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# Copyright 2022, Lefebvre Dalloz Services
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from fastapi import FastAPI
from onnxruntime import GraphOptimizationLevel, InferenceSession, SessionOptions
from transformers import AutoTokenizer, BatchEncoding, TensorType
app = FastAPI()
options = SessionOptions()
options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
model = InferenceSession("triton_models/model.onnx", options, providers=["CUDAExecutionProvider"])
tokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLM-L6-H384-uncased-sst2")
@app.get("/predict")
def predict(query: str):
encode_dict: BatchEncoding = tokenizer(
text=query,
max_length=128,
truncation=True,
return_tensors=TensorType.NUMPY,
)
result: np.ndarray = model.run(None, dict(encode_dict))[0]
return result.tolist()
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