app.py

import streamlit as st
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsRegressor
from geopy.distance import geodesic
import googlemaps
from geopy.exc import GeocoderTimedOut
import locale

def format_money(res: int) -> str:
    """
    Given some int, convert it to currency
    Args:
        res (int): Money input as int
    Returns:
        str: Currency as string
    """
    locale.setlocale(locale.LC_ALL, "pt_BR.UTF-8")
    return locale.currency(res, grouping=True)
    
# Function to calculate distance in meters between two coordinates
def calculate_distance(lat1, lon1, lat2, lon2):
    coords_1 = (lat1, lon1)
    coords_2 = (lat2, lon2)
    return geodesic(coords_1, coords_2).meters

def knn_predict(df, target_column, features_columns, k=5):
    # Separate features and target variable
    X = df[features_columns]
    y = df[target_column]

    # Check if there is enough data for prediction
    if len(X) < k:
        return np.zeros(len(X))  # Return an array of zeros if there isn't enough data

    # Create KNN regressor
    knn = KNeighborsRegressor(n_neighbors=k)

    # Fit the model
    knn.fit(X, y)

    # Use the model to predict target_column for the filtered_data
    predictions = knn.predict(df[features_columns])

    return predictions

# Set wide mode
st.set_page_config(layout="wide")

# Set dark theme
st.markdown(
    """
    <style>
        @font-face {font-family: 'Quicksand';
        src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype');

        }
        body {
            color: white;
            background-color: #1e1e1e;
            font-family: 'Quicksand', sans-serif;
        }
        .st-df-header, .st-df-body, .st-df-caption {
            color: #f8f9fa;  /* Bootstrap table header text color */
        }
        .st-eb {
            background-color: #343a40;  /* Streamlit exception box background color */
        }
    </style>
    """,
    unsafe_allow_html=True
)


# Create a DataFrame with sample data
data = pd.read_excel('ven_fim_PEDÓ_nov_23.xlsx')

# Initialize variables to avoid NameError
radius_visible = True
custom_address_initial = 'Centro, Lajeado - RS, Brazil'  # Initial custom address
custom_lat = data['latitude'].median()
custom_lon = data['longitude'].median()
radius_in_meters = 1500
filtered_data = data  # Initialize with the entire dataset

# Calculate a zoom level based on the maximum distance
zoom_level = 14

# Set font to 'Quicksand' for title_html
title_html = """
        <style>
        @font-face {font-family: 'Quicksand';
        src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype');
        }
        body {{
            font-family: 'Quicksand', sans-serif;
        }}
    </style>
    <span style='color: gray; font-size: 50px;'>aval</span>
    <span style='color: white; font-size: 50px;'>ia</span>
    <span style='color: gray; font-size: 50px;'>.NEXUS</span>
    """

# Set font to 'Quicksand' for factor_html
factor_html = """
    <style>
        @font-face {font-family: 'Quicksand';
        src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype');
        }
        body {{
            font-family: 'Quicksand', sans-serif;
        }}
    </style>
    <a href='https://huggingface.co/spaces/DavidSB/avaliaFACTOR' target='_blank' style='text-decoration: none; color: inherit;'>
    <span style='color: gray; font-size: 20px;'>aval</span>
    <span style='color: white; font-size: 20px;'>ia</span>
    <span style='color: gray; font-size: 20px;'>.FACTOR</span>
"""

# Set font to 'Quicksand' for evo_html
evo_html = """
    <style>
        @font-face {font-family: 'Quicksand';
        src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype');
        }
        body {{
            font-family: 'Quicksand', sans-serif;
        }}
    </style>
    <a href='https://huggingface.co/spaces/DavidSB/avalia.EVO' target='_blank' style='text-decoration: none; color: inherit;'>
    <span style='color: gray; font-size: 20px;'>aval</span>
    <span style='color: white; font-size: 20px;'>ia</span>
    <span style='color: gray; font-size: 20px;'>.EVO</span>
"""


