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