app.py

import gradio as gr
import shap
from model import UhiModel
from explainer import UhiExplainer
import numpy as np
import pandas as pd
import plotly.graph_objects as go

MODEL = UhiModel("mixed_buffers_ResNet_model.keras","mixed_buffers_standard_scaler.pkl")

def filter_map(uhi, longitude, latitude):
    '''
    This function generates a map based on uhi prediction
    '''
    #set up custom data
    data = [uhi, longitude, latitude]

    # Create the plot
    fig = go.Figure(go.Scattermapbox(
        lat=latitude,
        lon=longitude,
        mode='markers',
        marker=go.scattermapbox.Marker(
            size=6
        ),
        hoverinfo="text",
        hovertemplate='<b>UHI Index</b>: %{customdata[0]}<br><b>long</b>: %{customdata[1]}<br><b>lat</b>: %{customdata[2]}<br>',
        customdata=data
    ))

    fig.update_layout(
        mapbox_style="open-street-map",
        hovermode='closest',
        mapbox=dict(
            bearing=0,
            center=go.layout.mapbox.Center(
                lat=40.7128,
                lon=-74.0060  # Default to New York City for initial view
            ),
            pitch=0,
            zoom=10
        ),
    )

    return fig

def predict(
        longitude, latitude, m50_NPCRI, m100_Ground_Elevation, avg_wind_speed, 
        wind_direction, traffic_volume, m150_Ground_Elevation, 
        relative_humidity, m150_NDVI, m150_NDBI, 
        m300_SI, m300_NPCRI, m300_Coastal_Aerosol, 
        m300_Total_Building_Area_m2, m300_Building_Construction_Year, m300_Ground_Elevation, 
        m300_Building_Height, m300_Building_Count, m300_NDVI,
        m300_NDBI, m300_Building_Density, solar_flux
    ):
    '''
    Predict the UHI index for the data inputed, Longitude and Latitude are used to generate a map
    and do not affect the UHI index prediction.
    '''

    # Create a dictionary with input data and dataset var names
    input_data = {
        "50m_1NPCRI": m50_NPCRI,
        "100m_Ground_Elevation": m100_Ground_Elevation,
        "Avg_Wind_Speed": avg_wind_speed,
        "Wind_Direction": wind_direction,
        "Traffic_Volume": traffic_volume,
        "150m_Ground_Elevation": m150_Ground_Elevation,
        "Relative_Humidity": relative_humidity,
        "150m_NDVI": m150_NDVI,
        "150m_NDBI": m150_NDBI,
        "300m_SI": m300_SI,
        "300m_NPCRI": m300_NPCRI,
        "300m_Coastal_Aerosol": m300_Coastal_Aerosol,
        "300m_Total_Building_Area_m2": m300_Total_Building_Area_m2,
        "300m_Building_Construction_Year": m300_Building_Construction_Year,
        "300m_Ground_Elevation": m300_Ground_Elevation,
        "300m_Building_Height": m300_Building_Height,
        "300m_Building_Count": m300_Building_Count,
        "300m_NDVI": m300_NDVI,
        "300m_NDBI": m300_NDBI,
        "300m_Building_Density": m300_Building_Density,
        "Solar_Flux": solar_flux
    }
    
    # Convert to DataFrame
    input_df = pd.DataFrame(input_data, index=[0])

    #predict
    uhi_index = MODEL.predict(input_df)

    # explain the prediction
    explainer = UhiExplainer(
        model=MODEL.model,
        explainer_type=shap.DeepExplainer,
        X=input_df,
        feature_names=input_df.columns,
        ref_data=input_df,
        shap_values=None  # Compute SHAP values on the fly
    )
    reason = explainer.reasoning(index=0, location=(longitude, latitude))

    # generate map
    plot = filter_map(uhi_index, longitude, latitude)

    return uhi_index, reason["uhi_status"], reason["feature_contributions"], plot

def load_examples(csv_file):
    '''
    Load examples from csv file
    '''
    # Read examples from CSV file
    df = pd.read_csv(csv_file)

    # Convert DataFrame to a list of lists
    examples = df.values.tolist()

    return examples

def load_interface(): 
    '''
    Configure Gradio interface
    '''

    #set blocks
    info_page = gr.Blocks()

    with info_page:
        # set title and description
        gr.Markdown(
        """
        # ResNet model for Predicting Urban Heat Island (UHI) Index
        
        **Contributors**: Francisco Lozano, Dalton Knapp, Adam Zizi\n
        **University**: Depaul University\n

        ## Overview
        Our project focused on creating a micro-scale machine learning model that predicts the locations and severity of the UHI effect.
        The model used various datasets, including near-surface air temperatures, building footprint data, weather data, and 
        satellite data, to identify key drivers of UHI. This model provides insights into urban areas that are most affected by UHI, 
        enabling urban planners and policymakers to take effective mitigation actions.
        >NOTE: The longitude and latitude inputs are used to identify the location of the prediction, but they do not affect the UHI index prediction.\n

