Create app.py

app.py

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+from __future__ import annotations
+
+import gradio as gr
+import torch
+import os
+import mojimoji
+import polars as pl
+import re
+import json
+from datetime import datetime, timezone, timedelta
+from transformers import pipeline
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer, DistilBertTokenizerFast
+
+# version: 0.2.1
+
+from gradio import FlaggingCallback, utils
+from collections import OrderedDict
+from gradio.components import Component
+from gradio_client import utils as client_utils
+from typing import Sequence, Any
+from pathlib import Path
+import huggingface_hub
+import uuid
+import filelock
+import csv
+
+class HuggingFaceDatasetSaver(FlaggingCallback):
+    """
+    A callback that saves each flagged sample (both the input and output data) to a HuggingFace dataset.
+
+    Example:
+        import gradio as gr
+        hf_writer = gr.HuggingFaceDatasetSaver(HF_API_TOKEN, "image-classification-mistakes")
+        def image_classifier(inp):
+            return {'cat': 0.3, 'dog': 0.7}
+        demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
+                            allow_flagging="manual", flagging_callback=hf_writer)
+    Guides: using-flagging
+    """
+
+    def __init__(
+        self,
+        hf_token: str,
+        dataset_name: str,
+        private: bool = False,
+        info_filename: str = "dataset_info.json",
+        separate_dirs: bool = False,
+    ):
+        """
+        Parameters:
+            hf_token: The HuggingFace token to use to create (and write the flagged sample to) the HuggingFace dataset (defaults to the registered one).
+            dataset_name: The repo_id of the dataset to save the data to, e.g. "image-classifier-1" or "username/image-classifier-1".
+            private: Whether the dataset should be private (defaults to False).
+            info_filename: The name of the file to save the dataset info (defaults to "dataset_infos.json").
+            separate_dirs: If True, each flagged item will be saved in a separate directory. This makes the flagging more robust to concurrent editing, but may be less convenient to use.
+        """
+        self.hf_token = hf_token
+        self.dataset_id = dataset_name  # TODO: rename parameter (but ensure backward compatibility somehow)
+        self.dataset_private = private
+        self.info_filename = info_filename
+        self.separate_dirs = separate_dirs
+
+    def setup(self, components: Sequence[Component], flagging_dir: str):
+        """
+        Params:
+        flagging_dir (str): local directory where the dataset is cloned,
+        updated, and pushed from.
+        """
+        # Setup dataset on the Hub
+        self.dataset_id = huggingface_hub.create_repo(
+            repo_id=self.dataset_id,
+            token=self.hf_token,
+            private=self.dataset_private,
+            repo_type="dataset",
+            exist_ok=True,
+        ).repo_id
+        path_glob = "**/*.jsonl" if self.separate_dirs else "data.csv"
+        huggingface_hub.metadata_update(
+            repo_id=self.dataset_id,
+            repo_type="dataset",
+            metadata={
+                "configs": [
+                    {
+                        "config_name": "default",
+                        "data_files": [{"split": "train", "path": path_glob}],
+                    }
+                ]
+            },
+            overwrite=True,
+            token=self.hf_token,
+        )
+
+        # Setup flagging dir
+        self.components = components
+        self.dataset_dir = (
+            Path(flagging_dir).absolute() / self.dataset_id.split("/")[-1]
+        )
+        self.dataset_dir.mkdir(parents=True, exist_ok=True)
+        self.infos_file = self.dataset_dir / self.info_filename
+
+        # Download remote files to local
+        remote_files = [self.info_filename]
+        if not self.separate_dirs:
+            # No separate dirs => means all data is in the same CSV file => download it to get its current content
+            remote_files.append("data.csv")
+
+        for filename in remote_files:
+            try:
+                huggingface_hub.hf_hub_download(
+                    repo_id=self.dataset_id,
+                    repo_type="dataset",
+                    filename=filename,
+                    local_dir=self.dataset_dir,
+                    token=self.hf_token,
+                )
+            except huggingface_hub.utils.EntryNotFoundError:
+                pass
+
+    def flag(
+        self,
+        flag_data: list[Any],
+        flag_option: str = "",
+        username: str | None = None,
