Update app.py

app.py

@@ -4,41 +4,15 @@ import json
 import gradio as gr
 import spacy
 from spacy import displacy
-from transformers import RobertaTokenizer,pipeline
 import torch
 import nltk
-from nltk.tokenize import sent_tokenize
-from fin_readability_sustainability import BERTClass, do_predict
-import pandas as pd
-import en_core_web_sm
 from score_fincat import score_fincat
 from sus_fls import get_sustainability,fls
+from Cuad_others import quad,summarize_text,fin_ner
 nlp = en_core_web_sm.load()
 nltk.download('punkt')
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-
-
-
-##Summarization
-summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY") 
-def summarize_text(text):
-    resp = summarizer(text)
-    stext = resp[0]['summary_text']
-    return stext
-
-    
-##Company Extraction
-ner=pipeline('ner',model='Jean-Baptiste/camembert-ner-with-dates',tokenizer='Jean-Baptiste/camembert-ner-with-dates', aggregation_strategy="simple")
-def fin_ner(text):
-    replaced_spans = ner(text)
-    new_spans=[]
-    for item in replaced_spans:
-        item['entity']=item['entity_group']
-        del item['entity_group']
-        new_spans.append(item)
-    return {"text": text, "entities": new_spans}
-    
      
 #CUAD STARTS    
 def load_questions():
@@ -46,41 +20,14 @@ def load_questions():
     with open('questions.txt') as f:
         questions = f.readlines()
     return questions
-
-
-def load_questions_short():
-    questions_short = []
-    with open('questionshort.txt') as f:
-        questions_short = f.readlines()
-    return questions_short
 questions = load_questions()
-questions_short = load_questions_short()
-
-
 
-def quad(query,file):
-    with open(file.name) as f:
-        paragraph = f.read()
-    questions = load_questions()
-    questions_short = load_questions_short()
-    if (not len(paragraph)==0) and not (len(query)==0):
-        print('getting predictions')
-    predictions = run_prediction([query], paragraph, 'marshmellow77/roberta-base-cuad',n_best_size=5)
-    answer = ""
-    answer_p=""
-    if predictions['0'] == "":
-        answer = 'No answer found in document'
-    else:
-        with open("nbest.json") as jf:
-            data = json.load(jf)
-            for i in range(1):
-                raw_answer=data['0'][i]['text']
-                answer += f"{data['0'][i]['text']} -- \n"
-                answer_p =answer+ f"Probability: {round(data['0'][i]['probability']*100,1)}%\n\n"            
-    return answer_p,summarize_text(answer),fin_ner(answer),score_fincat(answer),get_sustainability(answer),fls(answer)    
+def mainFun(query,file):
+    answer,answer_p=quad(query,file.name)
+    return answer_p,summarize_text(answer),fin_ner(answer),score_fincat(answer),get_sustainability(answer),fls(answer)
                 
                    
-iface = gr.Interface(fn=quad, inputs=[gr.Dropdown(choices=questions_short,label='SEARCH QUERY'),gr.inputs.File(label='TXT FILE')], title="CONBERT",description="CONTRACT REVIEW TOOL",article='Article', outputs=[gr.outputs.Textbox(label='Answer'),gr.outputs.Textbox(label='Summary'),gr.HighlightedText(label='NER'),gr.HighlightedText(label='CLAIM'),gr.HighlightedText(label='SUSTAINABILITY'),gr.HighlightedText(label='FLS')], allow_flagging="never")
+iface = gr.Interface(fn=mainFun, inputs=[gr.Dropdown(choices=questions_short,label='SEARCH QUERY'),gr.inputs.File(label='TXT FILE')], title="CONBERT",description="CONTRACT REVIEW TOOL",article='Article', outputs=[gr.outputs.Textbox(label='Answer'),gr.outputs.Textbox(label='Summary'),gr.HighlightedText(label='NER'),gr.HighlightedText(label='CLAIM'),gr.HighlightedText(label='SUSTAINABILITY'),gr.HighlightedText(label='FLS')], allow_flagging="never")
 
 
 iface.launch()