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| 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_new_v5.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,SOVI_SCORE,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT): | |
| new_row = pd.DataFrame.from_dict({'CLIMATE_SCENARIO': CLIMATE_SCENARIO,'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 pre-selected counties are Miami-Dade County in Florida, Washington County in Minnesota, or Falls Church County in Virginia. To acquire the data for a county of their choosing, you can navigate to a team-built tableau dashboard (https://public.tableau.com/app/profile/michael.durst/viz/ClimateChangeHackathon/ExpectedRiskbyCounty?publish=yes). | |
| """ | |
| description2 =""" | |
| 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 you enter data from a county of your choosing, you can experiment with the data elements by clicking analyze and adjusting the variables available on the sliding scale to see how the outputs are affected. | |
| """ | |
| description3 = """ | |
| As an output, you can then see the effects these variables have on the risk rating. | |
| """ | |
| with gr.Blocks(title=title) as demo: | |
| gr.Markdown(f"## {title}") | |
| gr.Markdown(description1) | |
| gr.Markdown(description2) | |
| gr.Markdown(description3) | |
| 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,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, 63.85, .564, .4703, .3068, .2161, .3623, .6264]], | |
| [CLIMATE_SCENARIO,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, 15.37, .7231, .5359, .2884, .3828, .4070, .5015]], | |
| [CLIMATE_SCENARIO,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, 4.178, .8181, .5221, .3878, .2463, .389,.3921]], | |
| [CLIMATE_SCENARIO,SOVI_SCORE,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT], | |
| [label,local_plot], main_func, cache_examples=True, label="Falls Church, Virginia") | |
| demo.launch() |