app.py

import json
import time
from itertools import count, islice
from multiprocessing.pool import ThreadPool
from queue import Queue, Empty
from typing import Any, Callable, Iterable, Iterator, TypeVar

import gradio as gr
import ijson
import pandas as pd
import requests
from datasets import Features, Value, Sequence
from gradio_huggingfacehub_search import HuggingfaceHubSearch
from huggingface_hub import InferenceClient

from utils import StringIteratorIO


model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
client = InferenceClient(model_id)

session = requests.Session()
empty_dataframe = pd.DataFrame({"1": [], "2": [], "3": []})

NUM_ROWS_PREVIEW = 3
REWRITE_DATASET = (
    "A Machine Learning practitioner is looking for a dataset similar to '{dataset}' but slightly different. "
    "They want you to rewrite the dataset and apply this transformation: {prompt}."
    "The first rows of the dataset are below in JSON format (one JSON object per line):\n\n{rows}\n\n"
    "Rewrite those rows from the '{dataset}' dataset using the same format (one JSON object per line). "
    "Try to keep some of the text or meaning intact, and apply the requested transformation '{prompt}'."
)


with gr.Blocks() as demo:
    gr.Markdown(
        "# 🤗 WIP Dataset ReWriter ✍️✨\n\n"
        "Adjust, translate or transform completely existing datasets.\n\n"
    )
    with gr.Row():
        with gr.Column(scale=3):
            dataset_search =  HuggingfaceHubSearch(
                label="Hub Dataset ID",
                placeholder="Search for dataset id on Huggingface",
                search_type="dataset",
            )
        subset_dropdown = gr.Dropdown(info="Subset", show_label=False, visible=False)
        split_dropdown = gr.Dropdown(info="Split", show_label=False, visible=False)

    gr.Markdown("### Input")
    input_preview = gr.DataFrame(visible=False)
    pretty_input_preview = gr.DataFrame(interactive=False, wrap=True)

    gr.Markdown("### ReWrite")
    input_prompt = gr.Textbox(label="Enter the adjustment or transformation to apply to the dataset:")
    with gr.Accordion("Modify Format", open=False):
        output_format = gr.Textbox(interactive=True, show_label=False, container=False)
    rewrite_button = gr.Button("ReWrite Dataset", variant="primary")
    output_preview = gr.DataFrame(interactive=False, wrap=True)
    save_button = gr.Button("ReWrite Full Dataset", interactive=False)


    ############
    #
    #  Utils
    #
    ###########


    def stream_rows(dataset: str, subset: str, split: str, batch_size: int = 100) -> Iterable[dict[str, Any]]:
        for i in count():
            rows_resp = session.get(f"https://datasets-server.huggingface.co/rows?dataset={dataset}&config={subset}&split={split}&offset={i * batch_size}&length={batch_size}", timeout=10).json()
            if "error" in rows_resp:
                raise RuntimeError(rows_resp["error"])
            if not rows_resp["rows"]:
                break
            for row_item in rows_resp["rows"]:
                yield row_item["row"]


    T = TypeVar("T")


    def batched(it: Iterable[T], n: int) -> Iterator[list[T]]:
        it = iter(it)
        while batch := list(islice(it, n)):
            yield batch


    def stream_reponse(messages: list[dict[str: str]], response_format=None) -> Iterator[str]:
        for _ in range(3):
            message = None
            try:
                for message in client.chat_completion(
                    messages=messages,
                    max_tokens=5000,
                    stream=True,
                    top_p=0.8,
                    seed=42,
                    response_format=response_format
                ):
                    yield message.choices[0].delta.content
            except requests.exceptions.ConnectionError as e:
                if message:
                    raise
                print(e + "\n\nRetrying in 1sec")
                time.sleep(1)
                continue
            break


    def stream_rewrite_dataset_row_by_row(dataset: str, rows: list[dict[str, str]], prompt: str, format: str) -> Iterator[dict[str, str]]:
        prompt = prompt[:1000] if prompt.strip() else ""
        messages = [{"role": "user", "content": REWRITE_DATASET.format(
            dataset=dataset,
            rows=json.dumps({"data": rows}),
            prompt=prompt,
        )}]
        response_format = {"type": "json", "value": {"properties": {"data": {"type": "array", "maxItems": len(rows), "minItems": len(rows), "items": format}}, "required": ["data"]}}
        print("go")
        yield from islice(ijson.items(StringIteratorIO(stream_reponse(messages, response_format=response_format)), "data.item", buf_size=4), len(rows))
        print("done")


    def _write_generator_to_queue(queue: Queue, func: Callable[..., Iterable], kwargs: dict) -> None:
        for i, result in enumerate(func(**kwargs)):
            queue.put(result)
        return None


