add requirements and app for HF Spaces deployment

app.py

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+import streamlit as st
+import joblib
+import numpy as np
+import sys
+sys.path.insert(0, '.')
+from src.data_loader import load_data, preprocess, split_and_scale
+from src.llm_advisor import get_burnout_advice, get_burnout_chat_response
+
+# Load models
+@st.cache_resource
+def load_models():
+    xgb_model = joblib.load('models/xgboost_model.pkl')
+    scaler = joblib.load('models/scaler.pkl')
+    feature_cols = joblib.load('models/feature_cols.pkl')
+    return xgb_model, scaler, feature_cols
+
+def get_top_risk_factors(user_input, feature_cols, model):
+    importance = model.feature_importances_
+    user_values = [user_input[f] for f in feature_cols]
+    
+    # Weight importance by how extreme the value is (normalized 0-10)
+    risk_factors = {}
+    for i, col in enumerate(feature_cols):
+        val = user_values[i]
+        imp = importance[i]
+        # Higher stress/shouting/lost vacation = bad, higher sleep/flow = good
+        bad_features = ['DAILY_STRESS', 'LOST_VACATION', 'DAILY_SHOUTING', 'BMI_RANGE']
+        if col in bad_features:
+            score = imp * (val / 10)
+        else:
+            score = imp * (1 - val / 10)
+        risk_factors[col] = score
+    
+    top = sorted(risk_factors.items(), key=lambda x: x[1], reverse=True)[:3]
+    return {k: f"score {user_input[k]}/10" for k, _ in top}
+
+def main():
+    st.set_page_config(page_title="Burnout Tracker", page_icon="🔥", layout="wide")
+    
+    st.title("🔥 Burnout Risk Tracker")
+    st.markdown("*Predict your burnout risk and get personalized advice*")
+    
+    xgb_model, scaler, feature_cols = load_models()
+    
+    # Initialize session state
+    if 'conversation_history' not in st.session_state:
+        st.session_state.conversation_history = []
+    if 'risk_score' not in st.session_state:
+        st.session_state.risk_score = None
+    if 'advice' not in st.session_state:
+        st.session_state.advice = None
+
+    col1, col2 = st.columns([1, 1])
+    
+    with col1:
+        st.subheader("Your Daily Lifestyle Inputs")
+        
+        user_input = {}
+        user_input['DAILY_STRESS'] = st.slider("Daily Stress Level", 0, 10, 5)
+        user_input['SLEEP_HOURS'] = st.slider("Sleep Hours", 0, 10, 7)
+        user_input['LOST_VACATION'] = st.slider("Unused Vacation Days", 0, 10, 2)
+        user_input['TODO_COMPLETED'] = st.slider("Tasks Completed Today", 0, 10, 5)
+        user_input['FLOW'] = st.slider("Flow State at Work", 0, 10, 5)
+        user_input['TIME_FOR_PASSION'] = st.slider("Time for Passions", 0, 10, 3)
+        user_input['WEEKLY_MEDITATION'] = st.slider("Weekly Meditation Sessions", 0, 10, 2)
+        user_input['DAILY_SHOUTING'] = st.slider("Daily Emotional Outbursts", 0, 10, 1)
+        user_input['FRUITS_VEGGIES'] = st.slider("Fruits & Veggies Servings", 0, 10, 5)
+        user_input['DAILY_STEPS'] = st.slider("Daily Steps (thousands)", 0, 10, 5)
+        user_input['SUFFICIENT_INCOME'] = st.slider("Income Sufficiency", 0, 10, 5)
+        user_input['SOCIAL_NETWORK'] = st.slider("Social Network Strength", 0, 10, 5)
+        user_input['ACHIEVEMENT'] = st.slider("Sense of Achievement", 0, 10, 5)
+        user_input['SUPPORTING_OTHERS'] = st.slider("Supporting Others", 0, 10, 5)
+        user_input['PLACES_VISITED'] = st.slider("New Places Visited", 0, 10, 3)
+        user_input['CORE_CIRCLE'] = st.slider("Close Relationships", 0, 10, 5)
+        user_input['PERSONAL_AWARDS'] = st.slider("Personal Awards/Recognition", 0, 10, 3)
+        user_input['DONATION'] = st.slider("Charitable Giving", 0, 10, 2)
+        user_input['BMI_RANGE'] = st.slider("BMI Range (1=underweight, 4=obese)", 1, 4, 2)
+        user_input['LIVE_VISION'] = st.slider("Life Vision Clarity", 0, 10, 5)
+        user_input['AGE'] = st.selectbox("Age Range", [0, 1, 2, 3], 
+                                          format_func=lambda x: ['<20', '21-35', '36-50', '51+'][x])
+        user_input['GENDER'] = st.selectbox("Gender", [0, 1], 
+                                             format_func=lambda x: ['Female', 'Male'][x])
+
+        if st.button("🔍 Assess My Burnout Risk", type="primary"):
+            input_array = np.array([[user_input[f] for f in feature_cols]])
+            input_scaled = scaler.transform(input_array)
+            
+            risk_score = xgb_model.predict_proba(input_scaled)[0][1]
