app.py

import pickle
import pandas as pd
import shap
from shap.plots._force_matplotlib import draw_additive_plot
import gradio as gr
import numpy as np
import matplotlib.pyplot as plt

# load the model from disk
loaded_model = pickle.load(open("XGB_softprob (1).pkl", 'rb'))

# Setup SHAP
explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.

# Create the main function for server
# Create the main function for server

def main_func(CLIMATE_SCENARIO, EAL_SCORE,SOVI_SCORE,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT):
    
    new_row = pd.DataFrame.from_dict({'CLIMATE_SCENARIO': CLIMATE_SCENARIO, 'EAL_SCORE':EAL_SCORE,'SOVI_SCORE':SOVI_SCORE,
              'SOCIAL':SOCIAL,'ECONOMY':ECONOMY,'HOUSING_INFRASTRUCTURE':HOUSING_INFRASTRUCTURE,
              'COMMUNITY_CAPITAL':COMMUNITY_CAPITAL,'INSTITUTIONAL':INSTITUTIONAL,'ENVIRONMENT':ENVIRONMENT}, orient = 'index').transpose()
    
    prob = loaded_model.predict_proba(new_row)
    
    # compute SHAP values
    explainer = shap.TreeExplainer(loaded_model)
    shap_values = explainer.shap_values(new_row,check_additivity=False)
    plot = shap.summary_plot(shap_values[0], new_row.values, feature_names = new_row.columns)



    plt.tight_layout()
    local_plot = plt.gcf()
    plt.close()
    
    return {"Very Low Risk": float(prob[0][0]), "Moderately Low Risk": float(prob[0][1]), "Moderate Risk": float(prob[0][2]), "Moderately High Risk": float(prob[0][3]),  "Very High Risk": float(prob[0][4])}, local_plot

# Create the UI
title = "**Climate Risk Model** 🌍"
description1 = """
The effects of climate change are already being felt by communities across the world. However, each community will face unique hazards in intensity and type because of climate change. Understanding the cost of these hazards is one of the only ways local policymakers can balance competing financial priorities that prevent investments into climate adaptation. To make the most informed decision, local policymakers must determine which hazards they will be impacted by, how to strengthen their resilience to storms, how to properly utilize federal funding, and where to build key partnerships with businesses and NGOs. On the federal and state level, policymakers must understand which communities must be prioritized, how much money communities need, and which agencies to direct funds to.
To achieve this, we have created an interactive risk analysis tool that interface allows users to understand which communities will be most impacted, how much the increased impacts of hazards will affect risk, and what characteristics of community resilience can be improved so that risk can be reduced. In doing so, our tool addresses one of the greatest challenges to climate action: understanding the impacts on a granular, local level.
"""

description2 = """
This interface allows a user to populate data from three pre-selected counties or a county of their choosing. The pre-selected counties are Miami-Dade County in Florida, Washington County in Minnesota, or Falls Church County in Virginia. In order to acquire the data for a county of their choosing, a user can navigate to a team-built tableau dashboard (https://public.tableau.com/app/profile/michael.durst/viz/ClimateChangeHackathon/ExpectedRiskbyCounty?publish=yes). Either select one of three counties at bottom of page or enter custom data in the fields.
Once the interface is pre-populated with data or a user enters data from their county, a user can experiment with the following data elements by clicking analyze and adjusting the variables available on the sliding scale.
As an output, the user can then see the effects this variables have on the risk score and risk rating as well as see a high to low ranked order of community resilience categories.
""" 

with gr.Blocks(title=title) as demo:
    gr.Markdown(f"## {title}")
    # gr.Markdown("""![marketing](types-of-employee-turnover.jpg)""")
    gr.Markdown(description1)
    gr.Markdown("""---""")
    gr.Markdown(description2)
    gr.Markdown("""---""")
    with gr.Row():        
        with gr.Column():                 
            CLIMATE_SCENARIO = gr.Slider(label="Climate Scenario", minimum=0, maximum=2, value=0, step=1)        
            EAL_SCORE = gr.Slider(label="EAL Score", minimum=0, maximum=100, value=20, step=5)
            SOVI_SCORE = gr.Slider(label="SOVI Score", minimum=0, maximum=100, value=20, step=5)
            SOCIAL = gr.Slider(label="Social", minimum=0, maximum=1, value=.5, step=.1)
            ECONOMY = gr.Slider(label="Economy", minimum=0, maximum=1, value=.5, step=.1)
            HOUSING_INFRASTRUCTURE = gr.Slider(label="Housing Infrastructure", minimum=0, maximum=1, value=.5, step=.1)
            COMMUNITY_CAPITAL = gr.Slider(label="Community Capital", minimum=0, maximum=1, value=.5, step=.1)
            INSTITUTIONAL = gr.Slider(label="Institutional", minimum=0, maximum=1, value=.5, step=.1)
            ENVIRONMENT = gr.Slider(label="Environment", minimum=0, maximum=1, value=.5, step=.1)
            submit_btn = gr.Button("Analyze")
        with gr.Column(visible=True) as output_col:
            label = gr.Label(label = "RISK RATING")
            local_plot = gr.Plot(label = 'Shap:')
        
            submit_btn.click(
                main_func,
                [CLIMATE_SCENARIO,EAL_SCORE,SOVI_SCORE,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT],
                [label,local_plot], api_name="Climate Risk Model"
            )
    
    gr.Markdown("### Click on any of the examples below to see how it works:")
    gr.Examples([[0, 46.23, 63.85, .564, .4703, .3068, .2161, .3623, .6264]], 
                [CLIMATE_SCENARIO,EAL_SCORE,SOVI_SCORE,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT], 
                [label,local_plot], main_func, cache_examples=True, label="Miami-Dade County, Florida")
    gr.Examples([[0, 21.05, 15.37, .7231, .5359, .2884, .3828, .4070, .5015]], 
                [CLIMATE_SCENARIO,EAL_SCORE,SOVI_SCORE,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT], 
                [label,local_plot], main_func, cache_examples=True, label="Washington County, Minnesota")
    gr.Examples([[0, 6.929, 4.178, .8181, .5221, .3878, .2463, .389,.3921]], 
                [CLIMATE_SCENARIO,EAL_SCORE,SOVI_SCORE,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT], 
                [label,local_plot], main_func, cache_examples=True, label="Falls Church, Virginia")

demo.launch()