+    ) -> int:
+        if self.separate_dirs:
+            # JSONL files to support dataset preview on the Hub
+            unique_id = str(uuid.uuid4())
+            components_dir = self.dataset_dir / unique_id
+            data_file = components_dir / "metadata.jsonl"
+            path_in_repo = unique_id  # upload in sub folder (safer for concurrency)
+        else:
+            # Unique CSV file
+            components_dir = self.dataset_dir
+            data_file = components_dir / "data.csv"
+            path_in_repo = None  # upload at root level
+
+        return self._flag_in_dir(
+            data_file=data_file,
+            components_dir=components_dir,
+            path_in_repo=path_in_repo,
+            flag_data=flag_data,
+            flag_option=flag_option,
+            username=username or "",
+        )
+
+    def _flag_in_dir(
+        self,
+        data_file: Path,
+        components_dir: Path,
+        path_in_repo: str | None,
+        flag_data: list[Any],
+        flag_option: str = "",
+        username: str = "",
+    ) -> int:
+        # Deserialize components (write images/audio to files)
+        features, row = self._deserialize_components(
+            components_dir, flag_data, flag_option, username
+        )
+
+        # Write generic info to dataset_infos.json + upload
+        with filelock.FileLock(str(self.infos_file) + ".lock"):
+            if not self.infos_file.exists():
+                self.infos_file.write_text(
+                    json.dumps({"flagged": {"features": features}})
+                )
+
+                huggingface_hub.upload_file(
+                    repo_id=self.dataset_id,
+                    repo_type="dataset",
+                    token=self.hf_token,
+                    path_in_repo=self.infos_file.name,
+                    path_or_fileobj=self.infos_file,
+                )
+
+        headers = list(features.keys())
+
+        if not self.separate_dirs:
+            with filelock.FileLock(components_dir / ".lock"):
+                sample_nb = self._save_as_csv(data_file, headers=headers, row=row)
+                sample_name = str(sample_nb)
+                huggingface_hub.upload_folder(
+                    repo_id=self.dataset_id,
+                    repo_type="dataset",
+                    commit_message=f"Flagged sample #{sample_name}",
+                    path_in_repo=path_in_repo,
+                    ignore_patterns="*.lock",
+                    folder_path=components_dir,
+                    token=self.hf_token,
+                )
+        else:
+            sample_name = self._save_as_jsonl(data_file, headers=headers, row=row)
+            sample_nb = len(
+                [path for path in self.dataset_dir.iterdir() if path.is_dir()]
+            )
+            huggingface_hub.upload_folder(
+                repo_id=self.dataset_id,
+                repo_type="dataset",
+                commit_message=f"Flagged sample #{sample_name}",
+                path_in_repo=path_in_repo,
+                ignore_patterns="*.lock",
+                folder_path=components_dir,
+                token=self.hf_token,
+            )
+
+        return sample_nb
+
+    @staticmethod
+    def _save_as_csv(data_file: Path, headers: list[str], row: list[Any]) -> int:
+        """Save data as CSV and return the sample name (row number)."""
+        is_new = not data_file.exists()
+
+        with data_file.open("a", newline="", encoding="utf-8") as csvfile:
+            writer = csv.writer(csvfile)
+
+            # Write CSV headers if new file
+            if is_new:
+                writer.writerow(utils.sanitize_list_for_csv(headers))
+
+            # Write CSV row for flagged sample
+            writer.writerow(utils.sanitize_list_for_csv(row))
+
+        with data_file.open(encoding="utf-8") as csvfile:
+            return sum(1 for _ in csv.reader(csvfile)) - 1
+
+    @staticmethod
+    def _save_as_jsonl(data_file: Path, headers: list[str], row: list[Any]) -> str:
+        """Save data as JSONL and return the sample name (uuid)."""
+        Path.mkdir(data_file.parent, parents=True, exist_ok=True)
+        with open(data_file, "w", encoding="utf-8") as f:
+            json.dump(dict(zip(headers, row)), f)
+        return data_file.parent.name
+
+    def _deserialize_components(
+        self,
+        data_dir: Path,
+        flag_data: list[Any],
+        flag_option: str = "",
+        username: str = "",
+    ) -> tuple[dict[Any, Any], list[Any]]:
+        """Deserialize components and return the corresponding row for the flagged sample.
+
+        Images/audio are saved to disk as individual files.
