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| import streamlit as st | |
| import pandas as pd | |
| from catboost import CatBoostClassifier | |
| # Page configuration | |
| st.set_page_config(page_title="F1 Pit Strategy Predictor", layout="wide") | |
| def load_model(): | |
| model = CatBoostClassifier() | |
| model.load_model("f1_pit_strategy_model.cbm", format='cbm') | |
| return model | |
| model = load_model() | |
| st.title("🏎️ F1 Pit Stop Strategy Predictor") | |
| st.markdown(""" | |
| Predict the probability of a driver pitting on the next lap using your high-performance CatBoost model. | |
| **Developed by: [Basak Tamer](https://huggingface.co/basaktamer)** | |
| """) | |
| # Create columns for organized input | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| driver = st.text_input("Driver Code", value="HAM") | |
| race = st.text_input("Race Name", value="Monaco Grand Prix") | |
| year = st.number_input("Year", value=2026) | |
| compound = st.selectbox("Tyre Compound", ["SOFT", "MEDIUM", "HARD", "INTERMEDIATE", "WET"]) | |
| pit_stop = st.selectbox("PitStop (Current Lap)", [0, 1]) | |
| with col2: | |
| lap_number = st.number_input("Lap Number", min_value=1, value=20) | |
| stint = st.number_input("Stint Number", min_value=1, value=1) | |
| tyre_life = st.number_input("Tyre Life (Laps)", min_value=0, value=10) | |
| position = st.slider("Current Position", 1, 20, 5) | |
| pos_change = st.number_input("Position Change", value=0) | |
| with col3: | |
| lap_time = st.number_input("LapTime (s)", value=80.0) | |
| lap_delta = st.number_input("LapTime_Delta", value=0.1) | |
| cum_deg = st.number_input("Cumulative_Degradation", value=0.5) | |
| race_progress = st.slider("Race Progress (%)", 0.0, 1.0, 0.3) | |
| # Prediction button | |
| if st.button("Predict Pit Probability"): | |
| # The order MUST match: ['Driver', 'Compound', 'Race', 'Year', 'PitStop', 'LapNumber', | |
| # 'Stint', 'TyreLife', 'Position', 'LapTime (s)', 'LapTime_Delta', | |
| # 'Cumulative_Degradation', 'RaceProgress', 'Position_Change'] | |
| input_data = pd.DataFrame({ | |
| 'Driver': [driver], | |
| 'Compound': [compound], | |
| 'Race': [race], | |
| 'Year': [year], | |
| 'PitStop': [pit_stop], | |
| 'LapNumber': [lap_number], | |
| 'Stint': [stint], | |
| 'TyreLife': [tyre_life], | |
| 'Position': [position], | |
| 'LapTime (s)': [lap_time], | |
| 'LapTime_Delta': [lap_delta], | |
| 'Cumulative_Degradation': [cum_deg], | |
| 'RaceProgress': [race_progress], | |
| 'Position_Change': [pos_change] | |
| }) | |
| # Re-enforcing the exact order identified by your model.feature_names_ | |
| column_order = [ | |
| 'Driver', 'Compound', 'Race', 'Year', 'PitStop', 'LapNumber', | |
| 'Stint', 'TyreLife', 'Position', 'LapTime (s)', 'LapTime_Delta', | |
| 'Cumulative_Degradation', 'RaceProgress', 'Position_Change' | |
| ] | |
| input_data = input_data[column_order] | |
| try: | |
| prob = model.predict_proba(input_data)[0][1] | |
| st.divider() | |
| st.subheader(f"Pit Probability: {prob:.2%}") | |
| if prob > 0.5: | |
| st.error("Strategy Alert: High probability of pitting next lap!") | |
| else: | |
| st.success("Strategy Insight: Likely to stay out.") | |
| except Exception as e: | |
| st.error(f"Prediction Error: {e}") | |
| st.sidebar.markdown("### Model Details") | |
| st.sidebar.write("Target: `PitNextLap`") | |
| st.sidebar.write("Validation AUC: 0.9468") | |
| st.sidebar.write("Public Score: 0.9446") |