app.py

import gradio as gr
from gradio_huggingfacehub_search import HuggingfaceHubSearch
import nbformat as nbf
from huggingface_hub import HfApi
from httpx import Client
import logging
import pandas as pd
from utils.notebook_utils import (
    eda_cells,
    replace_wildcards,
    rag_cells,
    embeggins_cells,
)
from dotenv import load_dotenv
import os

# TODOS:
# 2. Add template for RAG and embeddings
# 3. Improve templates

load_dotenv()

HF_TOKEN = os.getenv("HF_TOKEN")
NOTEBOOKS_REPOSITORY = os.getenv("NOTEBOOKS_REPOSITORY")
assert HF_TOKEN is not None, "You need to set HF_TOKEN in your environment variables"
assert (
    NOTEBOOKS_REPOSITORY is not None
), "You need to set NOTEBOOKS_REPOSITORY in your environment variables"


BASE_DATASETS_SERVER_URL = "https://datasets-server.huggingface.co"
HEADERS = {"Accept": "application/json", "Content-Type": "application/json"}

client = Client(headers=HEADERS)

logging.basicConfig(level=logging.INFO)


def get_compatible_libraries(dataset: str):
    try:
        response = client.get(
            f"{BASE_DATASETS_SERVER_URL}/compatible-libraries?dataset={dataset}"
        )
        response.raise_for_status()
        return response.json()
    except Exception as e:
        logging.error(f"Error fetching compatible libraries: {e}")
        raise


def create_notebook_file(cells, notebook_name):
    nb = nbf.v4.new_notebook()
    nb["cells"] = [
        nbf.v4.new_code_cell(
            cmd["source"]
            if isinstance(cmd["source"], str)
            else "\n".join(cmd["source"])
        )
        if cmd["cell_type"] == "code"
        else nbf.v4.new_markdown_cell(cmd["source"])
        for cmd in cells
    ]

    with open(notebook_name, "w") as f:
        nbf.write(nb, f)
    logging.info(f"Notebook {notebook_name} created successfully")


def get_first_rows_as_df(dataset: str, config: str, split: str, limit: int):
    try:
        resp = client.get(
            f"{BASE_DATASETS_SERVER_URL}/first-rows?dataset={dataset}&config={config}&split={split}"
        )
        resp.raise_for_status()
        content = resp.json()
        rows = content["rows"]
        rows = [row["row"] for row in rows]
        first_rows_df = pd.DataFrame.from_dict(rows).sample(frac=1).head(limit)
        features = content["features"]
        features_dict = {feature["name"]: feature["type"] for feature in features}
        return features_dict, first_rows_df
    except Exception as e:
        logging.error(f"Error fetching first rows: {e}")
        raise


def generate_eda_cells(dataset_id):
    yield from generate_cells(dataset_id, eda_cells, "eda")


def generate_rag_cells(dataset_id):
    yield from generate_cells(dataset_id, rag_cells, "rag")


def generate_embedding_cells(dataset_id):
    yield from generate_cells(dataset_id, embeggins_cells, "embeddings")


def _push_to_hub(
    dataset_id,
    notebook_file,
):
    logging.info(f"Pushing notebook to hub: {dataset_id} on file {notebook_file}")

    notebook_name = notebook_file.split("/")[-1]
    api = HfApi(token=HF_TOKEN)
    try:
        logging.info(f"About to push {notebook_file} - {dataset_id}")
        api.upload_file(
            path_or_fileobj=notebook_file,
            path_in_repo=notebook_name,
            repo_id=NOTEBOOKS_REPOSITORY,
            repo_type="dataset",
        )
        link = f"https://huggingface.co/datasets/{NOTEBOOKS_REPOSITORY}/blob/main/{notebook_name}"
        logging.info(f"Notebook pushed to hub: {link}")
        return link
    except Exception as e:
        logging.info("Failed to push notebook", e)
        raise


def generate_cells(dataset_id, cells, notebook_type="eda"):
    try:
        libraries = get_compatible_libraries(dataset_id)
    except Exception as err:
        gr.Error("Unable to retrieve dataset info from HF Hub.")
        logging.error(f"Failed to fetch compatible libraries: {err}")
        return []