        ## Repository
        The code for this project is available on GitHub. It includes the model training, evaluation, and prediction scripts, as well as
        the datasets used for training and testing. The repository also contains Jupyter notebooks that provide detailed explanations of the model's
        architecture, training process, and evaluation metrics. The notebooks include visualizations of the model's performance and feature importance analysis.\n
        [Project Repo](https://github.com/FranciscoLozCoding/cooling_with_code)
        """
        )
    
    # set inputs and outputs for the model
    longitude = gr.Number(label="Longitude", precision=5, info="The Longitude of the location")
    latitude = gr.Number(label="Latitude", precision=5, info="The Latitude of the location")
    m50_NPCRI = gr.Number(label="50m NPCRI", precision=5, info="The average Normalized Difference Vegetation Index in a 50m Buffer Zone")
    m100_Ground_Elevation = gr.Number(label="100m Ground Elevation", precision=5, info="The average Ground Elevation in a 100m Buffer Zone")
    avg_wind_speed = gr.Number(label="Avg Wind Speed [m/s]", precision=5, info="The average Wind Speed at the location")
    wind_direction = gr.Number(label="Wind Direction [degrees]", precision=5, info="The average Wind Direction at the location")
    traffic_volume = gr.Number(label="Traffic Volume", precision=5, info="The Traffic Volume at the location")
    m150_Ground_Elevation = gr.Number(label="150m Ground Elevation", precision=5, info="The average Ground Elevation in a 150m Buffer Zone")
    relative_humidity = gr.Number(label="Relative Humidity [percent]", precision=5, info="The average Relative Humidity at the location")
    m150_NDVI = gr.Number(label="150m NDVI", precision=5, info="The average Normalized Difference Vegetation Index in a 150m Buffer Zone")
    m150_NDBI = gr.Number(label="150m NDBI", precision=5, info="The average Normalized Difference Built-up Index in a 150m Buffer Zone")
    m300_SI = gr.Number(label="300m SI", precision=5, info="The average Shadow Index in a 300m Buffer Zone")
    m300_NPCRI = gr.Number(label="300m NPCRI", precision=5, info="The average Normalized Pigment Chlorophyll Ratio Index in a 300m Buffer Zone")
    m300_Coastal_Aerosol = gr.Number(label="300m Coastal Aerosol", precision=5, info="The average Coastal Aerosol in a 300m Buffer Zone")
    m300_Total_Building_Area_m2 = gr.Number(label="300m Total Building Area(m2)", precision=5, info="The Total Building Area in a 300m Buffer Zone")
    m300_Building_Construction_Year = gr.Number(label="300m Building Construction Year", precision=5, info="The average Building Construction Year in a 300m Buffer Zone")
    m300_Ground_Elevation = gr.Number(label="300m Ground Elevation", precision=5, info="The average Ground Elevation in a 300m Buffer Zone")
    m300_Building_Height = gr.Number(label="300m Building Height", precision=5, info="The average Building Height in a 300m Buffer Zone")
    m300_Building_Count = gr.Number(label="300m Building Count", precision=5, info="The average Building Count in a 300m Buffer Zone")
    m300_NDVI = gr.Number(label="300m NDVI", precision=5, info="The average Normalized Difference Vegetation Index in a 300m Buffer Zone")
    m300_NDBI = gr.Number(label="300m NDBI", precision=5, info="The average Normalized Difference Built-up Index in a 300m Buffer Zone")
    m300_Building_Density = gr.Number(label="300m Building Density", precision=5, info="The average Building Density in a 300m Buffer Zone")
    solar_flux = gr.Number(label="Solar Flux [W/m^2]", precision=5, info="The average Solar Flux at the location")
    inputs = [longitude, latitude, m50_NPCRI, m100_Ground_Elevation, avg_wind_speed, wind_direction, 
              traffic_volume, m150_Ground_Elevation, relative_humidity, m150_NDVI, 
              m150_NDBI, m300_SI, m300_NPCRI, m300_Coastal_Aerosol, m300_Total_Building_Area_m2,
              m300_Building_Construction_Year, m300_Ground_Elevation, m300_Building_Height, m300_Building_Count,
              m300_NDVI, m300_NDBI, m300_Building_Density, solar_flux]
    uhi = gr.number(label="Predicted UHI Index", precision=5)

    # set model explainer outputs
    uhi_label = gr.Label(label="Predicted Status based on UHI Index")
    feature_contributions = gr.JSON(label="Feature Contributions", info="The contributions of each feature to the UHI index prediction")

    # Urban Location
    plot = gr.Plot(label="Urban Location", info="A plot showing the location of the prediction based on the longitude and latitude inputs")

    model_page = gr.Interface(
        predict, 
        inputs=inputs, 
        outputs=[uhi, uhi_label, feature_contributions, plot],
        live=True, 
        examples=load_examples("examples.csv"),
        title="Interact with The ResNet UHI Model",
        description="This model predicts the Urban Heat Island (UHI) index based on various environmental and urban factors. Adjust the inputs to see how they affect the UHI index prediction.",
    )

    iface = gr.TabbedInterface(
        [info_page, model_page],
        ["Information", "UHI Model"]
    )
    
    iface.launch(server_name="0.0.0.0", server_port=7860, allowed_paths=["/"])

if __name__ == "__main__":
    load_interface()