pages/1_Earnings_Sentiment_Analysis_📈_.py

import streamlit as st
import pandas as pd
import plotly_express as px
import plotly.graph_objects as go
from functions import *

st.set_page_config(page_title="Earnings Sentiment Analysis", page_icon="📈")
st.sidebar.header("Sentiment Analysis")
st.markdown("## Earnings Sentiment Analysis with FinBert-Tone")


results, title = inference(url_input,upload_wav)

st.subheader(title)

earnings_passages = results['text']

with open('earnings.txt','w') as f:
    f.write(earnings_passages)
    
with open('earnings.txt','r') as f:
    earnings_passages = f.read()
    
earnings_sentiment, earnings_sentences = sent_pipe(earnings_passages)

with st.expander("See Transcribed Earnings Text"):
    st.write(f"Number of Sentences: {len(earnings_ssentences)}")
    
    st.write(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']

# 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=50,
        r=50,
        b=50,
        t=50,
        pad=4
    )
)

col1, col2 = st.columns(2)

col1.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 = go.Figure()

fig.add_trace(go.Indicator(
    mode = "delta",
    value = sentiment_score,
    domain = {'row': 1, 'column': 1}))

fig.update_layout(
	template = {'data' : {'indicator': [{
        'title': {'text': "Sentiment score"},
        'mode' : "number+delta+gauge",
        'delta' : {'reference': 50}}]
                         }},
    autosize=False,
    width=400,
    height=500,
    margin=dict(
        l=20,
        r=50,
        b=50,
        pad=4
    )
)

col2.plotly_chart(fig)

## Display negative sentence locations
fig = px.scatter(sen_df, y='label', color='label', size='score', hover_data=['text'], color_discrete_map={"Negative":"firebrick","Neutral":"navajowhite","Positive":"darkgreen"}, title='Sentiment Score Distribution')


fig.update_layout(
	showlegend=False,
    autosize=False,
    width=1000,
    height=500,
    margin=dict(
        l=50,
        r=50,
        b=50,
        t=50,
        pad=4
    )
)

st.plotly_chart(fig)