import requests from collections import Counter from requests.adapters import HTTPAdapter, Retry import multiprocessing import os import time import logging import gradio as gr import pandas as pd import polars as pl import matplotlib.pyplot as plt import spaces from gradio_huggingfacehub_search import HuggingfaceHubSearch from huggingface_hub import PyTorchModelHubMixin import torch from torch import nn from transformers import AutoModel, AutoTokenizer, AutoConfig from tqdm import tqdm logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") session = requests.Session() retries = Retry(total=5, backoff_factor=1, status_forcelist=[502, 503, 504]) session.mount('http://', HTTPAdapter(max_retries=retries)) 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 = torch.compile(model) 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 plot_and_df(texts, preds): texts_df = pd.DataFrame({"quality": preds, "text": texts}) counts = Counter(preds) counts_df = pd.DataFrame( { "quality": ["Low", "Medium", "High"], "count": [counts.get("Low", 0), counts.get("Medium", 0), counts.get("High", 0)] } ) # counts.reset_index(inplace=True) return ( gr.BarPlot(counts_df, x="quality", y="count", sort=None), texts_df[texts_df["quality"] == "Low"][["text"]][:20], texts_df[texts_df["quality"] == "Medium"][["text"]][:20], texts_df[texts_df["quality"] == "High"][["text"]][:20], ) @spaces.GPU def run_quality_check(dataset, config, split, column, batch_size, num_examples): # info_resp = session.get(f"https://datasets-server.huggingface.co/info?dataset={dataset}", timeout=3).json() # if "error" in info_resp: # yield "❌ " + info_resp["error"], gr.BarPlot(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), # return # config = "default" if "default" in info_resp["dataset_info"] else next(iter(info_resp["dataset_info"])) # split = "train" if "train" in info_resp["dataset_info"][config]["splits"] else next( # iter(info_resp["dataset_info"][config]["splits"])) logging.info(f"Fetching data for {dataset} {config} {split}") try: data = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{config}/{split}/0000.parquet", columns=[column]) except pl.exceptions.ComputeError: try: data = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{config}/partial-{split}/0000.parquet", columns=[column]) except pl.exceptions.ComputeError: try: data = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{config}/{split}-part0/0000.parquet", columns=[column]) except Exception as error: yield f"❌ {error}", gr.BarPlot(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), return logging.info("Data fetched.") texts = [text[:10000] for text in data[column].to_list()] # texts_sample = data.sample(100, shuffle=True, seed=16).to_pandas() # batch_size = 100 predictions, texts_processed = [], [] num_examples = min(len(texts), num_examples) for i in range(0, num_examples, batch_size): batch_texts = texts[i:i+batch_size] batch_predictions = predict(batch_texts) predictions.extend(batch_predictions) texts_processed.extend(batch_texts) yield {"check in progress...": i / num_examples}, *plot_and_df(texts_processed, predictions), pd.DataFrame() # with multiprocessing.Pool(processes=8) as pool: # props = pool.map(proportion_non_ascii, texts) # # # non_ascii_df = pd.DataFrame.from_dict({"prop_non_ascii": props, "text": texts}) # plt.hist(props, bins=20, range=(0., 1.)) # plt.title('Histogram of proportion of non-ASCII characters') # plt.xlabel('Proportion of non-ASCII characters') # plt.ylabel('Number of texts') yield {"finished": 1.}, *plot_and_df(texts_processed, predictions), data PERSPECTIVE_API_KEY = os.environ.get("PERSPECTIVE_API_KEY") PERSPECTIVE_URL = f"https://commentanalyzer.googleapis.com/v1alpha1/comments:analyze?key={PERSPECTIVE_API_KEY}" REQUESTED_ATTRIBUTES = {"TOXICITY": {}, "SEVERE_TOXICITY": {}, "IDENTITY_ATTACK": {}, "INSULT": {}, "PROFANITY": {}, "THREAT": {}} ATT_SCORE = "attributeScores" SUM_SCORE = "summaryScore" def plot_toxicity(scores): fig, axs = plt.subplots(2, 3)#, figsize=(10, 6)) for x, y, score_name in zip([0,0,0,1,1,1], [0,1,2,0,1,2], scores): axs[x,y].hist(scores[score_name], bins=20, range=(0., 1.)) axs[x,y].set_xlabel(score_name) fig.supylabel("Number of texts") fig.suptitle("Histogram of toxicity scores") fig.tight_layout() return fig def call_perspective_api(texts_df, column_name, full_check=False): headers = { "content-type": "application/json", } req_att_scores = {attr: [] for attr in REQUESTED_ATTRIBUTES} texts = texts_df.sample(100, random_state=16)[column_name].values if not full_check else texts_df[column_name].values n_samples = len(texts) for i, text in tqdm(enumerate(texts), desc="scanning with perspective"): data = { "comment": {"text": text}, "languages": ["en"], "requestedAttributes": REQUESTED_ATTRIBUTES } time.sleep(1) try: req_response = requests.post(PERSPECTIVE_URL, json=data, headers=headers) except Exception as e: print(e) return req_att_scores if req_response.ok: response = req_response.json() # logger.info("Perspective API response is:") # logger.info(response) if ATT_SCORE in response: for req_att in REQUESTED_ATTRIBUTES: if req_att in response[ATT_SCORE]: att_score = response[ATT_SCORE][req_att][SUM_SCORE]["value"] req_att_scores[req_att].append(att_score) else: req_att_scores[req_att].append(0) else: # logger.error( # "Unexpected response format from Perspective API." # ) raise ValueError(req_response) else: try: req_response.raise_for_status() except Exception as e: print(e) return req_att_scores if i % 10 == 0: plot_toxicity(req_att_scores) print(len(texts[:i]), len(req_att_scores["TOXICITY"])) yield {"toxicity check in progress...": i / n_samples}, plt.gcf(), pd.DataFrame.from_dict({column_name: texts[:i+1], **req_att_scores}) plot_toxicity(req_att_scores) yield {"toxicity check finished.": 1.