Spaces:
Sleeping
Sleeping
File size: 2,160 Bytes
d58981a a0ead68 1872809 c379e84 a74ff63 c379e84 eebad8b c379e84 19d685b a74ff63 cf7f7d8 c379e84 cf7f7d8 27d29ad 06510d6 c379e84 27d29ad cf7f7d8 c379e84 eebad8b c379e84 eebad8b c379e84 eebad8b c379e84 eebad8b c379e84 eebad8b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
import os
os.system("apt-get install tesseract-ocr")
from fastapi import FastAPI, File, Request, UploadFile, Body, Depends, HTTPException
from fastapi.security.api_key import APIKeyHeader
from typing import Optional, Annotated
from fastapi.encoders import jsonable_encoder
from PIL import Image
from io import BytesIO
import pytesseract
from nltk.tokenize import sent_tokenize
from transformers import MarianMTModel, MarianTokenizer
API_KEY = os.environ.get("API_KEY")
app = FastAPI()
api_key_header = APIKeyHeader(name="api_key", auto_error=False)
def get_api_key(api_key: Optional[str] = Depends(api_key_header)):
if api_key is None or api_key != API_KEY:
raise HTTPException(status_code=401, detail="Unauthorized access")
return api_key
@app.post("/api/ocr", response_model=dict)
async def ocr(
api_key: str = Depends(get_api_key),
image: UploadFile = File(...),
# languages: list = Body(["eng"])
):
try:
content = await image.read()
image = Image.open(BytesIO(content))
# text = pytesseract.image_to_string(image, lang="+".join(languages))
text = pytesseract.image_to_string(image, lang = 'eng')
except Exception as e:
return {"error": str(e)}, 500
# return jsonable_encoder({"text": text})
return {"ImageText": text}
@app.post("/api/translate", response_model=dict)
async def translate(
api_key: str = Depends(get_api_key),
text: str = Body(...),
src: str = "en",
trg: str = "zh",
):
if api_key != API_KEY:
return {"error": "Invalid API key"}, 401
tokenizer, model = get_model(src, trg)
translated_text = ""
for sentence in sent_tokenize(text):
translated_sub = model.generate(**tokenizer(sentence, return_tensors="pt"))[0]
translated_text += tokenizer.decode(translated_sub, skip_special_tokens=True) + "\n"
return jsonable_encoder({"translated_text": translated_text})
def get_model(src: str, trg: str):
model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
return tokenizer, model
|