import streamlit as st
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# -------------------------- Page Config --------------------------
st.set_page_config(
    page_title="Wine Quality Master 🍷",
    page_icon="🍇",
    layout="centered",
    initial_sidebar_state="expanded"
)

# -------------------------- Custom CSS - Dark Purple Magic --------------------------
st.markdown("""
<style>
    .main {background: #0f001a; color: #e6e6fa;}
    .stApp {background: linear-gradient(135deg, #2a0052, #000000);}
    
    h1, h2 {background: linear-gradient(90deg, #9b59b6, #e91e63, #ff9800); 
             -webkit-background-clip: text; -webkit-text-fill-color: transparent;}
    .wine-red {color: #c0392b;}
    .wine-white {color: #f1c40f;}
    .prediction-good {font-size: 2.5rem; font-weight: bold; text-align: center; 
                      color: #8e44ad; text-shadow: 0 0 20px #9b59b6;}
</style>
""", unsafe_allow_html=True)

# -------------------------- Load Full Wine Quality Dataset (Red + White) --------------------------
@st.cache_data
def load_wine_data():
    # Red wine
    red = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv", sep=";")
    red["type"] = "Red"
    
    # White wine
    white = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv", sep=";")
    white["type"] = "White"
    
    # Combine
    df = pd.concat([red, white], ignore_index=True)
    
    # Binary classification: Good (>=6), Bad (<6)
    df["is_good"] = (df["quality"] >= 6).astype(int)
    
    return df

df = load_wine_data()

# -------------------------- Hero Section --------------------------
col1, col2, col3 = st.columns([1,3,1])
with col2:

    st.markdown("<h1 style='text-align:center;'>Wine Quality Master</h1>", unsafe_allow_html=True)
    st.markdown("<h2 style='text-align:center;'>VinoVerdict</h2>", unsafe_allow_html=True)
    st.markdown("<p style='text-align:center; font-size:1.4rem; opacity:0.9;'>Red or White – Will it be divine... or declined?</p>", unsafe_allow_html=True)

st.markdown("---")

# -------------------------- Dataset Info --------------------------
with st.container():
    st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
    col1, col2, col3, col4 = st.columns(4)
    with col1:
        st.metric("Total Wines", f"{len(df):,}")
    with col2:
        st.metric("Red Wines", f"{len(df[df['type']=='Red']):,}", "Red Wine")
    with col3:
        st.metric("White Wines", f"{len(df[df['type']=='White']):,}", "White Wine")
    with col4:
        st.metric("Good Wines (≥6)", f"{df['is_good'].sum():,}")
    
    st.markdown("<br>", unsafe_allow_html=True)
    st.dataframe(df.head(), use_container_width=True)
    st.markdown("</div>", unsafe_allow_html=True)

# -------------------------- Prepare Features --------------------------
X = df.drop(columns=["quality", "is_good"])
y = df["is_good"]

# One-hot encode wine type (ensures consistent column order)
X = pd.get_dummies(X, columns=["type"], drop_first=False)

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

# Scale numerical features (preserve exact column order)
scaler = StandardScaler()
numerical_cols = [col for col in X.columns if col not in ["type_Red", "type_White"]]
X_train_num_scaled = scaler.fit_transform(X_train[numerical_cols])
X_train = pd.concat([
    pd.DataFrame(X_train_num_scaled, columns=numerical_cols, index=X_train.index),
    X_train[["type_Red", "type_White"]]
], axis=1)

X_test_num_scaled = scaler.transform(X_test[numerical_cols])
X_test = pd.concat([
    pd.DataFrame(X_test_num_scaled, columns=numerical_cols, index=X_test.index),
    X_test[["type_Red", "type_White"]]
], axis=1)

# Train model
@st.cache_resource
def train_model():
    model = RandomForestClassifier(
        n_estimators=1000,
        max_depth=15,
        random_state=42,
        n_jobs=-1,
        class_weight="balanced"
    )
    model.fit(X_train, y_train)
    return model

model = train_model()

# Accuracy
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

with st.container():
    st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
    st.success(f"Model Accuracy on Test Set: *{accuracy:.4f}* ({accuracy*100:.2f}%)")
    st.markdown("</div>", unsafe_allow_html=True)

# -------------------------- Interactive Prediction --------------------------
st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
st.header("Predict Your Wine's Destiny")

# Wine type selector
wine_type = st.selectbox("Choose Wine Type", options=["Red", "White"], index=0)

col1, col2 = st.columns(2)
input_data = {"type_Red": 0, "type_White": 0}
input_data[f"type_{wine_type}"] = 1

features = [col for col in X.columns if col not in ["type_Red", "type_White"]]

for i, feature in enumerate(features):
    col = col1 if i % 2 == 0 else col2
    with col:
        min_v, max_v = float(df[feature].min()), float(df[feature].max())
        mean_v = float(df[feature].mean())
        val = st.slider(
            feature.replace("_", " ").title(),
            min_value=min_v,
            max_value=max_v,
            value=mean_v,
            step=0.1,
            format="%.2f"
        )
        input_data[feature] = val

if st.button("Reveal the Quality!", use_container_width=True, type="primary"):
    input_df = pd.DataFrame([input_data])
    
    # Scale numerical columns (FIX: Reconstruct to match exact column order)
    input_num_scaled = scaler.transform(input_df[numerical_cols])
    input_scaled = pd.concat([
        pd.DataFrame(input_num_scaled, columns=numerical_cols, index=input_df.index),
        input_df[["type_Red", "type_White"]]
    ], axis=1)
    
  
    
    pred = model.predict(input_scaled)[0]
    prob = model.predict_proba(input_scaled)[0]
    
    st.markdown("<br>", unsafe_allow_html=True)
    
    if pred == 1:
        st.balloons()
        st.markdown(f"<h2 class='prediction-good'>OUTSTANDING WINE! {wine_type} Wine</h2>", unsafe_allow_html=True)
        st.success(f"Confidence: *{prob[1]:.1%}* – This belongs in a museum... or your glass right now!")
    else:
        st.error(f"Not quite a masterpiece... {wine_type} Wine")
        st.warning(f"Confidence: *{prob[0]:.1%}* – Maybe use it for cooking?")
    
    # Feature importance hint
    st.info(f"Pro tip: For {wine_type.lower()} wines, alcohol, sulphates, and volatile acidity matter most!")

st.markdown("</div>", unsafe_allow_html=True)

# -------------------------- Footer --------------------------
st.markdown("---")
st.caption("Made with passion | Dataset: UCI Wine Quality (Red + White) | Model: Random Forest |Created by: MAN JETANI")