app.py

import streamlit as st
from transformers import pipeline
import re

import requests

API_URL = "https://api-inference.huggingface.co/models/microsoft/prophetnet-large-uncased-squad-qg"
headers = {"Authorization": "Bearer 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"}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()


#-----------------------------------------------------------

API_URL_evidence ="https://api-inference.huggingface.co/models/google/flan-t5-xxl"
headers_evidence = {"Authorization": "Bearer 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"}

def query_evidence(payload):
	response = requests.post(API_URL_evidence, headers=headers_evidence, json=payload)
	return response.json()

#-----------------------------------------------------------
st.title('Welcome to :blue[FACTIFY - 5WQA] ') 
st.header('5W Aspect-based Fact Verification through Question Answering :blue[Web Demo]')
st.subheader('Here are a few steps to begin exploring and interacting with this demo.')
st.caption('First you need to input your claim and press :green[ctrl+enter].')
st.caption('Then you need to input your evidence and press :green[ctrl+enter].')
st.caption('Upon completing these two steps, kindly wait for a minute to receive the results.')
st.caption(':red[At times, you may encounter null/none outputs, which could be a result of a delay in loading the models through the API. If you experience this problem, kindly try again after a few minutes.]')

st.caption('Start by inputting the following instance of a claim and corresponding evidence into the designated text fields.')

st.caption('**:Green[Claim]** :point_down:')
st.caption('''Amazon announced on March 16 it would hire 100,000 new warehouse and delivery workers and 
raise wages $ 2 per hour through April in response to increased demand for its services because of the coronavirus pandemic .''')

st.caption('**:Green[Evidence]** :point_down:')
st.caption('''Due to the consumers increasingly relying on online retailers, 
Amazon planned to hire over 99,000 workers in the warehouse and delivery sector during the Pandemic in the USA.''')


#-----------------------------------------------------------
# claim_text=st.text_area("Enter your claim:",'''
#     Due to the consumers increasingly relying on online retailers, Amazon planned to hire over 99,000 workers in the warehouse and delivery sector during the Pandemic in the USA.
#     ''')

# evidence_text=st.text_area("Enter your evidence:",'''
# Amazon announced on March 16 it would hire 100,000 new warehouse and delivery workers and raise wages $ 2 per hour through April in response to increased demand for its services because of the coronavirus pandemic .
# ''')


# import streamlit as st

claim_text = st.text_input('Enter your claim:')
# st.write('The claim is', claim_text)

evidence_text = st.text_input('Enter your evidence:')

import pandas as pd
import numpy as np
from allennlp.predictors.predictor import Predictor
import allennlp_models.tagging
predictor = Predictor.from_path("structured-prediction-srl-bert.tar.gz")

#---------------------------------------------------------------
def claim(text):
    import re
    df = pd.DataFrame({'claim' : [text]})
    pattern = r'(\d+),(\d+)'
    def remove_number_commas(match):
        return match.group(1) + match.group(2)
    df['claim'] = df['claim'].apply(lambda x: re.sub(pattern, remove_number_commas, x))
    def srl_allennlp(sent):
      try:
        #result = predictor.predict(sentence=sent)['verbs'][0]['description']
        #result = predictor.predict(sentence=sent)['verbs'][0]['tags']
        result = predictor.predict(sentence=sent)
        return(result)
      except IndexError: 
        pass
      #return(predictor.predict(sentence=sent))

    df['allennlp_srl'] = df['claim'].apply(lambda x: srl_allennlp(x))

    df['number_of_verbs'] = ''
    df['verbs_group'] = ''
    df['words'] = ''
    df['verbs'] = ''
    df['modified'] =''

    col1 = df['allennlp_srl']
    for i in range(len(col1)):
      num_verb = len(col1[i]['verbs'])
      df['number_of_verbs'][i] = num_verb
      df['verbs_group'][i] = col1[i]['verbs']
      df['words'][i] = col1[i]['words']

