import streamlit as st
import pandas as pd
import pickle
from utils import create_new_features, normalize, init_new_pred

with open('./trained_model.pkl', 'rb') as file:
    model = pickle.load(file)

# Define min and max values from the dictionaries
min_dict = {
    'bedrooms': 0,
    'bathrooms': 0,
    'sqft_living': 370,
    'sqft_lot': 638,
    'floors': 1,
    'waterfront': 0,
    'view': 0,
    'condition': 1,
    'sqft_above': 370,
    'sqft_basement': 0,
    'yr_built': 1900,
    'yr_renovated': 0,
    'house_age': 0,
    'years_since_renovation': 0
}

max_dict = {
    'bedrooms': 9,
    'bathrooms': 8,
    'sqft_living': 13540,
    'sqft_lot': 1074218,
    'floors': 3,
    'waterfront': 1,
    'view': 4,
    'condition': 5,
    'sqft_above': 9410,
    'sqft_basement': 4820,
    'yr_built': 2014,
    'yr_renovated': 2014,
    'house_age': 114,
    'years_since_renovation': 2014
}
    
# Display the predicted price at the top of the app
price_placeholder.write(f"Predicted Price: ${predicted_price[0][0]:,.2f}")

# Create two columns: one for the city and one for the map
col1, col2 = st.columns([1, 2])  # Adjust the width ratios as needed

# Display city dropdown in the first column
with col1:
    # Placeholder for displaying the predicted price at the top
    price_placeholder = st.empty()
    
    st.subheader('City Selection')
    city = st.selectbox(
        'Select City', 
        ['Algona', 'Auburn', 'Beaux Arts Village', 'Bellevue',
         'Black Diamond', 'Bothell', 'Burien', 'Carnation', 'Clyde Hill',
         'Covington', 'Des Moines', 'Duvall', 'Enumclaw', 'Fall City',
         'Federal Way', 'Inglewood-Finn Hill', 'Issaquah', 'Kenmore',
         'Kent', 'Kirkland', 'Lake Forest Park', 'Maple Valley', 'Medina',
         'Mercer Island', 'Milton', 'Newcastle', 'Normandy Park',
         'North Bend', 'Pacific', 'Preston', 'Ravensdale', 'Redmond',
         'Renton', 'Sammamish', 'SeaTac', 'Seattle', 'Shoreline',
         'Skykomish', 'Snoqualmie', 'Snoqualmie Pass', 'Tukwila', 'Vashon',
         'Woodinville', 'Yarrow Point']
    )

    # Create sliders for each item in the dictionaries
    bedrooms = st.slider('Bedrooms', min_value=min_dict['bedrooms'], max_value=max_dict['bedrooms'], value=min_dict['bedrooms'])
    bathrooms = st.slider('Bathrooms', min_value=min_dict['bathrooms'], max_value=max_dict['bathrooms'], value=min_dict['bathrooms'])
    sqft_living = st.slider('Square Feet (Living)', min_value=min_dict['sqft_living'], max_value=max_dict['sqft_living'], value=min_dict['sqft_living'])
    sqft_lot = st.slider('Square Feet (Lot)', min_value=min_dict['sqft_lot'], max_value=max_dict['sqft_lot'], value=min_dict['sqft_lot'])
    floors = st.slider('Floors', min_value=min_dict['floors'], max_value=max_dict['floors'], value=min_dict['floors'])
    waterfront = st.checkbox('Waterfront', value=False)
    view = st.slider('View', min_value=min_dict['view'], max_value=max_dict['view'], value=min_dict['view'])
    condition = st.slider('Condition', min_value=min_dict['condition'], max_value=max_dict['condition'], value=min_dict['condition'])
    sqft_above = st.slider('Square Feet (Above)', min_value=min_dict['sqft_above'], max_value=max_dict['sqft_above'], value=min_dict['sqft_above'])
    sqft_basement = st.slider('Square Feet (Basement)', min_value=min_dict['sqft_basement'], max_value=max_dict['sqft_basement'], value=min_dict['sqft_basement'])
    yr_built = st.slider('Year Built', min_value=min_dict['yr_built'], max_value=max_dict['yr_built'], value=min_dict['yr_built'])
    yr_renovated = st.slider('Year Renovated', min_value=min_dict['yr_renovated'], max_value=max_dict['yr_renovated'], value=min_dict['yr_renovated'])

    if (bedrooms != min_dict['bedrooms'] or
        bathrooms != min_dict['bathrooms'] or
        sqft_living != min_dict['sqft_living'] or
        sqft_lot != min_dict['sqft_lot'] or
        floors != min_dict['floors'] or
       # waterfront != min_dict['waterfront'] or
        view != min_dict['view'] or
        condition != min_dict['condition'] or
        sqft_above != min_dict['sqft_above'] or
        sqft_basement != min_dict['sqft_basement'] or
        yr_built != min_dict['yr_built'] or
        yr_renovated != min_dict['yr_renovated']):
        
        new_pred = init_new_pred()
        new_pred['bedrooms'] = bedrooms
        new_pred['bathrooms'] = bathrooms
        new_pred['sqft_living'] = sqft_living
        new_pred['sqft_lot'] = sqft_lot
        new_pred['floors'] = floors
        new_pred['waterfront'] = int(waterfront)
        new_pred['view'] = view
        new_pred['condition'] = condition
        new_pred['sqft_above'] = sqft_above
        new_pred['sqft_basement'] = sqft_basement
        new_pred['yr_built'] = yr_built
        new_pred['yr_renovated'] = yr_renovated
        new_pred[f'city_{city}'] = 1
    
        # Process the prediction
        new_pred = pd.DataFrame([new_pred])
        new_pred = create_new_features(new_pred)
        new_pred = normalize(new_pred)
    
        # Predict the price
        predicted_price = model.predict(new_pred)

# Display the map in the second column
with col2:
    if ()
    st.subheader('Map')
    data = pd.DataFrame({
        'latitude': [47.6097, 47.6205, 47.6762],  # Example latitudes
        'longitude': [-122.3331, -122.3493, -122.3198]  # Example longitudes
    })
    st.map(data)