Update app.py

app.py

@@ -1,74 +1,47 @@
-import streamlit as st 
-
+import streamlit as st
 from selenium import webdriver
 from selenium.webdriver.common.by import By
 from selenium.webdriver.chrome.options import Options
-
 from selenium.webdriver.chrome.service import Service
-
 import pandas as pd
-
 from selenium.webdriver.common.keys import Keys
-
 from selenium.webdriver.support.ui import WebDriverWait
 from selenium.webdriver.support import expected_conditions as EC
 import time
-import sys
 from datetime import datetime
-
-
 from webdriver_manager.chrome import ChromeDriverManager
 from selenium.webdriver.chrome.service import Service as ChromeService
-
 from webdriver_manager.core.os_manager import ChromeType
-
 import re
-
-
 import transformers
 from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
 import io
 import plotly.express as px
 import zipfile
-import torch 
-
-
+import torch
 
 with st.sidebar:
     st.button("DEMO APP", type="primary")
-   
-
     expander = st.expander("**Important notes on the YouTube Comments Sentiment Analysis App**")
     expander.write('''
-    
-    
-    **How to Use**
-    This app works with a YouTube URL.  Paste the URL and press the 'Sentiment Analysis' button to perform sentiment analysis on your YouTube Comments.
-    
-    
-    **Usage Limits**
-    You can perform sentiment analysis on YouTube Comments up to 5 times.
-    
-    
-    **Subscription Management**
-    This demo app offers a one-day subscription, expiring after 24 hours. If you are interested in building your own YouTube Comments Sentiment Analysis Web App, we invite you to explore our NLP Web App Store on our website. You can select your desired features, place your order, and we will deliver your custom app in five business days. If you wish to delete your Account with us, please contact us at [email protected]
-    
-    
-    **Customization**
-    To change the app's background color to white or black, click the three-dot menu on the right-hand side of your app, go to Settings and then Choose app theme, colors and fonts.
-    
-    
-    **Charts**
-    Hover to interact with and download the charts.
-    
-    
-    **File Handling and Errors**
-    For any errors or inquiries, please contact us at [email protected]
-   
-    
-    
-''')
+        **How to Use**
+        This app works with a YouTube URL.  Paste the URL and press the 'Sentiment Analysis' button to perform sentiment analysis on your YouTube Comments.
+
+        **Usage Limits**
+        You can perform sentiment analysis on YouTube Comments up to 5 times.
 
+        **Subscription Management**
+        This demo app offers a one-day subscription, expiring after 24 hours. If you are interested in building your own YouTube Comments Sentiment Analysis Web App, we invite you to explore our NLP Web App Store on our website. You can select your desired features, place your order, and we will deliver your custom app in five business days. If you wish to delete your Account with us, please contact us at [email protected]
+
+        **Customization**
+        To change the app's background color to white or black, click the three-dot menu on the right-hand side of your app, go to Settings and then Choose app theme, colors and fonts.
+
+        **Charts**
+        Hover to interact with and download the charts.
+
+        **File Handling and Errors**
+        For any errors or inquiries, please contact us at [email protected]
+    ''')
 