+            st.session_state.risk_score = risk_score
+            st.session_state.conversation_history = []
+            
+            top_risk_factors = get_top_risk_factors(user_input, feature_cols, xgb_model)
+            
+            with st.spinner("Getting personalized advice from AI coach..."):
+                advice = get_burnout_advice(risk_score, top_risk_factors, user_input)
+                st.session_state.advice = advice
+                st.session_state.risk_context = f"Risk score: {risk_score:.1%}, Top factors: {top_risk_factors}"
+
+    with col2:
+        if st.session_state.risk_score is not None:
+            risk_score = st.session_state.risk_score
+            
+            # Risk meter
+            st.subheader("Your Burnout Risk")
+            if risk_score > 0.7:
+                st.error(f"🚨 High Risk: {risk_score:.1%}")
+            elif risk_score > 0.4:
+                st.warning(f"⚠️ Moderate Risk: {risk_score:.1%}")
+            else:
+                st.success(f"✅ Low Risk: {risk_score:.1%}")
+            
+            st.progress(float(risk_score))
+            
+            # AI advice
+            st.subheader("AI Coach Advice")
+            st.markdown(st.session_state.advice)
+            
+            st.divider()
+            
+            # Chat
+            st.subheader("💬 Chat with Your AI Coach")
+            
+            for msg in st.session_state.conversation_history:
+                if msg['role'] == 'user':
+                    st.chat_message("user").write(msg['content'])
+                else:
+                    st.chat_message("assistant").write(msg['content'])
+            
+            if prompt := st.chat_input("Ask your coach anything..."):
+                st.chat_message("user").write(prompt)
+                with st.spinner("Thinking..."):
+                    response, st.session_state.conversation_history = get_burnout_chat_response(
+                        st.session_state.conversation_history,
+                        prompt,
+                        st.session_state.risk_context
+                    )
+                st.chat_message("assistant").write(response)
+                st.rerun()
+
+if __name__ == '__main__':
+    main()

app/app.py

@@ -0,0 +1,141 @@
+import streamlit as st
+import joblib
+import numpy as np
+import sys
+sys.path.insert(0, '.')
+from src.data_loader import load_data, preprocess, split_and_scale
+from src.llm_advisor import get_burnout_advice, get_burnout_chat_response
+
+# Load models
+@st.cache_resource
+def load_models():
+    xgb_model = joblib.load('models/xgboost_model.pkl')
+    scaler = joblib.load('models/scaler.pkl')
+    feature_cols = joblib.load('models/feature_cols.pkl')
+    return xgb_model, scaler, feature_cols
+
+def get_top_risk_factors(user_input, feature_cols, model):
+    importance = model.feature_importances_
+    user_values = [user_input[f] for f in feature_cols]
+    
+    # Weight importance by how extreme the value is (normalized 0-10)
+    risk_factors = {}
+    for i, col in enumerate(feature_cols):
+        val = user_values[i]
+        imp = importance[i]
+        # Higher stress/shouting/lost vacation = bad, higher sleep/flow = good
+        bad_features = ['DAILY_STRESS', 'LOST_VACATION', 'DAILY_SHOUTING', 'BMI_RANGE']
+        if col in bad_features:
+            score = imp * (val / 10)
+        else:
+            score = imp * (1 - val / 10)
+        risk_factors[col] = score
+    
+    top = sorted(risk_factors.items(), key=lambda x: x[1], reverse=True)[:3]
+    return {k: f"score {user_input[k]}/10" for k, _ in top}
+
+def main():
+    st.set_page_config(page_title="Burnout Tracker", page_icon="🔥", layout="wide")
+    
+    st.title("🔥 Burnout Risk Tracker")
+    st.markdown("*Predict your burnout risk and get personalized advice*")
+    
+    xgb_model, scaler, feature_cols = load_models()
+    
+    # Initialize session state
+    if 'conversation_history' not in st.session_state:
+        st.session_state.conversation_history = []
+    if 'risk_score' not in st.session_state:
+        st.session_state.risk_score = None
+    if 'advice' not in st.session_state:
+        st.session_state.advice = None
+
+    col1, col2 = st.columns([1, 1])
+    
+    with col1:
+        st.subheader("Your Daily Lifestyle Inputs")
+        
+        user_input = {}
+        user_input['DAILY_STRESS'] = st.slider("Daily Stress Level", 0, 10, 5)
+        user_input['SLEEP_HOURS'] = st.slider("Sleep Hours", 0, 10, 7)
