|
import streamlit as st |
|
from functions import * |
|
from langchain.chains import QAGenerationChain |
|
import itertools |
|
|
|
|
|
st.set_page_config(page_title="Question/Answering", page_icon="🔎") |
|
|
|
st.sidebar.header("Information_Retrieval_") |
|
|
|
st.markdown( |
|
""" |
|
<style> |
|
|
|
#MainMenu {visibility: hidden; |
|
# } |
|
footer {visibility: hidden; |
|
} |
|
.css-card { |
|
border-radius: 0px; |
|
padding: 30px 10px 10px 10px; |
|
background-color: black; |
|
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); |
|
margin-bottom: 10px; |
|
font-family: "IBM Plex Sans", sans-serif; |
|
} |
|
|
|
.card-tag { |
|
border-radius: 0px; |
|
padding: 1px 5px 1px 5px; |
|
margin-bottom: 10px; |
|
position: absolute; |
|
left: 0px; |
|
top: 0px; |
|
font-size: 0.6rem; |
|
font-family: "IBM Plex Sans", sans-serif; |
|
color: white; |
|
background-color: green; |
|
} |
|
|
|
.css-zt5igj {left:0; |
|
} |
|
|
|
span.css-10trblm {margin-left:0; |
|
} |
|
|
|
div.css-1kyxreq {margin-top: -40px; |
|
} |
|
|
|
|
|
|
|
|
|
|
|
</style> |
|
""", |
|
unsafe_allow_html=True, |
|
) |
|
|
|
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') |
|
|
|
st.sidebar.markdown('Earnings QnA Generator') |
|
|
|
chunk_size = 1000 |
|
overlap_size = 50 |
|
|
|
try: |
|
|
|
if search_input: |
|
|
|
if "sen_df" in st.session_state and "earnings_passages" in st.session_state: |
|
|
|
|
|
sen_df = st.session_state['sen_df'] |
|
|
|
title = st.session_state['title'] |
|
|
|
earnings_text = st.session_state['earnings_passages'] |
|
|
|
print(f'earnings_to_be_embedded:{earnings_text}') |
|
|
|
st.session_state.eval_set = generate_eval( |
|
earnings_text, 10, 3000) |
|
|
|
|
|
for i, qa_pair in enumerate(st.session_state.eval_set): |
|
st.sidebar.markdown( |
|
f""" |
|
<div class="css-card"> |
|
<span class="card-tag">Question {i + 1}</span> |
|
<p style="font-size: 12px;">{qa_pair['question']}</p> |
|
<p style="font-size: 12px;">{qa_pair['answer']}</p> |
|
</div> |
|
""", |
|
unsafe_allow_html=True, |
|
) |
|
|
|
embedding_model = bi_enc_dict[sbert_model_name] |
|
|
|
with st.spinner( |
|
text=f"Loading {embedding_model} embedding model and Generating Response..." |
|
): |
|
|
|
docsearch = process_corpus(earnings_text,title, embedding_model) |
|
|
|
result = embed_text(search_input,docsearch) |
|
|
|
|
|
references = [doc.page_content for doc in result['source_documents']] |
|
|
|
answer = result['answer'] |
|
|
|
sentiment_label = gen_sentiment(answer) |
|
|
|
|
|
|
|
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') |
|
|
|
|
|
|
