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from fastapi import FastAPI
from pydantic import BaseModel
from model.model import predict_pipeline
from model.model import __version__ as model_version
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TextClassificationPipeline
app = FastAPI()
class TextIn(BaseModel):
text: str
class PredictionOut(BaseModel):
language: str
class TopicClassificationOut(BaseModel):
result: str
@app.get("/")
def home():
return {"health_check": "OK", "model_version": model_version}
@app.post("/predict", response_model=PredictionOut)
def predict(payload: TextIn):
language = predict_pipeline(payload.text)
return {"language": language}
@app.post("/TopicClassification", response_model=TopicClassificationOut)
def TopicClassification(payload: TextIn):
model_name = 'lincoln/flaubert-mlsum-topic-classification'
loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)
loaded_model = AutoModelForSequenceClassification.from_pretrained(model_name)
nlp = TextClassificationPipeline(model=loaded_model, tokenizer=loaded_tokenizer)
result = nlp(payload.text, truncation=True)
return {"result": result}