app.py

import argparse
import asyncio
import functools
import json
import os
from io import BytesIO

import uvicorn
from fastapi import FastAPI, Body, Request
# from fastapi.responses import StreamingResponse
# from starlette.staticfiles import StaticFiles
# from starlette.templating import Jinja2Templates
from utils.utils import add_arguments, print_arguments
from sentence_transformers import SentenceTransformer, models



# def print_arguments(args):
#     print("-----------  Configuration Arguments -----------")
#     for arg, value in vars(args).items():
#         print("%s: %s" % (arg, value))
#     print("------------------------------------------------")


# def strtobool(val):
#     val = val.lower()
#     if val in ('y', 'yes', 't', 'true', 'on', '1'):
#         return True
#     elif val in ('n', 'no', 'f', 'false', 'off', '0'):
#         return False
#     else:
#         raise ValueError("invalid truth value %r" % (val,))

# def str_none(val):
#     if val == 'None':
#         return None
#     else:
#         return val

# def add_arguments(argname, type, default, help, argparser, **kwargs):
#     type = strtobool if type == bool else type
#     type = str_none if type == str else type
#     argparser.add_argument(
#         "--" + argname,
#         default=default,
#         type=type,
#         help=help + ' Default: %(default)s.',
#         **kwargs
#     )


os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'


parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)


add_arg("host",        type=str,  default="0.0.0.0", help="")
add_arg("port",        type=int,  default=5000,        help="")
add_arg("model_path",  type=str,  default="BAAI/bge-small-en-v1.5", help="")
add_arg("use_gpu",     type=bool, default=False,   help="")
# add_arg("use_int8",    type=bool, default=True,  help="")
add_arg("beam_size",   type=int,  default=10,     help="")
add_arg("num_workers", type=int,  default=2,      help="")
add_arg("vad_filter",  type=bool, default=True,  help="")
add_arg("local_files_only", type=bool, default=True, help="")


args = parser.parse_args()
print_arguments(args)



if args.use_gpu:
    bge_model = SentenceTransformer(args.model_path, device="cuda", compute_type="float16", cache_folder=".")
else:
    bge_model = SentenceTransformer(args.model_path, device='cpu', cache_folder=".")



if args.use_gpu:
    model_name = 'sam2ai/sbert-tsdae'
    word_embedding_model = models.Transformer(model_name)
    pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), 'cls')
    tsdae_model = SentenceTransformer(
        modules=[word_embedding_model, pooling_model],
        device="cuda",
        compute_type="float16",
        cache_folder="."
    )
else:
    model_name = 'sam2ai/sbert-tsdae'
    word_embedding_model = models.Transformer(model_name)
    pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), 'cls')
    tsdae_model = SentenceTransformer(
        modules=[word_embedding_model, pooling_model],
        device='cpu',
        cache_folder="."
    )


app = FastAPI(title="embedding Inference")


def similarity_score(model, textA, textB):
    em_test = model.encode(
        [textA, textB],
        normalize_embeddings=True
    )
    return em_test[0] @ em_test[1].T


@app.get("/")
async def index(request: Request):
    return {"detail": "API is Active !!"}


@app.post("/bge_embed")
async def api_bge_embed(
            text1: str = Body("text1", description="", embed=True),
            text2: str = Body("text2", description="", embed=True),
        ):

    scores = similarity_score(bge_model, text1, text2)
    print(scores)
    scores = scores.tolist()

    ret = {"similarity score": scores, "status_code": 200}
    return ret

@app.post("/tsdae_embed")
async def api_tsdae_embed(
            text1: str = Body("text1", description="", embed=True),
            text2: str = Body("text2", description="", embed=True),
        ):

    scores = similarity_score(tsdae_model, text1, text2)
    print(scores)
    scores = scores.tolist()

    ret = {"similarity score": scores, "status_code": 200}
    return ret




if __name__ == '__main__':
    uvicorn.run(app, host=args.host, port=args.port)