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import os
import random
from statistics import mean
from typing import Iterator, Union, Any
import fasttext
import gradio as gr
from dotenv import load_dotenv
from httpx import Client, Timeout
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import logging
from toolz import concat, groupby, valmap
from fastapi import FastAPI
from httpx import AsyncClient
from pathlib import Path
app = FastAPI()
logger = logging.get_logger(__name__)
load_dotenv()
DEFAULT_FAST_TEXT_MODEL = "laurievb/OpenLID"
def load_model(repo_id: str) -> fasttext.FastText._FastText:
model_path = hf_hub_download(repo_id, filename="model.bin")
return fasttext.load_model(model_path)
def yield_clean_rows(rows: Union[list[str], str], min_length: int = 3) -> Iterator[str]:
for row in rows:
if isinstance(row, str):
# split on lines and remove empty lines
line = row.split("\n")
for line in line:
if line:
yield line
elif isinstance(row, list):
try:
line = " ".join(row)
if len(line) < min_length:
continue
else:
yield line
except TypeError:
continue
FASTTEXT_PREFIX_LENGTH = 9 # fasttext labels are formatted like "__label__eng_Latn"
# model = load_model(DEFAULT_FAST_TEXT_MODEL)
Path("code/models").mkdir(parents=True, exist_ok=True)
model = fasttext.load_model(
hf_hub_download(
"facebook/fasttext-language-identification",
"model.bin",
cache_dir="code/models",
local_dir="code/models",
local_dir_use_symlinks=False,
)
)
def model_predict(inputs: str, k=1) -> list[dict[str, float]]:
predictions = model.predict(inputs, k=k)
return [
{"label": label[FASTTEXT_PREFIX_LENGTH:], "score": prob}
for label, prob in zip(predictions[0], predictions[1])
]
def get_label(x):
return x.get("label")
def get_mean_score(preds):
return mean([pred.get("score") for pred in preds])
def filter_by_frequency(counts_dict: dict, threshold_percent: float = 0.2):
"""Filter a dict to include items whose value is above `threshold_percent`"""
total = sum(counts_dict.values())
threshold = total * threshold_percent
return {k for k, v in counts_dict.items() if v >= threshold}
def predict_rows(rows, target_column, language_threshold_percent=0.2):
rows = (row.get(target_column) for row in rows)
rows = (row for row in rows if row is not None)
rows = list(yield_clean_rows(rows))
predictions = [model_predict(row) for row in rows]
predictions = [pred for pred in predictions if pred is not None]
predictions = list(concat(predictions))
predictions_by_lang = groupby(get_label, predictions)
langues_counts = valmap(len, predictions_by_lang)
keys_to_keep = filter_by_frequency(
langues_counts, threshold_percent=language_threshold_percent
)
filtered_dict = {k: v for k, v in predictions_by_lang.items() if k in keys_to_keep}
return {
"predictions": dict(valmap(get_mean_score, filtered_dict)),
"pred": predictions,
}
@app.get("/items/{hub_id}")
async def predict_language(
hub_id: str,
config: str | None = None,
split: str | None = None,
max_request_calls: int = 10,
number_of_rows: int = 1000,
) -> dict[Any, Any]:
is_valid = datasets_server_valid_rows(hub_id)
if not is_valid:
gr.Error(f"Dataset {hub_id} is not accessible via the datasets server.")
if not config:
config, split = await get_first_config_and_split_name(hub_id)
info = await get_dataset_info(hub_id, config)
if info is None:
gr.Error(f"Dataset {hub_id} is not accessible via the datasets server.")
if dataset_info := info.get("dataset_info"):
total_rows_for_split = dataset_info.get("splits").get(split).get("num_examples")
features = dataset_info.get("features")
column_names = set(features.keys())
logger.info(f"Column names: {column_names}")
if not set(column_names).intersection(TARGET_COLUMN_NAMES):
raise gr.Error(
f"Dataset {hub_id} {column_names} is not in any of the target columns {TARGET_COLUMN_NAMES}"
)
for column in TARGET_COLUMN_NAMES:
if column in column_names:
target_column = column
logger.info(f"Using column {target_column} for language detection")
break
random_rows = await get_random_rows(
hub_id,
total_rows_for_split,
number_of_rows,
max_request_calls,
config,
split,
)
logger.info(f"Predicting language for {len(random_rows)} rows")
predictions = predict_rows(random_rows, target_column)
predictions["hub_id"] = hub_id
predictions["config"] = config
predictions["split"] = split
return predictions
@app.get("/")
app_title = "Language Detection"
inputs = [
gr.Textbox(
None,
label="enter content",
),
gr.Textbox(None, label="split"),
]
interface = gr.Interface(
predict_language,
inputs=inputs,
outputs="json",
title=app_title,
# article=app_description,
)
interface.queue()
interface.launch() |