# Create a sidebar for controls
with st.sidebar:
    st.sidebar.markdown(title_html, unsafe_allow_html=True)

    # Add a dropdown for filtering "Tipo"
    selected_tipo = st.selectbox('Filtrar por Tipo', data['Tipo'].unique())

    data_tipo = data[data['Tipo'] == selected_tipo]
    
    custom_address = st.text_input('Informe o endereço', custom_address_initial)
    radius_visible = True  # Show radius slider for custom coordinates

    # Geocode the custom address using the Google Maps API
    gmaps = googlemaps.Client(key='AIzaSyDoJ6C7NE2CHqFcaHTnhreOfgJeTk4uSH0')  # Replace with your API key

    try:
        location = gmaps.geocode(custom_address)[0]['geometry']['location']
        custom_lat, custom_lon = location['lat'], location['lng']
    except (IndexError, GeocoderTimedOut):
        st.error("Erro: Não foi possível geocodificar o endereço fornecido. Por favor, verifique e tente novamente.")

    # Slider for setting the zoom level
    zoom_level = st.slider('Nível de zoom', min_value=1, max_value=15, value=zoom_level)

    # Conditionally render the radius slider
    if radius_visible:
        radius_in_meters = st.slider('Selecione raio (em metros)', min_value=100, max_value=5000, value=1000)

    # Initialize sliders variables
    dorm_range = (int(data_tipo['Dorm'].min()), int(data_tipo['Dorm'].max()))
    banho_range = (int(data_tipo['Banheiro'].min()), int(data_tipo['Banheiro'].max()))
    vaga_range = (int(data_tipo['Vaga'].min()), int(data_tipo['Vaga'].max()))

    # Add sliders to filter data based
    atotal_range = st.slider('Área Total', float(data_tipo['Atotal'].min()), float(data_tipo['Atotal'].max()), (float(data_tipo['Atotal'].min()), float(data_tipo['Atotal'].max())), step=.1 if data_tipo['Atotal'].min() != data_tipo['Atotal'].max() else 0.1)
    apriv_range = st.slider('Área Privativa', float(data_tipo['Apriv'].min()), float(data_tipo['Apriv'].max()), (float(data_tipo['Apriv'].min()), float(data_tipo['Apriv'].max())), step=.1 if data_tipo['Apriv'].min() != data_tipo['Apriv'].max() else 0.1)

    if int(data_tipo['Dorm'].min()) != 0 and int(data_tipo['Dorm'].max()) != 0:
        dorm_range = st.slider('Dormitórios', int(data_tipo['Dorm'].min()), int(data_tipo['Dorm'].max()), (int(data_tipo['Dorm'].min()), int(data_tipo['Dorm'].max())), step=1 if data_tipo['Dorm'].min() != data_tipo['Dorm'].max() else 1)
    if int(data_tipo['Banheiro'].min()) != 0 and int(data_tipo['Banheiro'].max()) != 0:
        banho_range = st.slider('Banheiros', int(data_tipo['Banheiro'].min()), int(data_tipo['Banheiro'].max()), (int(data_tipo['Banheiro'].min()), int(data_tipo['Banheiro'].max())), step=1 if data_tipo['Banheiro'].min() != data_tipo['Banheiro'].max() else 1)
    if int(data_tipo['Vaga'].min()) != 0 and int(data_tipo['Vaga'].max()) != 0:
        vaga_range = st.slider('Vaga de estacionamento', int(data_tipo['Vaga'].min()), int(data_tipo['Vaga'].max()), (int(data_tipo['Vaga'].min()), int(data_tipo['Vaga'].max())), step=1 if data_tipo['Vaga'].min() != data_tipo['Vaga'].max() else 1)
    
    # Initialize checkbox variables
    elev_checkbox = False
    churr_checkbox = False
    esq_checkbox = False
        
        # Add checkboxes for dummy features
    if int(data_tipo['Elevador'].min()) != 0 and int(data_tipo['Elevador'].max()) != 0:
        elev_checkbox = st.checkbox('Elevador')
    if int(data_tipo['Churrasq'].min()) != 0 and int(data_tipo['Churrasq'].max()) != 0:
        churr_checkbox = st.checkbox('Churrasqueira')
    if int(data_tipo['Lot_pos'].min()) != 0 and int(data_tipo['Lot_pos'].max()) != 0:
        esq_checkbox = st.checkbox('Duas ou mais frentes')

