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| from flask import Flask, request, jsonify | |
| import joblib, pandas as pd, os | |
| childcare_api = Flask(__name__) | |
| MODEL_PATH = "xgb_tuned_model.joblib" | |
| model = joblib.load(MODEL_PATH) | |
| # ── Validation constants ────────────────────────────────────── | |
| VALID_CARE_PROGRAM = {"Full Day", "Half Day", "After School"} | |
| VALID_FACILITY_SIZE = {"Small", "Medium", "Large"} | |
| VALID_CITY_TYPE = {"Tier 1", "Tier 2", "Tier 3"} | |
| VALID_FACILITY_TYPE = {"Full-Service Center", "Montessori School", "Home Daycare", "Corporate Daycare"} | |
| VALID_CHILD_ID_CHAR = {"FD", "HD", "AS"} | |
| VALID_ACTIVITY_CAT = {"Academic", "Creative", "Wellness"} | |
| REQUIRED_FIELDS = [ | |
| "Child_Age_Months", "Child_Care_Program", "Child_Attendance_Rate", | |
| "Child_Monthly_Fee", "Facility_Size", "Facility_Location_City_Type", | |
| "Facility_Type", "Child_Id_char", "Facility_Establishment_Year", | |
| "Activity_Type_Category", | |
| ] | |
| def validate_input(data): | |
| # 1. Check required fields | |
| missing = [f for f in REQUIRED_FIELDS if f not in data] | |
| if missing: | |
| return False, f"Missing required fields: {missing}" | |
| # 2. Type checks | |
| try: | |
| float(data["Child_Age_Months"]) | |
| float(data["Child_Attendance_Rate"]) | |
| float(data["Child_Monthly_Fee"]) | |
| int(data["Facility_Establishment_Year"]) | |
| except (ValueError, TypeError) as e: | |
| return False, f"Type error: {e}" | |
| # 3. Range checks | |
| if not (0 <= float(data["Child_Age_Months"]) <= 120): | |
| return False, "Child_Age_Months must be between 0 and 120" | |
| if not (0.0 <= float(data["Child_Attendance_Rate"]) <= 1.0): | |
| return False, "Child_Attendance_Rate must be between 0.0 and 1.0" | |
| if not (0 <= float(data["Child_Monthly_Fee"]) <= 10000): | |
| return False, "Child_Monthly_Fee must be between 0 and 10000" | |
| # 4. Categorical checks | |
| if data["Child_Care_Program"] not in VALID_CARE_PROGRAM: | |
| return False, f"Child_Care_Program must be one of {VALID_CARE_PROGRAM}" | |
| if data["Facility_Size"] not in VALID_FACILITY_SIZE: | |
| return False, f"Facility_Size must be one of {VALID_FACILITY_SIZE}" | |
| if data["Facility_Location_City_Type"] not in VALID_CITY_TYPE: | |
| return False, f"Facility_Location_City_Type must be one of {VALID_CITY_TYPE}" | |
| if data["Facility_Type"] not in VALID_FACILITY_TYPE: | |
| return False, f"Facility_Type must be one of {VALID_FACILITY_TYPE}" | |
| if data["Child_Id_char"] not in VALID_CHILD_ID_CHAR: | |
| return False, f"Child_Id_char must be one of {VALID_CHILD_ID_CHAR}" | |
| if data["Activity_Type_Category"] not in VALID_ACTIVITY_CAT: | |
| return False, f"Activity_Type_Category must be one of {VALID_ACTIVITY_CAT}" | |
| return True, None | |
| def health(): | |
| return jsonify({"status": "healthy", "model": "XGBoost ChildCare Revenue Pipeline"}) | |
| def predict_revenue(): | |
| data = request.get_json(force=True) | |
| is_valid, error_msg = validate_input(data) | |
| if not is_valid: | |
| return jsonify({"error": error_msg}), 400 | |
| input_df = pd.DataFrame([{ | |
| "Child_Age_Months": float(data["Child_Age_Months"]), | |
| "Child_Care_Program": data["Child_Care_Program"], | |
| "Child_Attendance_Rate": float(data["Child_Attendance_Rate"]), | |
| "Child_Monthly_Fee": float(data["Child_Monthly_Fee"]), | |
| "Facility_Size": data["Facility_Size"], | |
| "Facility_Location_City_Type": data["Facility_Location_City_Type"], | |
| "Facility_Type": data["Facility_Type"], | |
| "Child_Id_char": data["Child_Id_char"], | |
| "Facility_Establishment_Year": int(data["Facility_Establishment_Year"]), | |
| "Activity_Type_Category": data["Activity_Type_Category"], | |
| }]) | |
| prediction = float(model.predict(input_df)[0]) | |
| return jsonify({"Revenue": round(prediction, 2)}), 200 | |
| if __name__ == "__main__": | |
| port = int(os.environ.get("PORT", 7860)) | |
| childcare_api.run(host="0.0.0.0", port=port) | |