app.py

import pandas as pd
import geopandas as gpd
import gradio as gr
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.cluster.hierarchy import linkage, leaves_list

# ========================
# Data Loading
# ========================
conus_data = pd.read_csv("conus27.csv")
county_geojson = gpd.read_file("county.geojson")
county_embeddings = pd.read_csv("county_embeddings.csv")
county_unemployment = pd.read_csv("county_unemployment.csv")
zcta_poverty = pd.read_csv("zcta_poverty.csv")
zcta_geojson = gpd.read_file("zcta.geojson")

# ========================
# Data Preparation
# ========================
county_unemployment_melted = county_unemployment.melt(
    id_vars=['place'], var_name='date', value_name='unemployment_rate'
)
county_unemployment_melted['place'] = county_unemployment_melted['place'].astype(str)
county_geojson_unemployment = county_geojson.merge(
    county_unemployment_melted, on='place', how='left'
)

zcta_poverty_melted = zcta_poverty.melt(
    id_vars=['place'], var_name='year', value_name='poverty_rate'
)
zcta_poverty_melted['place'] = zcta_poverty_melted['place'].astype(str)
zcta_geojson['place'] = zcta_geojson['place'].astype(str)
zcta_geojson_poverty = zcta_geojson.merge(
    zcta_poverty_melted, on='place', how='left'
)

health_metrics = [c for c in conus_data.columns if c.startswith('Percent_Person_')]
simplified_metrics = [c.replace('Percent_Person_', '') for c in health_metrics]
metric_mapping = dict(zip(simplified_metrics, health_metrics))

if 'place' in conus_data.columns:
    merged_health = county_geojson.merge(conus_data, on='place', how='left')
elif 'GEOID' in county_geojson.columns and 'GEOID' in conus_data.columns:
    merged_health = county_geojson.merge(conus_data, on='GEOID', how='left')
else:
    raise ValueError("No matching key found to merge health data with geodata.")

# ========================
# Plotting Functions
# ========================

def plot_health_metric(metric):
    metric_full = metric_mapping[metric]
    fig, ax = plt.subplots(1, 1, figsize=(12, 8))
    merged_health.plot(
        column=metric_full, cmap='viridis', legend=True, 
        ax=ax, alpha=0.7, edgecolor='black', linewidth=0.5,
        missing_kwds={"color": "lightgrey", "label": "No Data"}
    )
    ax.set_title(f'{metric}: Geographic Distribution', fontsize=15)
    ax.axis('off')
    plt.tight_layout()
    return fig

def plot_health_histogram(metric):
    metric_full = metric_mapping[metric]
    data = conus_data[metric_full].dropna()
    fig, ax = plt.subplots(figsize=(8, 6))
    sns.histplot(data, kde=True, color='teal', ax=ax)
    ax.set_title(f'{metric}: Value Distribution', fontsize=15)
    ax.set_xlabel(f'{metric} (%)')
    ax.set_ylabel('Number of Counties')
    plt.tight_layout()
    return fig

def summarize_health_metrics(metric):
    metric_full = metric_mapping[metric]
    data = conus_data[metric_full].dropna()
    desc = data.describe().to_frame().reset_index()
    desc.columns = ['Statistic', 'Value']
    median_val = data.median()
    q1, q3 = data.quantile([0.25, 0.75])
    iqr = q3 - q1
    extra = pd.DataFrame({'Statistic': ['Median', 'IQR'], 'Value': [median_val, iqr]})
    return pd.concat([desc, extra], ignore_index=True)

def plot_correlation_matrix(metrics):
    selected = [metric_mapping[m] for m in metrics]
    corr = conus_data[selected].corr()
    linkage_matrix = linkage(1 - corr, method='average')
    idx = leaves_list(linkage_matrix)
    corr = corr.iloc[idx, :].iloc[:, idx]
    fig, ax = plt.subplots(figsize=(10, 8))
    sns.heatmap(
        corr, annot=True, cmap='coolwarm', square=True, ax=ax,
        xticklabels=corr.columns, yticklabels=corr.columns, cbar_kws={"shrink": .8}
    )
    ax.set_title('Health Metric Correlations', fontsize=15)
    plt.xticks(rotation=45, ha='right')
    plt.yticks(rotation=0)
    plt.tight_layout()
    return fig

