File size: 3,250 Bytes
411678e 31b6e92 411678e ce57a20 411678e ce57a20 3b52176 d98d50d 446f9c9 e232116 16f2ce2 e232116 446f9c9 abadefe d98d50d 3b52176 e741287 3b52176 446f9c9 e694dea 9f7471f 3b52176 a957eeb 741aa8b a957eeb 741aa8b a957eeb e232116 09e96c9 e232116 a957eeb 09e96c9 a957eeb ba86e7b cffcba4 446f9c9 fcd0f51 e232116 c712f91 b26f818 e232116 b26f818 e232116 a957eeb e232116 a957eeb 09c79f1 e232116 a957eeb e232116 a957eeb e232116 a957eeb e232116 a957eeb 741aa8b a957eeb 741aa8b 82bf281 a957eeb 953c510 8eb51fc ee6d004 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
import streamlit as st
from functions import *
st.set_page_config(page_title="Earnings Question/Answering", page_icon="🔎")
st.sidebar.header("Semantic Search")
st.markdown("## Earnings Semantic Search with LangChain, OpenAI & SBert")
def gen_sentiment(text):
'''Generate sentiment of given text'''
return sent_pipe(text)[0]['label']
def gen_annotated_text(df):
'''Generate annotated text'''
tag_list=[]
for row in df.itertuples():
label = row[2]
text = row[1]
if label == 'Positive':
tag_list.append((text,label,'#8fce00'))
elif label == 'Negative':
tag_list.append((text,label,'#f44336'))
else:
tag_list.append((text,label,'#000000'))
return tag_list
bi_enc_dict = {'mpnet-base-v2':"all-mpnet-base-v2",
'instructor-base': 'hkunlp/instructor-base'}
search_input = st.text_input(
label='Enter Your Search Query',value= "What key challenges did the business face?", key='search')
sbert_model_name = st.sidebar.selectbox("Embedding Model", options=list(bi_enc_dict.keys()), key='sbox')
chunk_size = st.sidebar.slider("Number of Chars per Chunk of Text",min_value=500,max_value=2000,value=1000)
overlap_size = st.sidebar.slider("Number of Overlap Chars in Search Response",min_value=50,max_value=300,value=50)
try:
if search_input:
if "sen_df" in st.session_state and "earnings_passages" in st.session_state:
## Save to a dataframe for ease of visualization
sen_df = st.session_state['sen_df']
title = st.session_state['title']
embedding_model = bi_enc_dict[sbert_model_name]
with st.spinner(
text=f"Loading {embedding_model} embedding model and Generating Response..."
):
docsearch = process_corpus(st.session_state['earnings_passages'],title, embedding_model)
result = embed_text(search_input,title,embedding_model,docsearch)
references = [doc.page_content for doc in result['source_documents']]
answer = result['result']
sentiment_label = gen_sentiment(answer)
##### Sematic Search #####
df = pd.DataFrame.from_dict({'Text':[answer],'Sentiment':[sentiment_label]})
text_annotations = gen_annotated_text(df)[0]
with st.expander(label='Query Result', expanded=True):
annotated_text(text_annotations)
with st.expander(label='References from Corpus used to Generate Result'):
for ref in references:
st.write(ref)
else:
st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file')
else:
st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file')
except RuntimeError:
st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file')
|