    if not libraries:
        gr.Error("Dataset not compatible with pandas library.")
        logging.error(f"Dataset not compatible with pandas library")
        return gr.File(visible=False), gr.Row.update(visible=False)

    pandas_library = next(
        (lib for lib in libraries.get("libraries", []) if lib["library"] == "pandas"),
        None,
    )
    if not pandas_library:
        gr.Error("Dataset not compatible with pandas library.")
        return []

    first_config_loading_code = pandas_library["loading_codes"][0]
    first_code = first_config_loading_code["code"]
    first_config = first_config_loading_code["config_name"]
    first_split = list(first_config_loading_code["arguments"]["splits"].keys())[0]
    features, df = get_first_rows_as_df(dataset_id, first_config, first_split, 3)

    html_code = f"<iframe src='https://huggingface.co/datasets/{dataset_id}/embed/viewer' width='80%' height='560px'></iframe>"
    wildcards = ["{dataset_name}", "{first_code}", "{html_code}"]
    replacements = [dataset_id, first_code, html_code]
    cells = replace_wildcards(cells, wildcards, replacements)
    generated_text = ""
    # Show only the first 40 lines, would like to have a scroll in gr.Code https://github.com/gradio-app/gradio/issues/9192
    viewer_lines = 0
    for cell in cells:
        generated_text += cell["source"] + "\n"
        yield generated_text, ""
        if generated_text.count("\n") > 38:
            generated_text += (
                f"## See more lines available in the generated notebook :) ......"
            )
            yield generated_text, ""
            break
    notebook_name = f"{dataset_id.replace('/', '-')}-{notebook_type}.ipynb"
    create_notebook_file(cells, notebook_name=notebook_name)
    notebook_link = _push_to_hub(dataset_id, notebook_name)
    yield generated_text, f"## Here you have the [generated notebook]({notebook_link})"


with gr.Blocks(fill_height=True, fill_width=True) as demo:
    gr.Markdown("# 🤖 Dataset notebook creator 🕵️")
    with gr.Row(equal_height=True):
        with gr.Column(scale=2):
            text_input = gr.Textbox(label="Suggested notebook type", visible=False)

            dataset_name = HuggingfaceHubSearch(
                label="Hub Dataset ID",
                placeholder="Search for dataset id on Huggingface",
                search_type="dataset",
                value="",
            )

            dataset_samples = gr.Examples(
                examples=[
                    [
                        "infinite-dataset-hub/WorldPopCounts",
                        "Try this dataset for Exploratory Data Analysis",
                    ],
                    [
                        "infinite-dataset-hub/GlobaleCuisineRecipes",
                        "Try this dataset for Embeddings generation",
                    ],
                    [
                        "infinite-dataset-hub/GlobalBestSellersSummaries",
                        "Try this dataset for RAG generation",
                    ],
                ],
                inputs=[dataset_name, text_input],
                cache_examples=False,
            )

            @gr.render(inputs=dataset_name)
            def embed(name):
                if not name:
                    return gr.Markdown("### No dataset provided")
                html_code = f"""
                <iframe
                src="https://huggingface.co/datasets/{name}/embed/viewer/default/train"
                frameborder="0"
                width="100%"
                height="350px"
                ></iframe>
                """
                return gr.HTML(value=html_code, elem_classes="viewer")

            with gr.Row():
                generate_eda_btn = gr.Button("Exploratory Data Analysis")
                generate_embedding_btn = gr.Button("Embeddings")
                generate_rag_btn = gr.Button("RAG")
                generate_training_btn = gr.Button(
                    "Training - Coming soon", interactive=False
                )

        with gr.Column(scale=2):
            code_component = gr.Code(
                language="python", label="Notebook Code Preview", lines=40
            )
            go_to_notebook = gr.Markdown("", visible=True)

    generate_eda_btn.click(
        generate_eda_cells,
        inputs=[dataset_name],
        outputs=[code_component, go_to_notebook],
    )

    generate_embedding_btn.click(
        generate_embedding_cells,
        inputs=[dataset_name],
        outputs=[code_component, go_to_notebook],
    )

    generate_rag_btn.click(
        generate_rag_cells,
        inputs=[dataset_name],
        outputs=[code_component, go_to_notebook],
    )

demo.launch()