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from typing import Union | |
from fastapi import FastAPI | |
from transformers import pipeline | |
from newspaper import Article | |
app = FastAPI() | |
# see dockerfile. it has a line to download the model when the docket container is initialized | |
# summarizer = pipeline("summarization", "/code/models") | |
summarizer = pipeline("summarization", model="Falconsai/text_summarization") | |
def read_root(): | |
return {"Hello": "World"} | |
def read_item(item_id: int, q: Union[str, None] = None): | |
return {"item_id": item_id, "q": q} | |
async def summarize(text: str): | |
# url = 'https://towardsdatascience.com/containerizing-huggingface-transformers-for-gpu-inference-with-docker-and-fastapi-on-aws-d4a83edede2f' | |
article = Article(text) | |
article.download() | |
article.parse() | |
return {"summary": f"{summarizer(article.text, max_length=130, min_length=30, do_sample=False)}"} | |