code: #11915010	Raghu Punnamraju
  #11915043	Anmol More
  #11915001	Sriganesh Balamurugan
  #11915052	Kapil Bindal
  
  import pandas as pd
  from ast import literal_eval
  
  from cdqa.utils.filters import filter_paragraphs
  from cdqa.utils.download import download_model, download_bnpp_data
  from cdqa.pipeline.cdqa_sklearn import QAPipeline
  
  #read the cleaned dataset and just take question and context for our model
  df = pd.read_csv('data/dataset_collected.csv', usecols=['question', 'context'])
  
  #convert paragraphs to a list
  df['paragraphs'] = df[df.columns[1:]].apply(
      lambda x: x.dropna().values.tolist(),
      axis=1)
  
  df.rename(columns={"question": "title"}, inplace=True)
  df.drop(columns='context', inplace=True)
  df.to_csv('df_corona.csv', index=False)
  
  #use a lighter pipleline model to build pipeline on top of it
  cdqa_pipeline = QAPipeline(reader='models/distilbert_qa.joblib')
  cdqa_pipeline.fit_retriever(df=df)
  
  print('Welcome to Corona Chatbot ! How can I help you ? ')
  print('Press enter twice to quit')
  
  while True:
  	query = input()
  	prediction = cdqa_pipeline.predict(query=query)
  	print('Query : {}\n'.format(query))
  	print('Reply from Bot: {}\n'.format(prediction[0]))
apis: pandas.read_csv