Update app.py

app.py

@@ -14,33 +14,21 @@ 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(EAL_SCORE,SOVI_SCORE,RESL_SCORE,DROUGHT_RISKS,EARTHQUAKE_RISKS,HURICANE_RISKS,TORNADO_RISKS,WILDFIRE_RISKS,WINTERWEATHER_RISKS,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT):
+
+def main_func(CLIMATE_SCENARIO, EAL_SCORE,SOVI_SCORE,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT):
     
-    new_row = pd.DataFrame.from_dict({'EAL_SCORE':EAL_SCORE,'SOVI_SCORE':SOVI_SCORE, 'RESL_SCORE':RESL_SCORE,
-              'DROUGHT_RISKS':DROUGHT_RISKS,'EARTHQUAKE_RISKS':EARTHQUAKE_RISKS,'HURICANE_RISKS':HURICANE_RISKS,
-              'TORNADO_RISKS':TORNADO_RISKS,'WILDFIRE_RISKS':WILDFIRE_RISKS,'WINTERWEATHER_RISKS':WINTERWEATHER_RISKS,
+    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,
-              'COMMUNNITY_CAPITAL':COMMUNNITY_CAPITAL,'INSTITUTIONAL':INSTITUTIONAL,'ENVIRONMENT':ENVIRONMENT}, orient = 'index').transpose()
+              '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, new_row.values, plot_type="bar", class_names= class_names, feature_names = new_row.columns)
-    plot = shap.summary_plot(shap_values[1], new_row.values, feature_names = new_row.columns)
-    
-    
+    plot = shap.summary_plot(shap_values[0], new_row.values, feature_names = new_row.columns)
+
 
-    # plot = shap.waterfall_plot(shap.Explanation(values=shap_values[0][0], 
-    #                                           base_values=explainer.expected_value[0], data=X_test.iloc[0],  
-    #                                      feature_names=X_test.columns.tolist()))
-    
-    # shap_values = explainer(new_row, check_additivity=False)
-    # plot = shap.force_plot(shap_values[0], matplotlib=True, figsize=(30,30), show=False)
-    # plot = shap.plots.waterfall(shap_values[0], max_display=6, show=False)
-    # plot = shap.waterfall_plot(explainer.base_values[0], shap_values[0][0], X[0])
-    # plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
 
     plt.tight_layout()
     local_plot = plt.gcf()
@@ -67,21 +55,15 @@ with gr.Blocks(title=title) as demo:
     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)
-            RESL_SCORE = gr.Slider(label="RESILIENCE Score", minimum=0, maximum=100, value=20, step=5)
-            DROUGHT_RISKS = gr.Slider(label="DROUGHT Score", minimum=0, maximum=100, value=20, step=5)
-            EARTHQUAKE_RISKS = gr.Slider(label="EARTHQUAKE Score", minimum=0, maximum=100, value=20, step=5)
-            HURICANE_RISKS = gr.Slider(label="HURICANE Score", minimum=0, maximum=100, value=20, step=5)
-            TORNADO_RISKS = gr.Slider(label="TORNADO Score", minimum=0, maximum=100, value=20, step=5)
-            WILDFIRE_RISKS = gr.Slider(label="WILDFIRE Score", minimum=0, maximum=100, value=20, step=5)
-            WINTERWEATHER_RISKS = gr.Slider(label="WINTER WEATHER Score", minimum=0, maximum=100, value=20, step=5)            
-            SOCIAL = gr.Slider(label="Social Score", minimum=0, maximum=1, value=.5, step=.1)
-            ECONOMY = gr.Slider(label="Economic Score", minimum=0, maximum=1, value=.5, step=.1)
-            HOUSING_INFRASTRUCTURE = gr.Slider(label="Housing Score", minimum=0, maximum=1, value=.5, step=.1)
-            COMMUNNITY_CAPITAL = gr.Slider(label="Community Capital Score", minimum=0, maximum=1, value=.5, step=.1)
-            INSTITUTIONAL = gr.Slider(label="Institutional Score", minimum=0, maximum=1, value=.5, step=.1)
-            ENVIRONMENT = gr.Slider(label="Environment Score", minimum=0, maximum=1, value=.5, step=.1)
+            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")
@@ -89,26 +71,19 @@ with gr.Blocks(title=title) as demo:
         
             submit_btn.click(
                 main_func,
-                [EAL_SCORE,SOVI_SCORE,RESL_SCORE,DROUGHT_RISKS,EARTHQUAKE_RISKS,HURICANE_RISKS,TORNADO_RISKS,WILDFIRE_RISKS,WINTERWEATHER_RISKS,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT],
+                [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.Markdown("Miami-Dade County, Florida")
-    gr.Examples([[32.03, 68.86, 50.5, 50.5, .1, 30, 19.41, 57.47, 1.024, 45.16, .5441, .4393, .4095, .2630, .3799, .3773]], 
-                [EAL_SCORE,SOVI_SCORE,RESL_SCORE,DROUGHT_RISKS,EARTHQUAKE_RISKS,HURICANE_RISKS,TORNADO_RISKS,WILDFIRE_RISKS,WINTERWEATHER_RISKS,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT], 
-                [label,local_plot], main_func, cache_examples=True)
-    gr.Textbox(label="Miami-Dade County, Florida")
-    gr.Examples([[46.24, 63.85, 53.39, 31.27, 6.771, 58.93, 76.76, 54.54, 0, .564, .4703, .3068, .2161, .3623, .6264]], 
-                [EAL_SCORE,SOVI_SCORE,RESL_SCORE,DROUGHT_RISKS,EARTHQUAKE_RISKS,HURICANE_RISKS,TORNADO_RISKS,WILDFIRE_RISKS,WINTERWEATHER_RISKS,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT], 
-                [label,local_plot], main_func, cache_examples=True)
-    gr.Markdown("Washington County, Minnesota")
-    gr.Examples([[21.06, 15.37, 58.77, 2.32, .5035, 0, 11.98, 4.957, 6.808, .7231, .5359, .2884, .328, .407, .5015]], 
-                [EAL_SCORE,SOVI_SCORE,RESL_SCORE,DROUGHT_RISKS,EARTHQUAKE_RISKS,HURICANE_RISKS,TORNADO_RISKS,WILDFIRE_RISKS,WINTERWEATHER_RISKS,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT], 
-                [label,local_plot], main_func, cache_examples=True)
-    gr.Markdown("Falls Church, Virginia")
-    gr.Examples([[6.947, 4.178, 56.86, 0, .1587, .5027, 1.071, 0, 0, .8181, .5221, .3878, .2463, .389, .3921]], 
-                [EAL_SCORE,SOVI_SCORE,RESL_SCORE,DROUGHT_RISKS,EARTHQUAKE_RISKS,HURICANE_RISKS,TORNADO_RISKS,WILDFIRE_RISKS,WINTERWEATHER_RISKS,SOCIAL,ECONOMY,HOUSING_INFRASTRUCTURE,COMMUNNITY_CAPITAL,INSTITUTIONAL,ENVIRONMENT], 
-                [label,local_plot], main_func, cache_examples=True)
+    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([[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()