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app.py

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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.
+""")

requirements.txt

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+altair==5.3.0
+attrs==23.2.0
+blinker==1.8.2
+cachetools==5.3.3
+certifi==2024.6.2
+charset-normalizer==3.3.2
+click==8.1.7
+filelock==3.15.4
+fsspec==2024.6.0
+gitdb==4.0.11
+GitPython==3.1.43
+huggingface-hub==0.23.4
+idna==3.7
+Jinja2==3.1.4
+joblib==1.4.2
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+llvmlite==0.41.1
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+mdurl==0.1.2
+numba==0.58.1
+numpy==1.25.2
+packaging==24.1
+pandas==2.2.2
+pillow==10.3.0
+protobuf==5.27.1
+pyarrow==16.1.0
+pydeck==0.9.1
+Pygments==2.18.0
+python-dateutil==2.9.0.post0
+pytz==2024.1
+PyYAML==6.0.1
+referencing==0.35.1
+requests==2.32.3
+rich==13.7.1
+rpds-py==0.18.1
+scikit-learn==1.2.2
+scipy==1.13.1
+six==1.16.0
+sklearn-pandas==2.2.0
+smmap==5.0.1
+streamlit==1.36.0
+tenacity==8.4.1
+threadpoolctl==3.5.0
+toml==0.10.2
+toolz==0.12.1
+tornado==6.4.1
+tqdm==4.66.4
+typing_extensions==4.12.2
+tzdata==2024.1
+urllib3==2.2.2
+xgboost==2.0.3

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:51618a02bb9ce18f78466307260c7634cf8b2114983b3472bb8275ce894fa2f0
+size 718