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app.py

@@ -2,37 +2,81 @@
 import gradio as gr
 import pandas as pd
 import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
 
 # Load the data
-df = pd.read_csv('data.csv')
+df = pd.read_csv("data.csv")
 
-# Get unique entities for dropdown
-entities = df['Entity'].unique().tolist()
+# Get unique entities and years for dropdowns and sliders
+entities = sorted(df["Entity"].unique().tolist())
+earliest_year = int(df["Year"].min())
+latest_year = int(df["Year"].max())
 
-def plot_data(selected_entities):
+def plot_data(selected_entities, year_range, show_raw_data):
     if not selected_entities:
-        return plt.figure()
+        return plt.figure(), pd.DataFrame()
 
-    fig, ax = plt.subplots(figsize=(10, 6))
+    min_year, max_year = year_range
+
+    filtered_df = df[df["Entity"].isin(selected_entities)].copy()
+    filtered_df = filtered_df[(filtered_df["Year"] >= min_year) & (filtered_df["Year"] <= max_year)]
+
+    # Create the plot
+    fig, ax = plt.subplots(figsize=(12, 7))
     for entity in selected_entities:
-        entity_df = df[df['Entity'] == entity]
-        ax.plot(entity_df['Year'], entity_df['Percent'], label=entity)
+        entity_data = filtered_df[filtered_df["Entity"] == entity]
+        ax.plot(entity_data["Year"], entity_data["Percent"], label=entity, marker=".", markersize=8)
+
+    ax.set_xlabel("年份", fontsize=12)
+    ax.set_ylabel("收入份额百分比", fontsize=12)
+    ax.set_title("Top 5% 收入份额", fontsize=16, fontweight=\"bold\")
+    ax.legend(title="国家/地区", bbox_to_anchor=(1.05, 1), loc=\"upper left\")
+    ax.grid(True, linestyle=":", alpha=0.7)
+    ax.set_facecolor("#f5f5f5") # Light grey background
+
+    # Improve x-axis ticks
+    ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+    plt.xticks(rotation=45, ha=\"right\")
+
+    plt.tight_layout() # Adjust layout to prevent labels from overlapping
 
-    ax.set_xlabel('Year')
-    ax.set_ylabel('Percent')
-    ax.set_title('Percent over Year for Selected Entities')
-    ax.legend()
-    ax.grid(True)
-    return fig
+    # Return raw data if requested
+    if show_raw_data:
+        return fig, filtered_df
+    else:
+        return fig, pd.DataFrame()
 
 # Gradio Interface
-iface = gr.Interface(
-    fn=plot_data,
-    inputs=gr.CheckboxGroup(entities, label='Select Entities'),
-    outputs='plot',
-    title='Entity Data Visualization',
-    description='Select one or more entities to visualize their \'Percent\' over \'Year\'.'
-)
-
-if __name__ == '__main__':
-    iface.launch()
+with gr.Blocks() as demo:
+    gr.Markdown("# Top 5% 收入份额可视化")
+    gr.Markdown("此应用展示了不同国家/地区人口中收入最高的 5% 人群的收入份额。")
+
+    with gr.Row():
+        with gr.Column():
+            entity_selector = gr.CheckboxGroup(
+                choices=entities,
+                label="选择国家/地区",
+                value=["Australia", "China", "Germany", "Japan", "United States"]
+            )
+            year_slider = gr.Slider(
+                minimum=earliest_year,
+                maximum=latest_year,
+                value=[earliest_year, latest_year],
+                step=1,
+                label="选择年份范围"
+            )
+            show_raw_data_checkbox = gr.Checkbox(label="显示原始数据", value=False)
+            submit_button = gr.Button("生成图表")
+
+        with gr.Column():
+            output_plot = gr.Plot(label="收入份额趋势图")
+            output_dataframe = gr.DataFrame(label="原始数据")
+
+    submit_button.click(
+        fn=plot_data,
+        inputs=[entity_selector, year_slider, show_raw_data_checkbox],
+        outputs=[output_plot, output_dataframe]
+    )
+
+if __name__ == "__main__":
+    demo.launch()