Initial commit

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import LabelEncoder
+
+def predict_grand_prix_winner(previous_season_data):
+    """
+    Predicts the grand prix winner for the 2025 season based on previous season data.
+
+    Args:
+        previous_season_data (pd.DataFrame): DataFrame containing previous season results.
+
+    Returns:
+        pd.DataFrame: DataFrame containing predicted results for the 2025 season.
+    """
+
+    # Preprocessing
+    le_driver = LabelEncoder()
+    le_team = LabelEncoder()
+    previous_season_data['Driver_encoded'] = le_driver.fit_transform(previous_season_data['Driver'])
+    previous_season_data['Team_encoded'] = le_team.fit_transform(previous_season_data['Team'])
+
+    # Feature Engineering
+    features = ['Driver_encoded', 'Team_encoded', 'Points', 'Starting Grid']
+    target = 'Position'
+
+    # Model Training
+    model = LinearRegression()
+    model.fit(previous_season_data[features], previous_season_data[target])
+
+    # Create a DataFrame for 2025 predictions
+    last_race = previous_season_data.groupby('Driver').last().reset_index()
+
+    # Predict positions for 2025
+    predictions = model.predict(last_race[features])
+    last_race['Predicted_Position'] = predictions
+    last_race['Predicted_Position'] = last_race['Predicted_Position'].round().astype(int)
+
+    # Decode labels back to original names
+    last_race['Driver'] = le_driver.inverse_transform(last_race['Driver_encoded'])
+    last_race['Team'] = le_team.inverse_transform(last_race['Team_encoded'])
+
+    # Sort by predicted position
+    predicted_results = last_race[['Driver', 'Team', 'Predicted_Position']].sort_values(by='Predicted_Position')
+
+    return predicted_results
+
+def main():
+    st.title("2025 Grand Prix Winner Prediction")
+
+    st.write("Upload the previous season's results (CSV format).")
+
+    # Modified file_uploader to accept CSV
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+    # Input for GP name and date
+    gp_name = st.text_input("Enter the Grand Prix Name (e.g., Australian Grand Prix):")
+    gp_date = st.date_input("Enter the Date of the Grand Prix:")
+
+    if uploaded_file is not None:
+        try:
+            previous_season_data = pd.read_csv(uploaded_file)
+            st.write("Uploaded data:")
+            st.dataframe(previous_season_data)
+
+            # Ensure necessary columns exist
+            required_columns = ['Driver', 'Team', 'Points', 'Position', 'Starting Grid']
+            if not all(col in previous_season_data.columns for col in required_columns):
+                st.error(f"Error: CSV must contain the following columns: {', '.join(required_columns)}")
+                return
+
+            if st.button("Predict 2025 Winners"):
+                predicted_results = predict_grand_prix_winner(previous_season_data)
+                st.write("Predicted 2025 Grand Prix Results:")
+                st.dataframe(predicted_results)
+                if not predicted_results.empty:
+                    winner = predicted_results.iloc[0]['Driver']
+                    team = predicted_results.iloc[0]['Team']
+                    # Display the prediction with GP name and date
+                    st.success(f"2025 {gp_name} Winner ({gp_date.strftime('%Y-%m-%d')}): {winner} from team {team}")
+
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+    else:
+        st.info("Please upload a CSV file with the previous season's results.")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+pandas
+scikit-learn