Spaces:
Sleeping
Sleeping
| from fastapi import FastAPI | |
| from fastapi.staticfiles import StaticFiles | |
| from fastapi.responses import FileResponse | |
| from transformers import pipeline | |
| from pysbd import Segmenter | |
| app = FastAPI() | |
| segmenter = Segmenter(language='en', clean=False) | |
| privacy_intent_pipe = pipeline("text-classification", | |
| "remzicam/privacy_intent" ) | |
| def doc2sent(text:str)-> dict: | |
| """ | |
| > It takes a string of text and returns a list of sentences | |
| Args: | |
| text (str): the text to be segmented | |
| Returns: | |
| A list of sentences | |
| """ | |
| sentences = segmenter.segment(text) | |
| return [sentence.replace("\n", "").strip() for sentence in sentences] | |
| def pipe(text:str) -> list[str]: | |
| """ | |
| It takes a string of text and returns a dictionary of sentences and their corresponding privacy | |
| intent labels. | |
| Args: | |
| text (str): the text to be classified | |
| Returns: | |
| A dictionary of sentences and their corresponding labels. | |
| """ | |
| sentences = doc2sent(text) | |
| preds = [privacy_intent_pipe(sent)[0]["label"] for sent in sentences] | |
| return dict(zip(sentences, preds)) | |
| def t5(input): | |
| return {"output": pipe(input)} | |
| app.mount("/", StaticFiles(directory="static", html=True), name="static") | |
| def index() -> FileResponse: | |
| return FileResponse(path="/app/static/index.html", media_type="text/html") |