+        """
+        # Components that can have a preview on dataset repos
+        file_preview_types = {gr.Audio: "Audio", gr.Image: "Image"}
+
+        # Generate the row corresponding to the flagged sample
+        features = OrderedDict()
+        row = []
+        for component, sample in zip(self.components, flag_data):
+            # Get deserialized object (will save sample to disk if applicable -file, audio, image,...-)
+            label = component.label or ""
+            save_dir = data_dir / client_utils.strip_invalid_filename_characters(label)
+            save_dir.mkdir(exist_ok=True, parents=True)
+            deserialized = utils.simplify_file_data_in_str(
+                component.flag(sample, save_dir)
+            )
+
+            # Add deserialized object to row
+            features[label] = {"dtype": "string", "_type": "Value"}
+            try:
+                deserialized_path = Path(deserialized)
+                if not deserialized_path.exists():
+                    raise FileNotFoundError(f"File {deserialized} not found")
+                row.append(str(deserialized_path.relative_to(self.dataset_dir)))
+            except (FileNotFoundError, TypeError, ValueError, OSError):
+                deserialized = "" if deserialized is None else str(deserialized)
+                row.append(deserialized)
+
+            # If component is eligible for a preview, add the URL of the file
+            # Be mindful that images and audio can be None
+            if isinstance(component, tuple(file_preview_types)):  # type: ignore
+                for _component, _type in file_preview_types.items():
+                    if isinstance(component, _component):
+                        features[label + " file"] = {"_type": _type}
+                        break
+                if deserialized:
+                    path_in_repo = str(  # returned filepath is absolute, we want it relative to compute URL
+                        Path(deserialized).relative_to(self.dataset_dir)
+                    ).replace("\\", "/")
+                    row.append(
+                        huggingface_hub.hf_hub_url(
+                            repo_id=self.dataset_id,
+                            filename=path_in_repo,
+                            repo_type="dataset",
+                        )
+                    )
+                else:
+                    row.append("")
+                    
+        timestamp = datetime.now(timezone(timedelta(hours=9))).isoformat()
+        features["flag"] = {"dtype": "string", "_type": "Value"}
+        features["username"] = {"dtype": "string", "_type": "Value"}
+        features["timestamp"] = {"dtype": "string", "_type": "Value"}
+        row.append(flag_option)
+        row.append(username)
+        row.append(timestamp)
+        return features, row
+
+# Get environment variable
+hf_token = os.getenv('HF_TOKEN')
+
+if torch.cuda.is_available():
+    print("GPU is enabled.")
+    print("device count: {}, current device: {}".format(torch.cuda.device_count(), torch.cuda.current_device()))
+else:
+    print("GPU is not enabled.")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Prepare logger for flagging
+hf_writer = HuggingFaceDatasetSaver(hf_token, "crowdsourced-sentiment_analysis")
+
+# Prepare model
+tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base", token=hf_token)
+model = AutoModelForSequenceClassification.from_pretrained("arcleife/roberta-sentiment-id", num_labels=3, token=hf_token).to(device)
+
+pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=device, return_token_type_ids=False)
+
+def get_label(result):
+    if result[0]['label'] == "LABEL_0":
+        return "POSITIVE"
+    elif result[0]['label'] == "LABEL_1":
+        return "NEUTRAL"
+    else:
+        return "NEGATIVE"
+    
+def text_classification(text):
+    result = pipe(text)
+    sentiment_label = get_label(result)
+    sentiment_score = result[0]['score']
+    return sentiment_label, sentiment_score
+
+examples=["Makanannya ga enak ini", "Nyaman ya tempatnya"]
+
+io = gr.Interface(fn=text_classification, 
+                  inputs=gr.Textbox(lines=2, label="Text", placeholder="Enter text here..."), 
+                  outputs=["text", "number"],
+                  title="Text Classification",
+                  description="Enter a text and see the text classification result!",
+                  examples=examples,
+                  # flagging_mode="manual",
+                  # flagging_options=["TOXIC", "NONTOXIC"],
+                  # flagging_callback=hf_writer
+                 )
+
+io.launch(inline=False)
+
+# with gr.Blocks() as main_interface:
+#     gr.LoginButton()
+    
+#     gr.Markdown("# 人格否定検知")
+#     gr.Markdown("**Input**にテキストを入力し、**実行**をクリックしてください。")
+#     with gr.Row():
+#         with gr.Column():
+#             inp = gr.Textbox(placeholder="テキストを入力してください。", label="Input", lines=4)
+#         with gr.Column():
+#             out = gr.Label(label="Result")
+#             flag = gr.Button("Flag")
+#     btn = gr.Button("実行")
+#     btn.click(fn=text_classification, inputs=inp, outputs=out)
+
+# main_interface.launch()