Update app.py

app.py

@@ -1,50 +1,54 @@
 import gradio as gr
 import pandas as pd
+import plotly.express as px
 import matplotlib.pyplot as plt
-import seaborn as sns
 
-# Sample data
+# Sample data with coordinates (approximate)
 data = {
     "Country": ["India", "China", "USA", "Indonesia", "Brazil"],
-    "Population": [1400000000, 1410000000, 331000000, 273000000, 213000000]
+    "Population": [1400000000, 1410000000, 331000000, 273000000, 213000000],
+    "Latitude": [20.5937, 35.8617, 37.0902, -0.7893, -14.2350],
+    "Longitude": [78.9629, 104.1954, -95.7129, 113.9213, -51.9253]
 }
 df = pd.DataFrame(data)
-df.set_index("Country", inplace=True)
-
-# Heatmap function
-def generate_heatmap(selected_countries):
-    filtered_df = df.loc[selected_countries]
-    normalized = filtered_df.copy()
-    normalized["Population"] = normalized["Population"] / 1e6  # Convert to millions
-
-    plt.figure(figsize=(6, 3))
-    sns.heatmap(normalized.T, annot=True, fmt=".1f", cmap="YlOrRd", cbar_kws={"label": "Population (in millions)"})
-    plt.title("🌍 Population Heatmap")
-    plt.yticks(rotation=0)
-    plt.tight_layout()
-    return plt
 
-# Pie chart function
+# Geo heatmap function
+def generate_geo_heatmap(selected_countries):
+    filtered_df = df[df["Country"].isin(selected_countries)]
+
+    fig = px.density_mapbox(
+        filtered_df,
+        lat="Latitude",
+        lon="Longitude",
+        z="Population",
+        hover_name="Country",
+        radius=30,
+        center=dict(lat=20, lon=0),
+        zoom=1,
+        mapbox_style="carto-positron"
+    )
+    fig.update_layout(title="🌍 World Population Heatmap (Globe Map)", height=500)
+    return fig
+
+# Pie chart function (same as before)
 def generate_pie_chart(selected_countries):
-    filtered_df = df.loc[selected_countries]
-
+    filtered_df = df[df["Country"].isin(selected_countries)]
     plt.figure(figsize=(5, 5))
-    plt.pie(filtered_df["Population"], labels=filtered_df.index, autopct="%1.1f%%", startangle=90)
+    plt.pie(filtered_df["Population"], labels=filtered_df["Country"], autopct="%1.1f%%", startangle=90)
     plt.title("🧩 Population Distribution")
     plt.tight_layout()
     return plt
 
 # Gradio App
 with gr.Blocks() as demo:
-    gr.Markdown("## 🌍 World Population Visualization")
-    selected = gr.CheckboxGroup(label="Select Countries", choices=df.index.tolist(), value=["India", "China", "USA"])
+    gr.Markdown("## 🌍 World Population Heatmap and Pie Chart")
+    selected = gr.CheckboxGroup(label="Select Countries", choices=df["Country"].tolist(), value=["India", "China", "USA"])
 
     with gr.Row():
-        heatmap_output = gr.Plot(label="Heatmap")
+        heatmap_output = gr.Plot(label="World Heatmap (Map View)")
         piechart_output = gr.Plot(label="Pie Chart")
 
-    # Link input to both outputs
-    selected.change(fn=generate_heatmap, inputs=selected, outputs=heatmap_output)
+    selected.change(fn=generate_geo_heatmap, inputs=selected, outputs=heatmap_output)
     selected.change(fn=generate_pie_chart, inputs=selected, outputs=piechart_output)
 
 demo.launch()