 st.subheader("YouTube Comments Sentiment Analysis", divider="red")
 tokenizer = transformers.DistilBertTokenizer.from_pretrained("tabularisai/robust-sentiment-analysis")
@@ -103,10 +76,8 @@ if st.button("Sentiment Analysis", type="secondary"):
                 data = []
                 wait = WebDriverWait(driver, 30)
                 driver.get(url)
-
                 placeholder = st.empty()
                 progress_bar = st.progress(0)
-
                 for item in range(30):
                     try:
                         body = WebDriverWait(driver, 30).until(EC.visibility_of_element_located((By.TAG_NAME, "body")))
@@ -117,31 +88,27 @@ if st.button("Sentiment Analysis", type="secondary"):
                     except Exception as e:
                         st.error(f"Exception during scrolling: {e}")
                         break
-
                 placeholder.text("Scrolling complete.")
                 progress_bar.empty()
-
-               try:
-    wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "#contents #contents")))
-    comments = driver.find_elements(By.CSS_SELECTOR, "#content #content-text")
-    user_id = 1
-    for comment in comments:
-        timestamp = None
-        try:
-            timestamp_element = comment.find_element(By.XPATH, './ancestor::ytd-comment-renderer//yt-formatted-string[@class="published-time-text style-scope ytd-comment-renderer"]')
-            timestamp = timestamp_element.text
-        except Exception as e:
-            print(f"Timestamp not found for comment: {comment.text}. Error: {e}")
-        data.append({"User ID": user_id, "Comment": comment.text, "comment_date": timestamp})
-        user_id += 1
-    data = [dict(t) for t in {tuple(d.items()) for d in data}]
-except Exception as e:
-    st.error(f"Exception during comment extraction: {e}")
-
+                try:
+                    wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "#contents #contents")))
+                    comments = driver.find_elements(By.CSS_SELECTOR, "#content #content-text")
+                    user_id = 1
+                    for comment in comments:
+                        timestamp = None
+                        try:
+                            timestamp_element = comment.find_element(By.XPATH, './ancestor::ytd-comment-renderer//yt-formatted-string[@class="published-time-text style-scope ytd-comment-renderer"]')
+                            timestamp = timestamp_element.text
+                        except Exception as e:
+                            print(f"Timestamp not found for comment: {comment.text}. Error: {e}")
+                        data.append({"User ID": user_id, "Comment": comment.text, "comment_date": timestamp})
+                        user_id += 1
+                    data = [dict(t) for t in {tuple(d.items()) for d in data}]
+                except Exception as e:
+                    st.error(f"Exception during comment extraction: {e}")
                 driver.quit()
                 df = pd.DataFrame(data, columns=["User ID", "Comment", "comment_date"])
                 st.dataframe(df)
-
                 if tokenizer and model:
                     inputs = tokenizer(df['Comment'].tolist(), return_tensors="pt", padding=True, truncation=True)
                     with torch.no_grad():
@@ -152,7 +119,6 @@ except Exception as e:
                         for i, label in enumerate(predicted_labels):
                             results.append({'Review Number': i + 1, 'Sentiment': model.config.id2label[label.item()]})
                         sentiment_df = pd.DataFrame(results)
-
                     value_counts1 = sentiment_df['Sentiment'].value_counts().rename_axis('Sentiment').reset_index(name='count')
                     final_df = value_counts1
                     tab1, tab2 = st.tabs(["Pie Chart", "Bar Chart"])
@@ -160,14 +126,11 @@ except Exception as e:
                         fig1 = px.pie(final_df, values='count', names='Sentiment', hover_data=['count'], labels={'count': 'count'})
                         fig1.update_traces(textposition='inside', textinfo='percent+label')
                         st.plotly_chart(fig1)
-
                     result = pd.concat([df, sentiment_df], axis=1)
                     st.dataframe(result)
-
                     with tab2:
                         fig2 = px.bar(result, x="Sentiment", y="comment_date", color="Sentiment")
                         st.plotly_chart(fig2)
-
                     csv = result.to_csv(index=False)
                     st.download_button(
                         label="Download data as CSV",
@@ -175,14 +138,10 @@ except Exception as e:
                         file_name='Summary of the results.csv',
                         mime='text/csv',
                     )
-                
         else:
             st.warning("Please enter a URL.")
     else:
         st.warning(f"You have reached the maximum URL attempts ({max_attempts}).")
 
-if 'url_count' in st.session_state: #added if statement.
-    st.write(f"URL pasted {st.session_state['url_count']} times.")
-
-
-            
+if 'url_count' in st.session_state:
+    st.write(f"URL pasted {st.session_state['url_count']} times.")