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import gradio as gr
from datasets import load_dataset
import pandas as pd
import matplotlib.pyplot as plt
# ---------- CONFIGURATION ---------- #
DATASET_NAME = "ag_news"
LABEL_MAPPING = {
0: "World",
1: "Sports",
2: "Business",
3: "Science/Technology"
}
SPLIT = "train[:1000]" # For demo purposes, only use a subset
# ---------- DATA LOADING ---------- #
def load_data():
dataset = load_dataset(DATASET_NAME, split=SPLIT)
df = pd.DataFrame(dataset)
df["label_name"] = df["label"].map(LABEL_MAPPING)
return df
df = load_data()
# ---------- APP FUNCTIONALITY ---------- #
def get_data_preview(n_rows: int = 5) -> pd.DataFrame:
"""Return the top n rows of the dataset."""
return df.head(n_rows)
def get_label_distribution_plot():
"""Return a bar chart of label distribution."""
counts = df["label_name"].value_counts().sort_index()
fig, ax = plt.subplots()
counts.plot(kind="bar", ax=ax, color="#4C72B0")
ax.set_title("Label Distribution")
ax.set_ylabel("Number of Samples")
ax.set_xlabel("Category")
ax.grid(axis="y", linestyle="--", alpha=0.7)
plt.tight_layout()
return fig
# ---------- UI LAYOUT ---------- #
with gr.Blocks(title="AG News Dataset Explorer") as demo:
gr.Markdown("""
# 🧠 AG News Dataset Explorer
Explore a cleaned and labeled version of the AG News dataset using Hugging Face `datasets`.
- View sample records
- Understand label distribution
- Great for data engineers & NLP practitioners
""")
with gr.Tab("📄 Data Preview"):
with gr.Row():
n_slider = gr.Slider(1, 20, value=5, label="Number of rows to preview")
preview_btn = gr.Button("Show Preview")
preview_table = gr.Dataframe(label="Sample Rows")
preview_btn.click(get_data_preview, inputs=n_slider, outputs=preview_table)
with gr.Tab("📊 Label Distribution"):
dist_btn = gr.Button("Generate Distribution Plot")
dist_plot = gr.Plot(label="Label Distribution")
dist_btn.click(get_label_distribution_plot, outputs=dist_plot)
gr.Markdown("""
---
🔗 **Dataset**: [AG News on Hugging Face](https://huggingface.co/datasets/ag_news)
✨
""")
# ---------- LAUNCH ---------- #
demo.launch()
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