    # Transform checkbox values into 1s and 0s
    elev_value = 1 if elev_checkbox else 0
    churr_value = 1 if churr_checkbox else 0
    esq_value = 1 if esq_checkbox else 0

    data_tipo = data_tipo[(data_tipo['Atotal'].between(atotal_range[0], atotal_range[1])) &
            (data_tipo['Apriv'].between(apriv_range[0], apriv_range[1])) &
            (data_tipo['Dorm'].between(dorm_range[0], dorm_range[1])) &
            (data_tipo['Banheiro'].between(banho_range[0], banho_range[1])) &
            (data_tipo['Vaga'].between(vaga_range[0], vaga_range[1])) &  
            (data_tipo['Elevador'] == elev_value) &
            (data_tipo['Churrasq'] == churr_value) &
            (data_tipo['Lot_pos'] == esq_value)]


    # Links to other apps at the bottom of the sidebar
    st.sidebar.markdown(factor_html, unsafe_allow_html=True)
    st.sidebar.markdown(evo_html, unsafe_allow_html=True)


filtered_data = data_tipo[data_tipo.apply(lambda x: calculate_distance(x['latitude'], x['longitude'], custom_lat, custom_lon), axis=1) <= radius_in_meters]
filtered_data = filtered_data.dropna()  # Drop rows with NaN values

# Add a custom CSS class to the map container
st.markdown(f"""<style>
.map {{
  width: 100%;
  height: 100vh;
}}
</style>""", unsafe_allow_html=True)

# Determine which area feature to use for prediction
filtered_data['area_feature'] = np.where(filtered_data['Apriv'] != 0, filtered_data['Apriv'], filtered_data['Atotal'])

# Define the target column based on conditions
filtered_data['target_column'] = np.where(filtered_data['Vunit_priv'] != 0, filtered_data['Vunit_priv'], filtered_data['Vunit_total'])

# Apply KNN and get predicted target values
predicted_target = knn_predict(filtered_data, 'target_column', ['latitude', 'longitude', 'area_feature'])  # Update with your features

# Add predicted target values to filtered_data
filtered_data['Predicted_target'] = predicted_target

# Display the map and filtered_data
with st.container():
    st.map(filtered_data, zoom=zoom_level, use_container_width=True)
    st.write("Dados:", filtered_data)  # Debug: Print filtered_data

k_threshold = 5

# Function to perform bootstrap on the predicted target values
def bootstrap_stats(predicted_target, num_samples=1000):
    # Reshape the predicted_target array
    predicted_target = np.array(predicted_target).reshape(-1, 1)

    # Bootstrap resampling
    bootstrapped_means = []
    for _ in range(num_samples):
        bootstrap_sample = np.random.choice(predicted_target.flatten(), len(predicted_target), replace=True)
        bootstrapped_means.append(np.mean(bootstrap_sample))

    # Calculate lower and higher bounds
    lower_bound = np.percentile(bootstrapped_means, 2.5)
    higher_bound = np.percentile(bootstrapped_means, 97.5)

    # Calculate the mean value
    mean_value = np.mean(bootstrapped_means)

    return lower_bound, higher_bound, mean_value

# Apply KNN and get predicted Predicted_target values
predicted_target = knn_predict(filtered_data, 'Predicted_target', ['latitude', 'longitude', 'area_feature'])

# Check if there are predictions to display
if 'Predicted_target' in filtered_data.columns and not np.all(predicted_target == 0):
    # Add predicted Predicted_target values to filtered_data
    filtered_data['Predicted_target'] = predicted_target

    # Apply bootstrap on the predicted values
    lower_bound, higher_bound, mean_value = bootstrap_stats(predicted_target)

    # Display the results with custom styling
    st.markdown("## **Resultado da Análise Estatística**")
    st.write(f"**Valor médio (R$/m²) para as características selecionadas:** {format_money(mean_value)}")
    st.write(f"**Os valores podem variar entre {format_money(lower_bound)} e {format_money(higher_bound)} dependendo das características dos imóveis.**")
else:
    st.warning(f"**Dados insuficientes para inferência do valor. Mínimo necessário:** {k_threshold}")