    def iflatmap_unordered(
        func: Callable[..., Iterable[T]],
        *,
        kwargs_iterable: Iterable[dict],
    ) -> Iterable[T]:
        queue = Queue()
        with ThreadPool() as pool:
            async_results = [pool.apply_async(_write_generator_to_queue, (queue, func, kwargs)) for kwargs in kwargs_iterable]
            try:
                while True:
                    try:
                        yield queue.get(timeout=0.05)
                    except Empty:
                        if all(async_result.ready() for async_result in async_results) and queue.empty():
                            break
            finally:  # in case there's an error to raise
                [async_result.get(timeout=0.05) for async_result in async_results]


    def features_to_format(features: Features) -> dict:
        def feature_to_format(feature):
            if isinstance(feature, Value):
                if "int" in feature.dtype:
                    return {"type": "integer"} 
                elif "float" in feature.dtype:
                    return {"type": "number"}
                else:
                    return {"type": "string"}
            elif isinstance(feature, list):
                return {"type": "array", "items": feature_to_format(feature[0])}
            elif isinstance(feature, dict):
                return {"properties": {k: feature_to_format(v) for k, v in feature.items()}, "required": list(feature)}
            elif isinstance(feature, Sequence):
                if isinstance(feature.feature, dict):
                    return {"properties": {k: {"type": "array", "items": v } for k, v in feature_to_format(feature.feature).items()}, "required": list(feature)}
                else:
                    return {"type": "array", "items": feature_to_format(feature.feature)}
            else:
                return {"type": "string"}
        return feature_to_format(features)


    ############
    #
    #  Events
    #
    ###########

    def _resolve_dataset_selection(dataset: str, default_subset: str, default_split: str) -> dict:
        if "/" not in dataset.strip().strip("/"):
            return None, None, {
                subset_dropdown: gr.Dropdown(visible=False),
                split_dropdown: gr.Dropdown(visible=False),
            }
        info_resp = session.get(f"https://datasets-server.huggingface.co/info?dataset={dataset}", timeout=3).json()
        if "error" in info_resp:
            return None, None, {
                subset_dropdown: gr.Dropdown(visible=False),
                split_dropdown: gr.Dropdown(visible=False),
            }
        subsets: list[str] = list(info_resp["dataset_info"])
        subset = default_subset if default_subset in subsets else subsets[0]
        splits: list[str] = info_resp["dataset_info"][subset]["splits"]
        split = default_split if default_split in splits else splits[0]
        json_format = json.dumps(features_to_format(Features.from_dict(info_resp["dataset_info"][subset]["features"])), indent=2)
        return subset, split, {
            subset_dropdown: gr.Dropdown(value=subset, choices=subsets, visible=len(subsets) > 1),
            split_dropdown: gr.Dropdown(value=split, choices=splits, visible=len(splits) > 1),
            output_format: gr.Textbox(json_format, lines=json_format.count("\n") + 1)
        }


    def _show_input_preview(dataset: str, default_subset: str, default_split: str) -> dict:
        subset, split, output = _resolve_dataset_selection(dataset, default_subset=default_subset, default_split=default_split)
        if subset is None or split is None:
            return output
        rows = list(islice((stream_rows(dataset, subset, split, batch_size=NUM_ROWS_PREVIEW)), NUM_ROWS_PREVIEW))
        return {
            input_preview: pd.DataFrame(rows),
            pretty_input_preview: pd.DataFrame([{k: str(v) for k, v in row.items()} for row in rows]),
            **output
        }


    @dataset_search.change(inputs=[dataset_search], outputs=[input_preview, pretty_input_preview, subset_dropdown, split_dropdown, output_format])
    def show_input_from_dataset_search(dataset: str) -> dict:
        return _show_input_preview(dataset, default_subset="default", default_split="train")

    @subset_dropdown.change(inputs=[dataset_search, subset_dropdown], outputs=[input_preview, pretty_input_preview, subset_dropdown, split_dropdown, output_format])
    def show_input_from_subset_dropdown(dataset: str, subset: str) -> dict:
        return _show_input_preview(dataset, default_subset=subset, default_split="train")

    @split_dropdown.change(inputs=[dataset_search, subset_dropdown, split_dropdown], outputs=[input_preview, pretty_input_preview, subset_dropdown, split_dropdown, output_format])
    def show_input_from_split_dropdown(dataset: str, subset: str, split: str) -> dict:
        return _show_input_preview(dataset, default_subset=subset, default_split=split)


    @rewrite_button.click(inputs=[dataset_search, subset_dropdown, split_dropdown, input_preview, input_prompt, output_format], outputs=[output_preview])
    def rewrite(dataset: str, subset: str, split: str, input_preview_df: pd.DataFrame, prompt: str, json_format: str) -> Iterator[pd.DataFrame]:
        rows = input_preview_df.to_dict(orient="records")
        output_rows = []
        for row in stream_rewrite_dataset_row_by_row(dataset=dataset, rows=rows, prompt=prompt, format=json.loads(json_format)):
            output_rows.append(row)
            yield pd.DataFrame(output_rows)


demo.launch()