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 *
import validators

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

if st.session_state.url or st.session_state.upload:
    
    results, title = inference(st.session_state.url,st.session_state.upload)

    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 = sentiment_pipe(earnings_passages)
    
    with st.expander("See Transcribed Earnings Text"):
        st.write(f"Number of Sentences: {len(earnings_sentences)}")
        
        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=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)
    
    ## 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=True,
        width=1000,
        height=500,
        margin=dict(
            b=5,
            t=50,
            pad=4
        )
    )
    
    st.plotly_chart(fig)
    
else:
    st.write("No YouTube URL or file upload detected")