Update app.py

app.py

@@ -1,12 +1,50 @@
-import streamlit as st
+import gradio as gr
 from datasets import load_dataset
 import pandas as pd
+import matplotlib.pyplot as plt
 
-st.title("Explore Hugging Face Dataset")
+# Load a dataset (you can change this to any HF dataset)
 dataset = load_dataset("ag_news", split="train[:1000]")
 
+# Convert to DataFrame
 df = pd.DataFrame(dataset)
-st.write("Sample data:", df.head())
 
-st.write("Category Distribution")
-st.bar_chart(df["label"].value_counts())
+# Label map for better readability
+label_map = {
+    0: "World",
+    1: "Sports",
+    2: "Business",
+    3: "Sci/Tech"
+}
+df["label_name"] = df["label"].map(label_map)
+
+def preview_data(n_rows):
+    return df.head(n_rows)
+
+def plot_distribution():
+    counts = df["label_name"].value_counts()
+    fig, ax = plt.subplots()
+    counts.plot(kind="bar", ax=ax, color="skyblue")
+    ax.set_title("Label Distribution")
+    ax.set_ylabel("Count")
+    ax.set_xlabel("Category")
+    return fig
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🧠 AG News Dataset Explorer")
+    gr.Markdown("Explore the AG News dataset from Hugging Face. Useful for data engineers and NLP practitioners.")
+
+    with gr.Row():
+        num_slider = gr.Slider(1, 20, value=5, label="Number of Rows")
+        data_output = gr.Dataframe()
+    
+    show_data_btn = gr.Button("Show Data")
+    show_data_btn.click(preview_data, inputs=[num_slider], outputs=[data_output])
+
+    gr.Markdown("## 📊 Class Distribution")
+    dist_btn = gr.Button("Show Distribution Chart")
+    chart_output = gr.Plot()
+    dist_btn.click(plot_distribution, outputs=[chart_output])
+
+# Launch app
+demo.launch()