initial commit

app.py

@@ -1,32 +1,7 @@
-import collections
-import math
-import re
-import string
 import streamlit as st
-from transformers.pipelines import pipeline
-import json
-import sys
 from predict import run_prediction
-import random
 from io import StringIO
-import requests
-import boto3 
-
-from transformers import (
-    AutoConfig,
-    AutoModelForQuestionAnswering,
-    AutoTokenizer,
-    squad_convert_examples_to_features
-)
-
-from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample
-from transformers.data.metrics.squad_metrics import compute_predictions_logits
-
-import gradio as gr
 import json
-import torch
-import time
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 
 st.set_page_config(layout="wide")
 st.cache(show_spinner=False, persist=True)
@@ -82,7 +57,7 @@ contract = st.sidebar.selectbox('Please Select a Contract', contracts_drop)
 
 
 idx = contracts_drop.index(contract)
-with open('data/'+contracts_files[idx-1]) as f:
+with open('data/'+contracts_files[idx]) as f:
     contract_data = f.read()
 
 # upload contract
@@ -91,7 +66,7 @@ print(user_upload)
 
 # process upload
 if user_upload is not None:
-    print(user_upload.name,user_upload.type)
+    print(user_upload.name, user_upload.type)
     extension = user_upload.name.split('.')[-1].lower()
     if extension == 'txt':
         print('text file uploaded')
@@ -132,7 +107,7 @@ st.header("Legal Contract Review Demo")
 st.write("This demo uses the CUAD dataset for Contract Understanding.")
 
 
-paragraph = st.text_area(label="Contract",value=contract_data,height=400)
+paragraph = st.text_area(label="Contract", value=contract_data, height=400)
 question = st.selectbox('Choose one of the 41 queries from the CUAD dataset:', questions)
 
 if st.button('Analyze'):