Update pages/1_Earnings_Sentiment_Analysis_📈_.py

pages/1_Earnings_Sentiment_Analysis_📈_.py

@@ -12,104 +12,111 @@ st.markdown("## Earnings Sentiment Analysis with FinBert-Tone")
 if "url" not in st.session_state:
     st.session_state.url = ''
 
-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)
+try:
 
-    st.subheader(title)
-    
-    earnings_passages = results['text']
-    
-    st.session_state['earnings_passages'] = earnings_passages
+    if st.session_state['url'] is not None or st.session_state['upload'] is not None:
         
-    earnings_sentiment, earnings_sentences = sentiment_pipe(earnings_passages)
+        results, title = inference(st.session_state.url,st.session_state.upload)
     
-    with st.expander("See Transcribed Earnings Text"):
-        st.write(f"Number of Sentences: {len(earnings_sentences)}")
+        st.subheader(title)
         
-        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']
-    
-    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
+        earnings_passages = results['text']
+        
+        st.session_state['earnings_passages'] = earnings_passages
+            
+        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']
+        
+        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
+        
+        
+        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
+        
+        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)
+        
+        st.plotly_chart(fig)
+        
+    else:
     
-else:
+        st.write("No YouTube URL or file upload detected")
+        
+except AttributeError:
+
     st.write("No YouTube URL or file upload detected")