      x=[]
      for verb in range(len(col1[i]['verbs'])):
        x.append(col1[i]['verbs'][verb]['verb'])
      df['verbs'][i] = x

      verb_dict ={}
      desc = []
      for j in range(len(col1[i]['verbs'])):
        string = (col1[i]['verbs'][j]['description'])
        string = string.replace("ARG0", "who")
        string = string.replace("ARG1", "what")
        string = string.replace("ARGM-TMP", "when")
        string = string.replace("ARGM-LOC", "where")
        string = string.replace("ARGM-CAU", "why")
        desc.append(string)
        verb_dict[col1[i]['verbs'][j]['verb']]=string
      df['modified'][i] = verb_dict


    #----------FOR COLUMN "WHO"------------#
    df['who'] = ''
    for j in range(len(df['modified'])):
      val_list = []
      val_string = ''
      for k,v in df['modified'][j].items():
        # print(type(v))
        val_list.append(v)

      who = []
      for indx in range(len(val_list)):
        val_string = val_list[indx]
        pos = val_string.find("who: ")
        substr = ''

        if pos != -1:
          for i in range(pos+5, len(val_string)):
            if val_string[i] == "]":
              break
            else:
              substr = substr + val_string[i]
        else:
          substr = None
        who.append(substr)

      df['who'][j] = who

    #----------FOR COLUMN "WHAT"------------#
    df['what'] = ''
    for j in range(len(df['modified'])):
      val_list = []
      val_string = ''
      for k,v in df['modified'][j].items():
        # print(type(v))
        val_list.append(v)

      what = []
      for indx in range(len(val_list)):
        val_string = val_list[indx]
        pos = val_string.find("what: ")
        substr = ''

        if pos != -1:
          for i in range(pos+6, len(val_string)):
            if val_string[i] == "]":
              break
            else:
              substr = substr + val_string[i]
        else:
          substr = None
        what.append(substr)

      df['what'][j] = what

    #----------FOR COLUMN "WHY"------------#
    df['why'] = ''
    for j in range(len(df['modified'])):
      val_list = []
      val_string = ''
      for k,v in df['modified'][j].items():
        # print(type(v))
        val_list.append(v)

      why = []
      for indx in range(len(val_list)):
        val_string = val_list[indx]
        pos = val_string.find("why: ")
        substr = ''

        if pos != -1:
          for i in range(pos+5, len(val_string)):
            if val_string[i] == "]":
              break
            else:
              substr = substr + val_string[i]
        else:
          substr = None
        why.append(substr)

      df['why'][j] = why

     #----------FOR COLUMN "WHEN"------------#
    df['when'] = ''
    for j in range(len(df['modified'])):
      val_list = []
      val_string = ''
      for k,v in df['modified'][j].items():
        # print(type(v))
        val_list.append(v)

      when = []
      for indx in range(len(val_list)):
        val_string = val_list[indx]
        pos = val_string.find("when: ")
        substr = ''

        if pos != -1:
          for i in range(pos+6, len(val_string)):
            if val_string[i] == "]":
              break
            else:
              substr = substr + val_string[i]
        else:
          substr = None
        when.append(substr)

      df['when'][j] = when


    #----------FOR COLUMN "WHERE"------------#
    df['where'] = ''
    for j in range(len(df['modified'])):
      val_list = []
      val_string = ''
      for k,v in df['modified'][j].items():
        # print(type(v))
        val_list.append(v)

      where = []
      for indx in range(len(val_list)):
        val_string = val_list[indx]
        pos = val_string.find("where: ")
        substr = ''

        if pos != -1:
          for i in range(pos+7, len(val_string)):
            if val_string[i] == "]":
              break
            else:
              substr = substr + val_string[i]
        else:
          substr = None
        where.append(substr)

      df['where'][j] = where

    data=df[["claim","who","what","why","when","where"]].copy()    
    import re
    def remove_trail_comma(text):
        x = re.sub(",\s*$", "", text)
        return x


