import streamlit as st
import pandas as pd
from sklearn.preprocessing import LabelEncoder

import tensorflow as tf
from tensorflow import keras
from keras.metrics import Precision, Recall
import numpy as np

# Load dataset directly from file path
df = pd.read_csv(r"Assessment - Form Responses.csv")

# Streamlit config
st.set_page_config(page_title=" Match Prediction Using Personality and Lifestyle Data", layout="centered")
st.title("Match Prediction Using Personality and Lifestyle Data")

# Drop unwanted columns
if "Timestamp" in df.columns and "Email Address" in df.columns:
    df.drop(columns=["Timestamp", "Email Address"], inplace=True)

# Show data
st.subheader("📋 Raw Data")
st.write(df.head())

# Save original for UI
original_df = df.copy()

# Label Encoding
encoders = {}
for col in df.columns:
    le = LabelEncoder()
    df[col] = le.fit_transform(df[col])
    encoders[col] = le

# Features and labels (last column = label)
x = df.iloc[:, :-1]
y = df.iloc[:, -1]

# Build ANN
model = keras.Sequential([
    keras.layers.Input(shape=(x.shape[1],)),
    keras.layers.Dense(8, kernel_initializer=keras.initializers.GlorotNormal(seed=42)),
    keras.layers.PReLU(),
    keras.layers.Dense(3, kernel_initializer=keras.initializers.GlorotNormal(seed=42),
                       kernel_regularizer=keras.regularizers.L2()),
    keras.layers.PReLU(),
    keras.layers.BatchNormalization(),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(1, activation="sigmoid", kernel_initializer=keras.initializers.HeNormal(seed=42))
])

model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy", Precision(), Recall()])

# Train model
if st.button("🚀 Train Model"):
    checkpoint_cb = keras.callbacks.ModelCheckpoint(
        "best_model.h5", monitor="val_loss", save_best_only=True, mode="min"
    )

    with st.spinner("Training in progress..."):
        history = model.fit(x, y, validation_split=0.2, batch_size=8, epochs=30,
                            callbacks=[checkpoint_cb], verbose=0)

    st.success("✅ Model training completed!")

# Prediction section
st.subheader("💡 Make a Prediction")

user_inputs = {}
for col in x.columns:
    options = encoders[col].classes_
    selected = st.selectbox(f"Select {col}:", options)
    user_inputs[col] = encoders[col].transform([selected])[0]

if st.button("🔮 Predict Match"):
    input_df = pd.DataFrame([user_inputs.values()], columns=user_inputs.keys())
    prediction = model.predict(input_df)[0][0]

    if prediction >= 0.5:
        st.success(f"💘 It's a Match! (Confidence: {round(prediction * 100, 2)}%)")
    else:
        st.warning(f"💔 No Match. (Confidence: {round((1 - prediction) * 100, 2)}%)")