app.py

# 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
)