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Update app.py
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app.py
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@@ -39,30 +39,22 @@ def main_func(CLIMATE_SCENARIO, EAL_SCORE,SOVI_SCORE,SOCIAL,ECONOMY,HOUSING_INFR
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# Create the UI
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title = "**Climate Risk Model** π"
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description1 = """
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"""
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description2 ="""
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"""
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description3 = """
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"""
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description4 = """
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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.
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"""
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with gr.Blocks(title=title) as demo:
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gr.Markdown(f"## {title}")
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# gr.Markdown("""""")
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gr.Markdown(description1)
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# gr.Markdown("""---""")
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gr.Markdown(description2)
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gr.Markdown(description3)
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gr.Markdown(description4)
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# gr.Markdown("""---""")
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with gr.Row():
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with gr.Column():
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CLIMATE_SCENARIO = gr.Slider(label="Climate Scenario", minimum=0, maximum=2, value=0, step=1)
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# Create the UI
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title = "**Climate Risk Model** π"
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description1 = """
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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).
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"""
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description2 ="""
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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.
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"""
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description3 = """
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As an output, you can then see the effects these variables have on the risk rating.
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"""
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with gr.Blocks(title=title) as demo:
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gr.Markdown(f"## {title}")
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gr.Markdown(description1)
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gr.Markdown(description2)
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gr.Markdown(description3)
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with gr.Row():
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with gr.Column():
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CLIMATE_SCENARIO = gr.Slider(label="Climate Scenario", minimum=0, maximum=2, value=0, step=1)
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