app.py

import gradio as gr
import pandas as pd
import io
import os
from pathlib import Path
from huggingface_hub import HfApi, Repository
import matplotlib.pyplot as plt

# Global DataFrame
df = pd.DataFrame()

def upload_csv(file):
    global df
    df = pd.read_csv(file.name)
    if "text" not in df.columns or "label" not in df.columns:
        return gr.update(visible=False), "❌ CSV must contain ‘text’ and ‘label’ columns."
    df["label"] = df["label"].fillna("")
    return (
        gr.update(value=df[["text","label"]], visible=True),
        "✅ File uploaded — you can now edit labels."
    )

def save_changes(edited_table):
    global df
    df = pd.DataFrame(edited_table, columns=["text","label"])
    return "💾 Changes saved."

def download_csv():
    global df
    out_path = "annotated_data.csv"
    df.to_csv(out_path, index=False)
    return out_path

def create_distribution_figure(df_input):
    counts = df_input["label"].value_counts().sort_values(ascending=False)
    labels = counts.index.tolist()
    values = counts.values.tolist()
    
    fig, (ax_table, ax_bar) = plt.subplots(
        nrows=1, ncols=2,
        gridspec_kw={"width_ratios": [1, 2]},
        figsize=(8, max(2, len(labels) * 0.3))
    )
    
    # Table
    ax_table.axis("off")
    table_data = [[lab, cnt] for lab, cnt in zip(labels, values)]
    tbl = ax_table.table(cellText=table_data, colLabels=["Label","Count"], loc="center")
    tbl.auto_set_font_size(False)
    tbl.set_fontsize(10)
    tbl.scale(1, 1.5)
    
    # Bar chart
    ax_bar.barh(labels, values)
    ax_bar.invert_yaxis()
    ax_bar.set_xlabel("Count")
    ax_bar.set_ylabel("")
    
    plt.tight_layout()
    return fig

def visualize_and_download_chart():
    global df
    fig = create_distribution_figure(df)
    chart_path = "label_distribution.png"
    fig.savefig(chart_path, dpi=150)
    return fig, chart_path

def push_to_hub(repo_name: str, hf_token: str) -> str:
    global df
    try:
        api = HfApi()
        api.create_repo(repo_id=repo_name, token=hf_token, repo_type="dataset", exist_ok=True)

        local_dir = Path(f"./{repo_name.replace('/', '_')}")
        if local_dir.exists():
            for child in local_dir.iterdir(): child.unlink()
            local_dir.rmdir()

        repo = Repository(
            local_dir=str(local_dir),
            clone_from=repo_name,
            repo_type="dataset",
            use_auth_token=hf_token
        )

        csv_path = local_dir / "data.csv"
        df.to_csv(csv_path, index=False)
        repo.push_to_hub(commit_message="📑 Update annotated data")
        return f"🚀 Pushed to https://huggingface.co/datasets/{repo_name}"
    except Exception as e:
        return f"❌ Push failed: {e}"

with gr.Blocks(theme=gr.themes.Default()) as app:
    gr.Markdown("## 🏷️ Label It! Text Annotation Tool")
    gr.Markdown("Upload a `.csv` with **text** + **label** columns, annotate in-place, then export, visualize, or publish.")

    with gr.Row():
        file_input = gr.File(label="📁 Upload CSV", file_types=[".csv"])
        upload_btn = gr.Button("Upload")

    df_table = gr.Dataframe(headers=["text","label"], label="📝 Editable Table",
                            interactive=True, visible=False)
    status = gr.Textbox(label="Status", interactive=False)

    with gr.Row():
        save_btn        = gr.Button("💾 Save")
        download_btn    = gr.Button("⬇️ Download CSV")
        download_out    = gr.File(label="📥 Downloaded File")

    with gr.Row():
        visualize_btn   = gr.Button("📊 Visualize Distribution")
        chart_plot      = gr.Plot(label="Label Distribution")
        download_chart  = gr.File(label="📥 Download Chart")

    with gr.Accordion("📦 Push to Hugging Face Hub", open=False):
        repo_input  = gr.Textbox(label="Repo (username/dataset-name)")
        token_input = gr.Textbox(label="🔑 HF Token", type="password")
        push_btn    = gr.Button("🚀 Push")
        push_status = gr.Textbox(label="Push Status", interactive=False)

    # Bind events
    upload_btn.click(upload_csv, inputs=file_input, outputs=[df_table, status])
    save_btn.click(save_changes, inputs=df_table, outputs=status)
    download_btn.click(download_csv, outputs=download_out)
    visualize_btn.click(visualize_and_download_chart, outputs=[chart_plot, download_chart])
    push_btn.click(push_to_hub, inputs=[repo_input, token_input], outputs=push_status)

app.launch()