    data['claim']=data['claim'].apply(lambda x: str(x).replace('\'','').replace('\'',''))
    data['claim']=data['claim'].apply(lambda x: str(x).replace('[','').replace(']','')) 



    data['who']=data['who'].apply(lambda x: str(x).replace(" 's","'s"))
    data['who']=data['who'].apply(lambda x: str(x).replace("s ’","s’"))
    data['who']=data['who'].apply(lambda x: str(x).replace(" - ","-"))
    data['who']=data['who'].apply(lambda x: str(x).replace('\'','').replace('\'',''))
#     data['who']=data['who'].apply(lambda x: str(x).replace('"','').replace('"',''))
    data['who']=data['who'].apply(lambda x: str(x).replace('[','').replace(']','')) 
    data['who']=data['who'].apply(lambda x: str(x).rstrip(','))
    data['who']=data['who'].apply(lambda x: str(x).lstrip(','))
    data['who']=data['who'].apply(lambda x: str(x).replace('None,','').replace('None','')) 
    data['who']=data['who'].apply(remove_trail_comma)



    data['what']=data['what'].apply(lambda x: str(x).replace(" 's","'s"))
    data['what']=data['what'].apply(lambda x: str(x).replace("s ’","s’"))
    data['what']=data['what'].apply(lambda x: str(x).replace(" - ","-"))
    data['what']=data['what'].apply(lambda x: str(x).replace('\'','').replace('\'',''))
#     data['what']=data['what'].apply(lambda x: str(x).replace('"','').replace('"',''))
    data['what']=data['what'].apply(lambda x: str(x).replace('[','').replace(']','')) 
    data['what']=data['what'].apply(lambda x: str(x).rstrip(','))
    data['what']=data['what'].apply(lambda x: str(x).lstrip(','))
    data['what']=data['what'].apply(lambda x: str(x).replace('None,','').replace('None','')) 
    data['what']=data['what'].apply(remove_trail_comma)

    data['why']=data['why'].apply(lambda x: str(x).replace(" 's","'s"))
    data['why']=data['why'].apply(lambda x: str(x).replace("s ’","s’"))
    data['why']=data['why'].apply(lambda x: str(x).replace(" - ","-"))
    data['why']=data['why'].apply(lambda x: str(x).replace('\'','').replace('\'',''))
#     data['why']=data['why'].apply(lambda x: str(x).replace('"','').replace('"',''))
    data['why']=data['why'].apply(lambda x: str(x).replace('[','').replace(']',''))
    data['why']=data['why'].apply(lambda x: str(x).rstrip(','))
    data['why']=data['why'].apply(lambda x: str(x).lstrip(','))
    data['why']=data['why'].apply(lambda x: str(x).replace('None,','').replace('None','')) 
    data['why']=data['why'].apply(remove_trail_comma)

    data['when']=data['when'].apply(lambda x: str(x).replace(" 's","'s"))
    data['when']=data['when'].apply(lambda x: str(x).replace("s ’","s’"))
    data['when']=data['when'].apply(lambda x: str(x).replace(" - ","-"))
    data['when']=data['when'].apply(lambda x: str(x).replace('\'','').replace('\'',''))
#     data['when']=data['when'].apply(lambda x: str(x).replace('"','').replace('"',''))
    data['when']=data['when'].apply(lambda x: str(x).replace('[','').replace(']',''))
    data['when']=data['when'].apply(lambda x: str(x).rstrip(','))
    data['when']=data['when'].apply(lambda x: str(x).lstrip(','))
    data['when']=data['when'].apply(lambda x: str(x).replace('None,','').replace('None','')) 
    data['when']=data['when'].apply(remove_trail_comma)