+        user_input['LOST_VACATION'] = st.slider("Unused Vacation Days", 0, 10, 2)
+        user_input['TODO_COMPLETED'] = st.slider("Tasks Completed Today", 0, 10, 5)
+        user_input['FLOW'] = st.slider("Flow State at Work", 0, 10, 5)
+        user_input['TIME_FOR_PASSION'] = st.slider("Time for Passions", 0, 10, 3)
+        user_input['WEEKLY_MEDITATION'] = st.slider("Weekly Meditation Sessions", 0, 10, 2)
+        user_input['DAILY_SHOUTING'] = st.slider("Daily Emotional Outbursts", 0, 10, 1)
+        user_input['FRUITS_VEGGIES'] = st.slider("Fruits & Veggies Servings", 0, 10, 5)
+        user_input['DAILY_STEPS'] = st.slider("Daily Steps (thousands)", 0, 10, 5)
+        user_input['SUFFICIENT_INCOME'] = st.slider("Income Sufficiency", 0, 10, 5)
+        user_input['SOCIAL_NETWORK'] = st.slider("Social Network Strength", 0, 10, 5)
+        user_input['ACHIEVEMENT'] = st.slider("Sense of Achievement", 0, 10, 5)
+        user_input['SUPPORTING_OTHERS'] = st.slider("Supporting Others", 0, 10, 5)
+        user_input['PLACES_VISITED'] = st.slider("New Places Visited", 0, 10, 3)
+        user_input['CORE_CIRCLE'] = st.slider("Close Relationships", 0, 10, 5)
+        user_input['PERSONAL_AWARDS'] = st.slider("Personal Awards/Recognition", 0, 10, 3)
+        user_input['DONATION'] = st.slider("Charitable Giving", 0, 10, 2)
+        user_input['BMI_RANGE'] = st.slider("BMI Range (1=underweight, 4=obese)", 1, 4, 2)
+        user_input['LIVE_VISION'] = st.slider("Life Vision Clarity", 0, 10, 5)
+        user_input['AGE'] = st.selectbox("Age Range", [0, 1, 2, 3], 
+                                          format_func=lambda x: ['<20', '21-35', '36-50', '51+'][x])
+        user_input['GENDER'] = st.selectbox("Gender", [0, 1], 
+                                             format_func=lambda x: ['Female', 'Male'][x])
+
+        if st.button("🔍 Assess My Burnout Risk", type="primary"):
+            input_array = np.array([[user_input[f] for f in feature_cols]])
+            input_scaled = scaler.transform(input_array)
+            
+            risk_score = xgb_model.predict_proba(input_scaled)[0][1]
+            st.session_state.risk_score = risk_score
+            st.session_state.conversation_history = []
+            
+            top_risk_factors = get_top_risk_factors(user_input, feature_cols, xgb_model)
+            
+            with st.spinner("Getting personalized advice from AI coach..."):
+                advice = get_burnout_advice(risk_score, top_risk_factors, user_input)
+                st.session_state.advice = advice
+                st.session_state.risk_context = f"Risk score: {risk_score:.1%}, Top factors: {top_risk_factors}"
+
+    with col2:
+        if st.session_state.risk_score is not None:
+            risk_score = st.session_state.risk_score
+            
+            # Risk meter
+            st.subheader("Your Burnout Risk")
+            if risk_score > 0.7:
+                st.error(f"🚨 High Risk: {risk_score:.1%}")
+            elif risk_score > 0.4:
+                st.warning(f"⚠️ Moderate Risk: {risk_score:.1%}")
+            else:
+                st.success(f"✅ Low Risk: {risk_score:.1%}")
+            
+            st.progress(float(risk_score))
+            
+            # AI advice
+            st.subheader("AI Coach Advice")
+            st.markdown(st.session_state.advice)
+            
+            st.divider()
+            
+            # Chat
+            st.subheader("💬 Chat with Your AI Coach")
+            
+            for msg in st.session_state.conversation_history:
+                if msg['role'] == 'user':
+                    st.chat_message("user").write(msg['content'])
+                else:
+                    st.chat_message("assistant").write(msg['content'])
+            
+            if prompt := st.chat_input("Ask your coach anything..."):
+                st.chat_message("user").write(prompt)
+                with st.spinner("Thinking..."):
+                    response, st.session_state.conversation_history = get_burnout_chat_response(
+                        st.session_state.conversation_history,
+                        prompt,
+                        st.session_state.risk_context
+                    )
+                st.chat_message("assistant").write(response)
+                st.rerun()
+
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -0,0 +1,8 @@
+streamlit
+xgboost
+scikit-learn
+pandas
+numpy
+groq
+python-dotenv
+joblib