}, plt.gcf(), pd.DataFrame.from_dict({column_name: texts, **req_att_scores}) def proportion_non_ascii(s): """ Compute the proportion of non-ASCII characters in a string. Parameters: s (str): The input string. Returns: float: The proportion of non-ASCII characters in the string. """ non_ascii_count = sum(1 for c in s if ord(c) > 127) total_chars = len(s) return non_ascii_count / total_chars if total_chars > 0 else 0.0 def non_ascii_check(texts_df, column_name): texts = texts_df[column_name].to_list() with multiprocessing.Pool(processes=8) as pool: props = pool.map(proportion_non_ascii, texts) # non_ascii_df = pd.DataFrame.from_dict({"prop_non_ascii": props, "text": texts}) plt.hist(props, bins=20, range=(0., 1.)) plt.title('Histogram of proportion of non-ASCII characters') plt.xlabel('Proportion of non-ASCII characters') plt.ylabel('Number of texts') return plt.gcf() with gr.Blocks() as demo: gr.Markdown( """ # 💫 Dataset Quality Checker 💫 Use [nvidia/quality-classifier-deberta](https://huggingface.co/nvidia/quality-classifier-deberta) on any text dataset on the Hub. ## Select dataset and text column """ ) with gr.Row(): with gr.Column(scale=3): dataset_name = HuggingfaceHubSearch( label="Hub Dataset ID", placeholder="Search for dataset id on Huggingface", search_type="dataset", # value="fka/awesome-chatgpt-prompts", ) subset_dropdown = gr.Dropdown(info="Subset", show_label=False, visible=False) split_dropdown = gr.Dropdown(info="Split", show_label=False, visible=False) # config_name = "default" # TODO: user input with gr.Accordion("Dataset preview", open=False): @gr.render(inputs=[dataset_name, subset_dropdown, split_dropdown]) def embed(name, subset, split): html_code = f""" """ return gr.HTML(value=html_code) def _resolve_dataset_selection(dataset: str, default_subset: str, default_split: str): if "/" not in dataset.strip().strip("/"): return { 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 { 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] return { subset_dropdown: gr.Dropdown(value=subset, choices=subsets, visible=len(subsets) > 1), split_dropdown: gr.Dropdown(value=split, choices=splits, visible=len(splits) > 1), } @dataset_name.change(inputs=[dataset_name], outputs=[subset_dropdown, split_dropdown]) def show_input_from_subset_dropdown(dataset: str) -> dict: return _resolve_dataset_selection(dataset, default_subset="default", default_split="train") @subset_dropdown.change(inputs=[dataset_name, subset_dropdown], outputs=[subset_dropdown, split_dropdown]) def show_input_from_subset_dropdown(dataset: str, subset: str) -> dict: return _resolve_dataset_selection(dataset, default_subset=subset, default_split="train") @split_dropdown.change(inputs=[dataset_name, subset_dropdown, split_dropdown], outputs=[subset_dropdown, split_dropdown]) def show_input_from_split_dropdown(dataset: str, subset: str, split: str) -> dict: return _resolve_dataset_selection(dataset, default_subset=subset, default_split=split) text_column = gr.Textbox(placeholder="text", label="Text colum name to check (data must be non-nested, raw texts!)") gr.Markdown("## Run nvidia quality classifier") batch_size = gr.Slider(0, 64, 32, step=4, label="Inference batch size (set this to smaller value if this space crashes.)") num_examples = gr.Number(500, label="Number of first examples to check") gr_check_btn = gr.Button("Check Dataset") progress_bar = gr.Label(show_label=False) plot = gr.BarPlot() with gr.Accordion("Explore some individual examples for each class", open=False): gr.Markdown("### Low") df_low = gr.DataFrame() gr.Markdown("### Medium") df_medium = gr.DataFrame() gr.Markdown("### High") df_high = gr.DataFrame() texts_df = gr.DataFrame(visible=False) gr_check_btn.click( run_quality_check, inputs=[dataset_name, subset_dropdown, split_dropdown, text_column, batch_size, num_examples], outputs=[progress_bar, plot, df_low, df_medium, df_high, texts_df] ) gr.Markdown("""## Compute text quality measures * proportion of non-ascii characters * #TODO""") gr_ascii_btn = gr.Button("Data measures") non_ascii_hist = gr.Plot() gr_ascii_btn.click(non_ascii_check, inputs=[texts_df, text_column], outputs=[non_ascii_hist]) gr.Markdown("## Explore toxicity") checkbox = gr.Checkbox(value=False, label="Run on full first parquet data (better not)") gr_toxicity_btn = gr.Button("Run perpspective API to check toxicity of random samples.") toxicity_progress_bar = gr.Label(show_label=False) toxicity_hist = gr.Plot() with gr.Accordion("Explore examples with toxicity scores:", open=False): toxicity_df = gr.DataFrame() gr_toxicity_btn.click( call_perspective_api, inputs=[texts_df, text_column, checkbox], outputs=[toxicity_progress_bar, toxicity_hist, toxicity_df] ) demo.launch()