Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import pandas as pd | |
| from data_fetcher import get_last_n_races_results, get_next_race_info | |
| from feature_engineering import FeatureEngineer | |
| from model_trainer import ModelTrainer | |
| st.title("🏁 F1 Simple Race Predictor") | |
| n_races = st.number_input("🔢 How many past races should the model use?", min_value=1, max_value=10, value=5) | |
| if st.button(f"Fetch Last {n_races} Race Results"): | |
| race_results = get_last_n_races_results(n_races) | |
| if not race_results.empty: | |
| st.session_state['race_results'] = race_results | |
| st.success("✅ Race results fetched!") | |
| st.dataframe(race_results) | |
| unique_circuits = race_results['CircuitName'].unique() | |
| st.info(f"ℹ️ Using results from **{len(unique_circuits)} different circuits**.") | |
| else: | |
| st.error("❌ No race results found.") | |
| if 'race_results' in st.session_state: | |
| fe = FeatureEngineer(n_races=n_races) | |
| try: | |
| features, labels = fe.prepare_features(st.session_state['race_results']) | |
| st.session_state['features'] = features | |
| st.session_state['labels'] = labels | |
| except Exception as e: | |
| st.error(f"❌ Error preparing features: {e}") | |
| if st.button("Train Model"): | |
| trainer = ModelTrainer() | |
| try: | |
| X = st.session_state['features'] | |
| y = st.session_state['labels'] | |
| trainer.train(X, y) | |
| st.session_state['trainer'] = trainer | |
| st.success("✅ Model trained successfully!") | |
| except Exception as e: | |
| st.error(f"❌ Error during training: {e}") | |
| if 'trainer' in st.session_state and st.button("Predict Next Race"): | |
| next_race = get_next_race_info() | |
| if next_race is not None and 'EventName' in next_race: | |
| race_name = next_race['EventName'] | |
| st.subheader(f"🏎️ Predictions for: {race_name}") | |
| st.info(f"🔮 Predicting race results for **{race_name}**!") | |
| else: | |
| st.subheader("🏎️ Predictions for: (Unknown Upcoming Race)") | |
| st.warning("⚠️ Next race information is missing.") | |
| try: | |
| prediction_features = fe.prepare_prediction_features(st.session_state['race_results']) | |
| preds = st.session_state['trainer'].predict(prediction_features) | |
| drivers = st.session_state['race_results']['DriverId'].unique() | |
| prediction_df = pd.DataFrame({ | |
| 'Driver': drivers, | |
| 'Predicted Finish Position': preds | |
| }).sort_values('Predicted Finish Position') | |
| st.write("### 🏁 Predicted Race Results") | |
| st.dataframe(prediction_df) | |
| except Exception as e: | |
| st.error(f"❌ Error during prediction: {e}") | |