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| import pickle | |
| import streamlit as st | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| # Function to load the scaler | |
| def load_scaler(): | |
| with open('scaler.pkl', 'rb') as file: | |
| scaler = pickle.load(file) | |
| return scaler | |
| # Function to preprocess user input | |
| def preprocess_input(user_input, scaler): | |
| user_input_df = pd.DataFrame([user_input], columns=feature_names) | |
| scaled_input = scaler.transform(user_input_df) | |
| return pd.DataFrame(scaled_input, columns=user_input_df.columns) | |
| # Function to load the model | |
| def load_model(): | |
| model_path = hf_hub_download(repo_id="elladeandra/sports-prediction", filename="ensemble_model.pkl") | |
| with open(model_path, 'rb') as file: | |
| model = pickle.load(file) | |
| return model | |
| # Define feature names | |
| feature_names = ['value_eur', 'age', 'potential', 'movement_reactions', 'wage_eur'] | |
| # Streamlit app title and description | |
| st.title('Football Player Rating Predictor') | |
| st.markdown(""" | |
| This application predicts the rating of a football player based on their attributes using an ensemble model. | |
| The model combines Random Forest, Gradient Boosting, and XGBoost algorithms for robust predictions. | |
| """) | |
| # Sidebar for user input | |
| st.sidebar.header('Input Player Attributes') | |
| def get_user_input(): | |
| value_eur = st.sidebar.number_input('Market Value (EUR)', min_value=0, max_value=int(1e9), value=int(1e6)) | |
| wage_eur = st.sidebar.number_input('Weekly Wage (EUR)', min_value=0, max_value=int(1e9), value=int(1e6)) | |
| age = st.sidebar.slider('Player Age', 16, 40, 25) | |
| potential = st.sidebar.slider('Potential Score', 1, 100, 50) | |
| movement_reactions = st.sidebar.slider('Reactions', 1, 100, 50) | |
| data = { | |
| 'value_eur': value_eur, | |
| 'wage_eur': wage_eur, | |
| 'age': age, | |
| 'potential': potential, | |
| 'movement_reactions': movement_reactions | |
| } | |
| return data | |
| user_input = get_user_input() | |
| try: | |
| # Load scaler and preprocess input | |
| scaler = load_scaler() | |
| scaled_input = preprocess_input(user_input, scaler) | |
| # Load model and predict | |
| model = load_model() | |
| predicted_rating = model.predict(scaled_input) | |
| # Display prediction | |
| st.subheader('Predicted Player Rating') | |
| st.write(f"Estimated Rating: {predicted_rating[0]:.1f}") | |
| # Explanation section | |
| if st.button('About the Prediction'): | |
| st.markdown(""" | |
| This application uses an ensemble model combining Random Forest, Gradient Boosting, and XGBoost algorithms to predict football player ratings. | |
| The model is trained on data from the FIFA video game series, which includes attributes such as age, potential, market value, and reaction times. | |
| **Note**: This is a demo project and should not be used for professional scouting or analysis purposes. | |
| """) | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| # Additional features for better user experience | |
| if st.sidebar.button('Reset Inputs'): | |
| st.experimental_rerun() | |
| st.sidebar.markdown(""" | |
| **Instructions**: | |
| - Adjust the player attributes using the input fields. | |
| - Click the 'Predict' button to see the estimated rating. | |
| - Use the 'About the Prediction' button for more information. | |
| """) | |