    data['where']=data['where'].apply(lambda x: str(x).replace(" 's","'s"))
    data['where']=data['where'].apply(lambda x: str(x).replace("s ’","s’"))
    data['where']=data['where'].apply(lambda x: str(x).replace(" - ","-"))
    data['where']=data['where'].apply(lambda x: str(x).replace('\'','').replace('\'',''))
#     data['where']=data['where'].apply(lambda x: str(x).replace('"','').replace('"',''))
    data['where']=data['where'].apply(lambda x: str(x).replace('[','').replace(']','')) 
    data['where']=data['where'].apply(lambda x: str(x).rstrip(','))
    data['where']=data['where'].apply(lambda x: str(x).lstrip(','))
    data['where']=data['where'].apply(lambda x: str(x).replace('None,','').replace('None','')) 
    data['where']=data['where'].apply(remove_trail_comma)
    return data
#-------------------------------------------------------------------------
def split_ws(input_list):
    import re
    output_list = []
    for item in input_list:
        split_item = re.findall(r'[^",]+|"[^"]*"', item)
        output_list += split_item
    result = [x.strip() for x in output_list]
    return result

#--------------------------------------------------------------------------    
def gen_qq(df):
    w_list=["who","when","where","what","why"]
    ans=[]
    cl=[]
    ind=[]
    ques=[]
    evid=[]
    for index,value in enumerate(w_list):
        for i,row in df.iterrows():
            srl=df[value][i]
            claim=df['claim'][i]
            evidence_text=df['evidence'][i]
            answer= split_ws(df[value])
            try:
                if len(srl.split())>0 and len(srl.split(","))>0:
                    for j in range(0,len(answer)):
                        FACT_TO_GENERATE_QUESTION_FROM = f"""{answer[j]} [SEP] {claim}"""
                        question_ids = query({"inputs":FACT_TO_GENERATE_QUESTION_FROM, 
                                  "num_beams":5, 
                                  "early_stopping":True})
                        #print("claim : {}".format(claim))
                        #print("answer : {}".format(answer[j]))
                        #print("question : {}".format(question_ids[0]['generated_text']))
                        ind.append(i)
                        cl.append(claim)
                        ans.append(answer[j])
                        ques.append(question_ids[0]['generated_text'].capitalize())
                        evid.append(evidence_text)
                        #print("-----------------------------------------")
            except:
                pass
    return cl,ques,ans,evid
#------------------------------------------------------------    
def qa_evidence(final_data):
    ans=[]
    cl=[]
    #ind=[]
    ques=[]
    evi=[]
    srl_ans=[]


    for i,row in final_data.iterrows():
        question=final_data['gen_question'][i]
        evidence=final_data['evidence'][i]
        claim=final_data['actual_claim'][i]
        srl_answer=final_data['actual_answer'][i]
        #index=df["index"][i]

        input_evidence = f"question: {question} context: {evidence}"

        answer = query_evidence({
            "inputs":input_evidence,
            "truncation":True})

        #ind.append(index)
        cl.append(claim)
        ans.append(answer[0]["generated_text"])
        ques.append(question)
        evi.append(evidence)
        srl_ans.append(srl_answer)

        #print(f"""index: {index}""")
#         print(f"""evidence: {evidence}""")
#         print(f"""claim: {claim}""")
#         print(f"""Question: {question}""")
#         print(f"""Answer: {answer}""")
#         print(f"""SRL Answer: {srl_answer}""")
#         print("------------------------------------")
#     return list(zip(cl,ques,srl_ans)),list(zip(evi,ques,ans))
#     return cl,ques
    return list(zip(ques,srl_ans)),list(zip(ques,ans))

#------------------------------------------------------------  

if claim_text:
    if evidence_text:
        df=claim(claim_text)
        df["evidence"]=evidence_text
        actual_claim,gen_question,actual_answer,evidence=gen_qq(df)
        final_data=pd.DataFrame([actual_claim,gen_question,actual_answer,evidence]).T
        final_data.columns=["actual_claim","gen_question","actual_answer","evidence"]
        a,b=qa_evidence(final_data)
#         qa_evidence(final_data)
#         st.json(qa_evidence(final_data))
        st.json({'QA pair from claim':[{"Question": qu, "Answer": an} for qu, an in a],
                'QA pair from evidence':[{"Question": qu, "Answer": an} for qu, an in b]})