st.slider("Participation Rating", 1, 5, 3)
+
+    with st.expander("➕ Optional Features (Category Inputs)"):
+        gender = st.selectbox("Gender", categorical_inputs['gender'])
+        part_time = st.selectbox("Part-Time Job", categorical_inputs['part_time_job'])
+        diet = st.selectbox("Diet Quality", categorical_inputs['diet_quality'])
+        parent_edu = st.selectbox("Parental Education Level", categorical_inputs['parental_education_level'])
+        net_quality = st.selectbox("Internet Quality", categorical_inputs['internet_quality'])
+        extracurricular = st.selectbox("Extracurricular Participation", categorical_inputs['extracurricular_participation'])
+
+    use_ai = st.checkbox("💬 Use AI Suggestions ", value=True)
+    submit_btn = st.form_submit_button("✨ Predict Support Need")
+
+# --- Main Panel ---
+if submit_btn:
+    if scaler is None or model is None or feature_names is None:
+        st.error("Artifacts not loaded. Please check the model files.")
+    else:
+        with st.spinner("Analyzing student data..."):
+            user_inputs = {
+                'study_hours_per_day': study_hours,
+                'social_media_hours': social_media,
+                'attendance_percentage': attendance,
+                'sleep_hours': sleep_hours,
+                'exam_score': exam_score,
+                'mental_health_rating': mental_health_rating
+            }
+            cat_values = {
+                'gender': gender,
+                'part_time_job': part_time,
+                'diet_quality': diet,
+                'parental_education_level': parent_edu,
+                'internet_quality': net_quality,
+                'extracurricular_participation': extracurricular
+            }
+            input_df = prepare_input(user_inputs, cat_values, feature_names)
+            prediction, probability = predict_support(input_df, scaler, model)
+            risk_score = calculate_risk_score(study_hours, exam_score, attendance, participation, sleep_hours, social_media, probability)
+
+        if risk_score >= 30:
+            st.error(f"🚨 This student may be a slow learner (Risk Score: {risk_score}/100)")
+        else:
+            st.success("✅ No significant learning difficulties detected.")
+
+        st.progress(risk_score / 100.0, text=f"Slow Learner Risk Score: {risk_score}/100")
+
+        suggestions = get_remedial_suggestions(
+            risk_score, probability, exam_score, attendance, participation,
+            study_hours, sleep_hours, social_media, part_time, extracurricular, mental_health_rating
+        )
+
+        if suggestions:
+            with st.expander("💡 Suggested Remedial Actions", expanded=True):
+                for tip in suggestions:
+                    st.markdown(tip)
+
+        if use_ai:
+            st.markdown("### 🤖 AI-Based Suggestions (Hugging Face)")
+            student_data = {
+                "Study Hours": study_hours,
+                "Exam Score": exam_score,
+                "Attendance": attendance,
+                "Participation": participation,
+                "Sleep Hours": sleep_hours,
+                "Social Media Hours": social_media,
+                "Part-time Job": part_time,
+                "Extracurricular Activities": extracurricular,
+                "Mental Health Rating": mental_health_rating
+            }
+            hf_advice = get_hf_suggestions(student_data)
+            st.markdown(hf_advice)
+
+st.markdown("---")
+st.caption("Disclaimer: This is an AI-based tool. Use results alongside academic judgment.")

random_forest_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:795d5c482e3ca62b82c467526d88ced48296220286c50aa1b73cb796751677db
+size 6626643

scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2d1f6bb2a962a03a316572dd2fdcb960a5fcbb692b83eaa6f68e67a6e38a07df
+size 1727