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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:
## Save to a dataframe for ease of visualization
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)
# Display the question-answer pairs in the sidebar with smaller text
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)
##### 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')
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