from nlp import NLP
from fastapi import FastAPI
from pydantic import BaseModel
class Message(BaseModel):
text: str
app = FastAPI()
nlp = NLP()
@app.post("/sentiment/")
async def sentiment_analysis(message: Message):
sentiment = nlp.predict_sentiment(message.text)
return sentiment
# use uvicorn main:app --reload to run the server
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 28 02:49:12 2020
@author: erick
"""
from nlp import NLP
import time
object_nlp = NLP()
begin = time.time()
string = u'Ich bin am 26.12.1993 geboren'
object_nlp.setDoc(string)
object_nlp.docDetails()
#dsobject_nlp.docDetails()
errors = object_nlp.checkSatz(2)
finish = time.time()
#print("%.2fs"% (finish-begin))
#print(f'Frase: {object_nlp.doc}\n')
for error in errors:
print(f'Erro: {error["match"]}')
print(f'Correção: {error["tip"]}\n')
def __init__(self, gazetteer, datasetFile, annotatedEntities,
vocabularyFile):
self.nlp = NLP()
self.ner = NER(gazetteer, annotatedEntities)
self.topicModel = TopicClassification(datasetFile, vocabularyFile)
