Update app.py

app.py

@@ -4,44 +4,14 @@ 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
-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
+from webdriver_manager.chrome import ChromeType
 import transformers
-from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
-import io
-import plotly.express as px
-import zipfile
 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]
-    ''')
+import plotly.express as px
 
 st.subheader("YouTube Comments Sentiment Analysis", divider="red")
 tokenizer = transformers.DistilBertTokenizer.from_pretrained("tabularisai/robust-sentiment-analysis")
@@ -76,40 +46,54 @@ 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")))
-                        body.send_keys(Keys.END)
+                        driver.execute_script("window.scrollBy(0, 500);")
+                        wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "#content #content-text")))
                         placeholder.text(f"Scrolled {item + 1} times")
-                        progress_bar.progress((item + 1) / 150)
-                        time.sleep(0.5)
+                        progress_bar.progress((item + 1) / 30)
+                        time.sleep(1) #Increased wait time for dynamic loading
                     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:
+                            # Try a more direct XPath
                             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:
+                            try:
+                                # Try a more general XPath
+                                timestamp_element = comment.find_element(By.XPATH, './ancestor::ytd-comment-renderer//yt-formatted-string[contains(@class, "time-text")]')
+                                timestamp = timestamp_element.text
+                            except Exception:
+                                try:
+                                    #try grabbing the a tag.
+                                    timestamp_element = comment.find_element(By.XPATH, './ancestor::ytd-comment-renderer//a[@id="time"]')
+                                    timestamp = timestamp_element.text
+                                except Exception as inner_e:
+                                    print(f"Timestamp not found for comment: {comment.text}. Error: {inner_e}") #debug
+                        data.append({"Comment": comment.text, "comment_date": timestamp})
+
                 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:
+                df = pd.DataFrame(data, columns=["Comment", "comment_date"])
+
+                if not df.empty and not df['Comment'].tolist() == []:
+                    st.dataframe(df)
                     inputs = tokenizer(df['Comment'].tolist(), return_tensors="pt", padding=True, truncation=True)
                     with torch.no_grad():
                         logits = model(**inputs).logits
@@ -119,6 +103,7 @@ if st.button("Sentiment Analysis", type="secondary"):
                         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"])
@@ -126,22 +111,23 @@ if st.button("Sentiment Analysis", type="secondary"):
                         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",
-                        data=csv,
-                        file_name='Summary of the results.csv',
-                        mime='text/csv',
-                    )
+                    st.download_button(label="Download data as CSV", data=csv, file_name='Summary of the results.csv', mime='text/csv')
+                else:
+                    st.warning("No comments were scraped. Sentiment analysis could not be performed.")
+
         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:
-    st.write(f"URL pasted {st.session_state['url_count']} times.")
+    st.write(f"URL pasted {st.session_state['url_count']} times.")