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