app.py

import streamlit as st
import requests, os

st.set_page_config(
    page_title="ChildCare Revenue Predictor",
    page_icon="🏫",
    layout="wide",
)

st.title("🏫 ChildCare Facility Revenue Predictor")
st.markdown("""
Predict the **monthly revenue** a child will generate for a day care facility,
based on child profile, program enrolment, and facility characteristics.

Fill in the details below and click **Predict Revenue**.
""")

BACKEND_URL = os.environ.get(
    "BACKEND_URL",
    "https://ramzai9-childcareprediction.hf.space"
)

with st.sidebar:
    st.header("ℹ️ How to Use")
    st.markdown("""
1. Enter the child's details in the **Child Profile** section.
2. Enter the facility details in the **Facility Details** section.
3. Click **Predict Revenue** to get the model's estimate.

**Field Guide:**
- **Child Age** – child's current age in months (6 = 6 months, 60 = 5 years)
- **Care Program** – Full Day covers 8+ hours; Half Day covers 4–5 hours; After School is 3–4 hours after school
- **Attendance Rate** – proportion of days the child attends (1.0 = never absent)
- **Monthly Fee** – the fee charged per month in USD
- **Child ID Prefix** – two-letter code derived from program (FD, HD, AS)
- **Activity Category** – primary activity type the child is enrolled in
""")
    st.divider()
    st.markdown("**Backend API:**")
    st.code(BACKEND_URL, language=None)

col1, col2 = st.columns(2)

with col1:
    st.subheader("👶 Child Profile")
    child_age = st.number_input(
        "Child Age (months)",
        min_value=0.0, max_value=120.0, value=36.0, step=1.0,
        help="Enter the child's age in months. E.g. 24 = 2 years old.",
    )
    care_program = st.selectbox(
        "Care Program",
        options=["Full Day", "Half Day", "After School"],
        help="Full Day (8+ hrs), Half Day (4–5 hrs), After School (3–4 hrs after school).",
    )
    attendance_rate = st.slider(
        "Attendance Rate",
        min_value=0.50, max_value=1.00, value=0.85, step=0.01,
        help="Proportion of scheduled days the child attends. 1.0 = 100% attendance.",
    )
    monthly_fee = st.number_input(
        "Monthly Fee ($)",
        min_value=0.0, max_value=10000.0, value=2000.0, step=50.0,
        help="The monthly fee charged for the child's enrolment, in USD.",
    )
    child_id_char = st.selectbox(
        "Child ID Prefix",
        options=["FD", "HD", "AS"],
        help="FD = Full Day, HD = Half Day, AS = After School.",
    )
    activity_category = st.selectbox(
        "Activity Type Category",
        options=["Academic", "Creative", "Wellness"],
        help="Academic: Reading, Math, STEM. Creative: Arts, Music, Dance. Wellness: PE, Yoga, Outdoor Play.",
    )

with col2:
    st.subheader("🏢 Facility Details")
    facility_size = st.selectbox(
        "Facility Size",
        options=["Small", "Medium", "Large"],
        help="Small (<30 children), Medium (30–80 children), Large (80+ children).",
    )
    city_type = st.selectbox(
        "City Type",
        options=["Tier 1", "Tier 2", "Tier 3"],
        help="Tier 1 = major metropolitan area. Tier 2 = mid-sized city. Tier 3 = small town or rural.",
    )
    facility_type = st.selectbox(
        "Facility Type",
        options=["Full-Service Center", "Montessori School", "Home Daycare", "Corporate Daycare"],
        help="Type of facility offering child care services.",
    )
    facility_year = st.number_input(
        "Facility Establishment Year",
        min_value=1900, max_value=2100, value=2005, step=1,
        help="The year the facility was established. Older facilities may have more established reputations.",
    )

st.divider()
predict_btn = st.button("🔮 Predict Revenue", type="primary", use_container_width=True)

if predict_btn:
    payload = {
        "Child_Age_Months":            child_age,
        "Child_Care_Program":          care_program,
        "Child_Attendance_Rate":       attendance_rate,
        "Child_Monthly_Fee":           monthly_fee,
        "Facility_Size":               facility_size,
        "Facility_Location_City_Type": city_type,
        "Facility_Type":               facility_type,
        "Child_Id_char":               child_id_char,
        "Facility_Establishment_Year": facility_year,
        "Activity_Type_Category":      activity_category,
    }

    with st.spinner("Contacting the prediction API..."):
        try:
            resp = requests.post(
                f"{BACKEND_URL}/v1/predict",
                json=payload,
                timeout=30,
            )
            if resp.status_code == 200:
                revenue = resp.json().get("Revenue", "N/A")
                st.success(f"### 💰 Predicted Monthly Revenue: **${revenue:,.2f}**")
                st.balloons()

                with st.expander("📋 Input Summary"):
                    st.json(payload)
            else:
                st.error(f"API Error ({resp.status_code}): {resp.json().get('error', resp.text)}")
        except requests.exceptions.Timeout:
            st.error("⏱️ Request timed out. The backend may be starting up — please try again in 30 seconds.")
        except requests.exceptions.ConnectionError:
            st.error("🔌 Could not connect to the backend. Please verify the BACKEND_URL environment variable.")
        except Exception as e:
            st.error(f"Unexpected error: {e}")

st.divider()
st.caption("Built with XGBoost + Flask + Streamlit · Deployed on Hugging Face Spaces")