def plot_unemployment_map(date):
    data = county_geojson_unemployment[county_geojson_unemployment['date'] == str(date)]
    fig, ax = plt.subplots(1, 1, figsize=(12, 8))
    data.plot(
        column='unemployment_rate', cmap='YlGnBu', linewidth=0.5,
        ax=ax, edgecolor='0.8', legend=True,
        missing_kwds={"color": "lightgrey", "label": "Missing values"},
    )
    ax.set_title(f'Unemployment Rate by County: {date}', fontsize=15)
    ax.axis('off')
    plt.tight_layout()
    return fig

def plot_poverty_map(year):
    data = zcta_geojson_poverty[zcta_geojson_poverty['year'] == str(year)]
    fig, ax = plt.subplots(1, 1, figsize=(12, 8))
    data.plot(
        column='poverty_rate', cmap='YlOrRd', linewidth=0.5,
        ax=ax, edgecolor='0.8', legend=True,
        missing_kwds={"color": "lightgrey", "label": "Missing values"},
    )
    ax.set_title(f'Poverty Rate by ZCTA: {year}', fontsize=15)
    ax.axis('off')
    plt.tight_layout()
    return fig

# ========================
# Gradio Interface Logic
# ========================

def health_metric_interface(metric):
    return plot_health_metric(metric), summarize_health_metrics(metric), plot_health_histogram(metric)

def correlation_interface(metrics):
    if len(metrics) < 2:
        return "Please select at least two metrics to see a correlation matrix."
    return plot_correlation_matrix(metrics)

# ========================
# Gradio App Setup
# ========================

with gr.Blocks(title="US Population Health Dashboard") as demo:
    gr.Markdown("# US Population Health Dashboard")
    gr.Markdown("""
    A comprehensive visualization platform for analyzing county-level and ZCTA-level health and socioeconomic indicators across the United States.
    """)

    with gr.Tab("Health Metrics"):
        gr.Markdown("### Analyze Individual Health Metrics")
        gr.Markdown("Select a health metric to view its geographic distribution, summary statistics, and value distribution.")
        m = gr.Dropdown(label="Select a Health Metric", choices=simplified_metrics, value=simplified_metrics[0])
        m_plot = gr.Plot()
        m_summary = gr.Dataframe(headers=["Statistic", "Value"])
        m_hist = gr.Plot()
        m.change(health_metric_interface, m, [m_plot, m_summary, m_hist])

    with gr.Tab("Metric Correlations"):
        gr.Markdown("### Explore Relationships Between Metrics")
        gr.Markdown("Select multiple health metrics to view their correlations. Metrics are reordered using hierarchical clustering.")
        cm = gr.CheckboxGroup(choices=simplified_metrics, value=simplified_metrics[:5])
        cm_plot = gr.Plot()
        cm.change(correlation_interface, cm, cm_plot)

    with gr.Tab("Unemployment Trends"):
        gr.Markdown("### Track County Unemployment Over Time")
        gr.Markdown("Select a date to see unemployment rate distribution across counties.")
        d = sorted(county_unemployment_melted['date'].unique())
        ud = gr.Dropdown(choices=d, value=d[0])
        up = gr.Plot()
        ud.change(lambda x: plot_unemployment_map(x), ud, up)

    with gr.Tab("Poverty Trends"):
        gr.Markdown("### Track ZCTA Poverty Over Time")
        gr.Markdown("Select a year to see poverty rate distribution across ZIP Code Tabulation Areas.")
        y = sorted(zcta_poverty_melted['year'].unique())
        py = gr.Dropdown(choices=y, value=y[0])
        pp = gr.Plot()
        py.change(lambda x: plot_poverty_map(x), py, pp)

if __name__ == "__main__":
    demo.launch()