Update pages/1_Earnings_Sentiment_Analysis_📈_.py

pages/1_Earnings_Sentiment_Analysis_📈_.py

@@ -9,104 +9,107 @@ 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.on_change or st.session_state.upload.on_change:
+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)
+    st.subheader(title)
     
-with open('earnings.txt','r') as f:
-    earnings_passages = f.read()
+    earnings_passages = results['text']
     
-earnings_sentiment, earnings_sentences = sent_pipe(earnings_passages)
-
-with st.expander("See Transcribed Earnings Text"):
-    st.write(f"Number of Sentences: {len(earnings_ssentences)}")
+    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)
     
-    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
+    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
+    
+    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
+    
+    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)
+    
+    st.plotly_chart(fig)
+    
+else:
+    st.write("No YouTube URL or file upload detected")