Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Load dataset automatically from GitHub
+CSV_URL = "https://raw.githubusercontent.com/datablist/sample-csv-files/main/files/people/people-100.csv"
+
+def load_data():
+    df = pd.read_csv(CSV_URL)
+    return df.head(10)
+
+def visualize_data(column_x, column_y):
+    df = pd.read_csv(CSV_URL)
+    plt.figure(figsize=(7, 5))
+    sns.scatterplot(x=df[column_x], y=df[column_y])
+    plt.title(f"Scatter Plot: {column_x} vs {column_y}")
+    plt.tight_layout()
+    return plt.gcf()
+
+def get_column_names():
+    df = pd.read_csv(CSV_URL)
+    return list(df.columns)
+
+# Dropdown options
+cols = get_column_names()
+
+# Gradio interface
+with gr.Blocks(title="CSV Visualizer using Gradio") as demo:
+    gr.Markdown("# 📊 CSV Visualizer\nUpload from GitHub and Explore the Data!")
+
+    with gr.Tab("View Data"):
+        view_btn = gr.Button("Load Data")
+        data_output = gr.Dataframe(label="Data Preview")
+        view_btn.click(load_data, outputs=data_output)
+
+    with gr.Tab("Visualize Data"):
+        x_col = gr.Dropdown(choices=cols, label="Select X-axis Column")
+        y_col = gr.Dropdown(choices=cols, label="Select Y-axis Column")
+        plot_btn = gr.Button("Generate Plot")
+        plot_output = gr.Plot(label="Visualization Output")
+
+        plot_btn.click(visualize_data, inputs=[x_col, y_col], outputs=plot_output)
+
+# Launch app
+demo.launch()