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import streamlit as st
import pandas as pd
import plotly_express as px
import plotly.graph_objects as go
from functions import *
import validators
import textwrap
#st.set_page_config(page_title="Earnings Sentiment Analysis", page_icon="📈")
st.sidebar.header("Sentiment Analysis")
st.markdown("## Earnings Sentiment Analysis with FinBert-Tone")
#load whisper model
asr_model = load_asr_model(st.session_state.sbox)
if "url" not in st.session_state:
st.session_state.url = ''
if "title" not in st.session_state:
st.session_state.title = ''
try:
if st.session_state['url'] is not None or st.session_state['upload'] is not None:
results, title = inference(st.session_state.url,st.session_state.upload,asr_model)
print(f'results, page1: {results}')
st.subheader(title)
earnings_passages = clean_text(results)
st.session_state['earnings_passages'] = earnings_passages
st.session_state['title'] = title
earnings_sentiment, earnings_sentences = sentiment_pipe(earnings_passages)
with st.expander("See Transcribed Earnings Text"):
st.write(f"Number of Sentences: {len(earnings_sentences)}")
st.write(st.session_state['earnings_passages'])
## Save to a dataframe for ease of visualization
sen_df = pd.DataFrame(earnings_sentiment)
sen_df['text'] = earnings_sentences
grouped = pd.DataFrame(sen_df['label'].value_counts()).reset_index()
grouped.columns = ['sentiment','count']
st.session_state['sen_df'] = sen_df
# Display number of positive, negative and neutral sentiments
fig = px.bar(grouped, x='sentiment', y='count', color='sentiment', color_discrete_map={"Negative":"firebrick","Neutral":\
"navajowhite","Positive":"darkgreen"},\
title='Earnings Sentiment')
fig.update_layout(
showlegend=False,
autosize=True,
margin=dict(
l=25,
r=25,
b=25,
t=50,
pad=2
)
)
st.plotly_chart(fig)
## Display sentiment score
pos_perc = grouped[grouped['sentiment']=='Positive']['count'].iloc[0]*100/sen_df.shape[0]
neg_perc = grouped[grouped['sentiment']=='Negative']['count'].iloc[0]*100/sen_df.shape[0]
neu_perc = grouped[grouped['sentiment']=='Neutral']['count'].iloc[0]*100/sen_df.shape[0]
sentiment_score = neu_perc+pos_perc-neg_perc
fig_1 = go.Figure()
fig_1.add_trace(go.Indicator(
mode = "delta",
value = sentiment_score,
domain = {'row': 1, 'column': 1}))
fig_1.update_layout(
template = {'data' : {'indicator': [{
'title': {'text': "Sentiment Score"},
'mode' : "number+delta+gauge",
'delta' : {'reference': 50}}]
}},
autosize=False,
width=250,
height=250,
margin=dict(
l=5,
r=5,
b=5,
pad=2
)
)
with st.sidebar:
st.plotly_chart(fig_1)
hd = sen_df.text.apply(lambda txt: '<br>'.join(textwrap.wrap(txt, width=70)))
## Display negative sentence locations
fig = px.scatter(sen_df, y='label', color='label', size='score', hover_data=[hd], color_discrete_map={"Negative":"firebrick","Neutral":"navajowhite","Positive":"darkgreen"}, title='Sentiment Score Distribution')
fig.update_layout(
showlegend=False,
autosize=True,
width=800,
height=500,
margin=dict(
b=5,
t=50,
pad=4
)
)
st.plotly_chart(fig)
else:
st.write("No YouTube URL or file upload detected")
except (AttributeError, TypeError):
st.write("No YouTube URL or file upload detected") |