app file

app.py

@@ -0,0 +1,70 @@
+from fastT5 import get_onnx_model,get_onnx_runtime_sessions,OnnxT5
+from transformers import AutoTokenizer
+from pathlib import Path
+import os
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+app = FastAPI()
+
+
+class QuestionRequest(BaseModel):
+    context: str
+    answer: str
+
+class QuestionResponse(BaseModel):
+    question: str
+
+trained_model_path = './t5_squad_v1/'
+
+pretrained_model_name = Path(trained_model_path).stem
+
+
+encoder_path = os.path.join(trained_model_path,f"{pretrained_model_name}-encoder-quantized.onnx")
+decoder_path = os.path.join(trained_model_path,f"{pretrained_model_name}-decoder-quantized.onnx")
+init_decoder_path = os.path.join(trained_model_path,f"{pretrained_model_name}-init-decoder-quantized.onnx")
+
+model_paths = encoder_path, decoder_path, init_decoder_path
+model_sessions = get_onnx_runtime_sessions(model_paths)
+model = OnnxT5(trained_model_path, model_sessions)
+
+tokenizer = AutoTokenizer.from_pretrained(trained_model_path)
+
+
+def get_question(sentence,answer,mdl,tknizer):
+  text = "context: {} answer: {}".format(sentence,answer)
+  print (text)
+  max_len = 256
+  encoding = tknizer.encode_plus(text,max_length=max_len, pad_to_max_length=False,truncation=True, return_tensors="pt")
+
+  input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]
+
+  outs = mdl.generate(input_ids=input_ids,
+                                  attention_mask=attention_mask,
+                                  early_stopping=True,
+                                  num_beams=5,
+                                  num_return_sequences=1,
+                                  no_repeat_ngram_size=2,
+                                  max_length=128)
+
+
+  dec = [tknizer.decode(ids,skip_special_tokens=True) for ids in outs]
+
+
+  Question = dec[0].replace("question:","")
+  Question= Question.strip()
+  return Question
+
+
+
+@app.get('/')
+def index():
+    return {'message':'hello world'}
+
+@app.post("/getquestion", response_model=QuestionResponse)
+def getquestion(request: QuestionRequest):
+	context = request.context
+	answer = request.answer
+	ques = get_question(context,answer,model,tokenizer)
+	return QuestionResponse(question=ques)
+