def execute_pipeline(query):
df = pd.read_csv('data/bnpp_newsroom_v1.1/bnpp_newsroom-v1.1.csv',
converters={'paragraphs': literal_eval})
df = filter_paragraphs(df)
cdqa_pipeline = QAPipeline(
reader='models/bert_qa_vCPU-sklearn.joblib')
cdqa_pipeline.fit(X=df)
cdqa_pipeline.reader.output_dir = None
prediction = cdqa_pipeline.predict(X=query)
result = (prediction[0], prediction[1])
return result
import os
import pandas as pd
from ast import literal_eval
import cdqa
from cdqa.utils.filters import filter_paragraphs
from cdqa.pipeline.cdqa_sklearn import QAPipeline
df = pd.read_csv('/home/ubuntu/data/bnpp_newsroom_v1.1/bnpp_newsroom-v1.1.csv',
converters={'paragraphs': literal_eval})
df = filter_paragraphs(df)
df['title'] = df['category']
cdqa_pipeline = QAPipeline(
reader='/home/ubuntu/data/bert_qa_vCPU-sklearn.joblib')
cdqa_pipeline.fit(X=df)
print('At result')
class QAModule():
def __init__(self):
self.query = 'Since when does the Excellence Program of BNP Paribas exist?'
def getAnswer(self, query):
prediction = cdqa_pipeline.predict(X=query)
return prediction
class SentimentModule():
def __init__(self):