app.py

import gradio as gr
import polars as pl
from gradio_huggingfacehub_search import HuggingfaceHubSearch
import torch
import spaces
from torch import nn
from transformers import AutoModel, AutoTokenizer, AutoConfig
from huggingface_hub import PyTorchModelHubMixin
import pandas as pd


class QualityModel(nn.Module, PyTorchModelHubMixin):
    def __init__(self, config):
        super(QualityModel, self).__init__()
        self.model = AutoModel.from_pretrained(config["base_model"])
        self.dropout = nn.Dropout(config["fc_dropout"])
        self.fc = nn.Linear(self.model.config.hidden_size, len(config["id2label"]))

    def forward(self, input_ids, attention_mask):
        features = self.model(
            input_ids=input_ids, attention_mask=attention_mask
        ).last_hidden_state
        dropped = self.dropout(features)
        outputs = self.fc(dropped)
        return torch.softmax(outputs[:, 0, :], dim=1)

device = "cuda" if torch.cuda.is_available() else "cpu"
config = AutoConfig.from_pretrained("nvidia/quality-classifier-deberta")
tokenizer = AutoTokenizer.from_pretrained("nvidia/quality-classifier-deberta")
model = QualityModel.from_pretrained("nvidia/quality-classifier-deberta").to(device)
model.eval()


@spaces.GPU
def predict(texts: list[str]):
    inputs = tokenizer(
        texts, return_tensors="pt", padding="longest", truncation=True
    ).to(device)
    outputs = model(inputs["input_ids"], inputs["attention_mask"])
    predicted_classes = torch.argmax(outputs, dim=1)
    predicted_domains = [
        config.id2label[class_idx.item()] for class_idx in predicted_classes.cpu().numpy()
    ]
    return predicted_domains


def run_quality_check(dataset, column, n_samples):
    config = "default"
    data = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{config}/train/0000.parquet", columns=[column])
    texts = data[column].to_list()[:n_samples]
    predictions = predict(texts[:n_samples])
    texts_df = pd.DataFrame({"quality": predictions, "text": texts})
    counts = pd.DataFrame({"quality": predictions}).value_counts().to_frame()
    counts.reset_index(inplace=True)
    return (
        gr.BarPlot(counts, x="quality", y="count"),
        texts_df[texts_df["quality"] == "Low"][:20],
        texts_df[texts_df["quality"] == "Medium"][:20],
        texts_df[texts_df["quality"] == "High"][:20],
    )

with gr.Blocks() as demo:
    gr.Markdown("# 💫 Dataset Quality Checker 💫")
    dataset_name = HuggingfaceHubSearch(
            label="Hub Dataset ID",
            placeholder="Search for dataset id on Huggingface",
            search_type="dataset",
            value="fka/awesome-chatgpt-prompts",
        )
    # config_name = "default"  # TODO: user input
    @gr.render(inputs=dataset_name)
    def embed(name):
        html_code = f"""
        <iframe
          src="https://huggingface.co/datasets/{name}/embed/viewer/default/train"
          frameborder="0"
          width="100%"
          height="700px"
        ></iframe>
            """
        return gr.HTML(value=html_code)
    text_column = gr.Textbox(placeholder="text", label="Text colum name to check (data must be non-nested, raw texts!)")
    n_samples = gr.Number(label="Num first samples to run check")
    gr_check_btn = gr.Button("Check Dataset")
    plot = gr.BarPlot()

    with gr.Accordion("Explore some individual examples for each class", open=False):
        df_low, df_medium, df_high = gr.DataFrame(), gr.DataFrame(), gr.DataFrame()
    gr_check_btn.click(run_quality_check, inputs=[dataset_name, text_column, n_samples], outputs=[plot, df_low, df_medium, df_high])

demo.launch()