import streamlit as st
from functions import *

st.set_page_config(page_title="Earnings Semantic Search", page_icon="🔎")
st.sidebar.header("Semantic Search")
st.markdown("## Earnings Semantic Search with SBert")

search_input = st.text_input(
        label='Enter Your Search Query, e.g "What challenges did the business face?"', key='search')
        
top_k = st.sidebar.slider("Number of Top Hits Generated",min_value=1,max_value=5,value=2)

window_size = st.sidebar.slider("Number of Sentences Generated in Search Response",min_value=1,max_value=5,value=3)

## Save to a dataframe for ease of visualization
sen_df = st.session_state['sen_def']
    
passages = preprocess_plain_text(st.session_state['earnings_passages'],window_size=window_size)

##### Sematic Search #####
# Encode the query using the bi-encoder and find potentially relevant passages
corpus_embeddings = sbert.encode(passages, convert_to_tensor=True, show_progress_bar=True)
question_embedding = sbert.encode(search_input, convert_to_tensor=True)
question_embedding = question_embedding.cpu()
hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k,score_function=util.dot_score)
hits = hits[0]  # Get the hits for the first query

##### Re-Ranking #####
# Now, score all retrieved passages with the cross_encoder
cross_inp = [[query, passages[hit['corpus_id']]] for hit in hits]
cross_scores = cross_encoder.predict(cross_inp)

# Sort results by the cross-encoder scores
for idx in range(len(cross_scores)):
    hits[idx]['cross-score'] = cross_scores[idx]

# Output of top-3 hits from re-ranker
hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)

score='cross-score'
df = pd.DataFrame([(hit[score],passages[hit['corpus_id']]) for hit in hits[0:2]],columns=['Score','Text'])
df['Score'] = round(df['Score'],2)

tag_list = []

def gen_annotated_text(para):
    for i in sent_tokenize(para):
        label = sen_df.loc[sen_df['text']==i, 'label'].values[0]
        if label == 'Negative':
            tag_list.append((i,label,'#faa'))
        elif label == 'Positive':
            tag_list.append((i,label,'#afa'))
        else:
            tag_list.append((i,label,'#fea'))
    return tag_list  

text_to_annotate = [gen_annotated_text(para) for para in df.Text.tolist()]

for i in text_to_annotate:
    annotated_text(i)