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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import xgboost as xgb | |
| import pickle | |
| import datetime | |
| # 1. MUST BE THE FIRST STREAMLIT COMMAND | |
| st.set_page_config(page_title="Kaggle Sticker Sales Forecast", layout="centered") | |
| # --- ASSET LOADING --- | |
| def load_assets(): | |
| # Load the XGBoost model | |
| model = xgb.XGBRegressor() | |
| model.load_model("model.ubj") | |
| # Load the exact column list from your training phase | |
| with open("model_columns.pkl", "rb") as f: | |
| model_columns = pickle.load(f) | |
| return model, model_columns | |
| # Load model and columns | |
| model, model_columns = load_assets() | |
| # --- INTERFACE --- | |
| st.title("📊 Sticker Sales Forecasting") | |
| st.write("Predict sales volume based on historical training data patterns.") | |
| # --- INPUT SECTION --- | |
| with st.sidebar: | |
| st.header("Input Features") | |
| date = st.date_input("Select Date", datetime.date(2026, 5, 4)) | |
| # Categories based on your data summary | |
| country = st.selectbox("Country", ["Finland", "Italy", "Singapore", "Norway", "Canada", "Kenya"]) | |
| store = st.selectbox("Store", ["Premium Sticker Mart", "Stickers for Less", "Discount Stickers"]) | |
| product = st.selectbox("Product", ["Kaggle", "Kaggle Tiers", "Kerneler Dark Mode", "Kerneler", "Holographic Goose"]) | |
| # --- PREDICTION LOGIC --- | |
| if st.button("Predict Units Sold"): | |
| # 1. Feature Engineering (Match your training steps) | |
| input_date = pd.to_datetime(date) | |
| data = { | |
| 'year': input_date.year, | |
| 'month': input_date.month, | |
| 'day': input_date.day, | |
| 'dayofweek': input_date.dayofweek, | |
| 'is_weekend': 1 if input_date.dayofweek >= 5 else 0 | |
| } | |
| # 2. Build the full feature row using the loaded columns | |
| input_df = pd.DataFrame(0, index=[0], columns=model_columns) | |
| # 3. Fill Time Features | |
| for key, value in data.items(): | |
| if key in input_df.columns: | |
| input_df.at[0, key] = value | |
| # 4. Fill One-Hot Features | |
| for feat in [f"country_{country}", f"store_{store}", f"product_{product}"]: | |
| if feat in input_df.columns: | |
| input_df.at[0, feat] = 1 | |
| # 5. Model Inference | |
| raw_pred = model.predict(input_df) | |
| # Reverse log transformation if used during training (np.expm1) | |
| final_prediction = np.expm1(raw_pred)[0] | |
| # --- RESULTS --- | |
| st.markdown("---") | |
| if final_prediction < 0: final_prediction = 0 | |
| st.metric(label="Predicted Units Sold", value=f"{int(final_prediction)}") | |
| if final_prediction > 0: | |
| st.success("Calculation complete.") | |
| else: | |
| st.warning("The model returned a near-zero prediction. Check feature alignment.") |