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app.py

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+# app.py
+import streamlit as st
+import joblib
+import pandas as pd
+import numpy as np
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.preprocessing import QuantileTransformer, StandardScaler
+from sklearn.cluster import KMeans
+
+seed = 42
+
+selected_features = ['volatile_acidity', 'citric_acid', 'chlorides',
+    'total_sulfur_dioxide','density', 'pH',
+    'sulphates','alcohol', 'total_acidity',
+    'acidity_to_pH_ratio','free_sulfur_dioxide_to_total_sulfur_dioxide_ratio',
+    'alcohol_to_acidity_ratio', 'residual_sugar_to_citric_acid_ratio',
+    'alcohol_to_density_ratio', 'total_alkalinity', 'total_minerals']
+
+def feat_eng(df):
+    df.columns = df.columns.str.replace(' ', '_')
+    df['total_acidity'] = df['fixed_acidity'] + df['volatile_acidity'] + df['citric_acid']
+    df['acidity_to_pH_ratio'] = df['total_acidity'] / df['pH']
+    df['free_sulfur_dioxide_to_total_sulfur_dioxide_ratio'] = df['free_sulfur_dioxide'] / df['total_sulfur_dioxide']
+    df['alcohol_to_acidity_ratio'] = df['alcohol'] / df['total_acidity']
+    df['residual_sugar_to_citric_acid_ratio'] = df['residual_sugar'] / df['citric_acid']
+    df['alcohol_to_density_ratio'] = df['alcohol'] / df['density']
+    df['total_alkalinity'] = df['pH'] + df['alcohol']
+    df['total_minerals'] = df['chlorides'] + df['sulphates'] + df['residual_sugar']
+
+    df = df.replace([np.inf, -np.inf], 0)
+    df = df.dropna()
+    
+    df = df[selected_features]
+    
+    return df
+    
+class CustomQuantileTransformer(BaseEstimator, TransformerMixin):
+    def __init__(self, random_state=None):
+        self.random_state = random_state
+        self.quantile_transformer = QuantileTransformer(output_distribution='normal', random_state=self.random_state)
+
+    def fit(self, X_train, y=None):
+        self.quantile_transformer.fit(X_train)
+        return self
+
+    def transform(self, X):
+        X_transformed = self.quantile_transformer.transform(X)
+        X = pd.DataFrame(X_transformed, columns=X.columns)
+        return X
+
+class CustomStandardScaler(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        self.scaler = StandardScaler()
+
+    def fit(self, X_train, y=None):
+        self.scaler.fit(X_train)
+        return self
+
+    def transform(self, X):
+        X_transformed = self.scaler.transform(X)
+        X = pd.DataFrame(X_transformed, columns=X.columns)
+        return X
+
+class KMeansTransformer(BaseEstimator, TransformerMixin):
+    
+    def __init__(self, n_clusters=3, random_state=seed):
+        self.n_clusters = n_clusters
+        self.random_state = random_state
+        self.kmeans = KMeans(n_clusters=self.n_clusters, random_state=self.random_state)
+   
+    def fit(self, X_train, y=None):
+        self.kmeans.fit(X_train)
+        return self
+    
+    def transform(self, X):
+        X_clustered = pd.DataFrame(X.copy())
+        cluster_labels = self.kmeans.predict(X)
+        X_clustered['Cluster'] = cluster_labels
+        return X_clustered
+
+# Loading the model
+pipe = joblib.load('wine_quality_prediction.pkl')
+
+input_features = [
+    "fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", 
+    "chlorides", "free_sulfur_dioxide", "total_sulfur_dioxide", "density", 
+    "pH", "sulphates", "alcohol"
+]
+
+
+st.title('Wine Quality Predictor Model')
+
+
+def get_user_input():
+    input_dict = {}
+
+    
+    with st.form(key='my_form'):
+        for feat in input_features:
+            input_value = st.number_input(f"Enter value for {feat}", value=0.0, step=0.01)
+            input_dict[feat] = input_value
+
+       
+        submit_button = st.form_submit_button(label='Submit')
+
+    return pd.DataFrame([input_dict]), submit_button
+
+
+user_input, submit_button = get_user_input()
+
+
+# When the 'Submit' button is pressed, perform the prediction
+if submit_button:
+    # Predict wine quality
+    prediction = pipe.predict(user_input)
+    prediction_value = prediction[0]
+
+    # Display the prediction
+    st.header("Predicted Quality")
+    st.write(prediction_value)
+    
+
+st.markdown(
+    """
+    See how this model was created on Kaggle:<br>
+    [🍷 Wine Quality - EDA, Prediction and Deploy](https://www.kaggle.com/code/lusfernandotorres/wine-quality-eda-prediction-and-deploy/notebook)
+    """, unsafe_allow_html=True
+)
+

requirements.txt

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+streamlit
+pandas
+joblib
+catboost
+scikit-learn

wine_quality_prediction.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d32233832b105ead94ce7c430b1a120fd3793091768a4535e8392976ed1d3bb
+size 402943