Update app.py

app.py

@@ -1,74 +1,19 @@
-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
-
-
+from webdriver_manager.chrome import ChromeType
 import transformers
-from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
-import io
+import torch
 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]
-   
-    
-    
-''')
-
 
 st.subheader("YouTube Comments Sentiment Analysis", divider="red")
 tokenizer = transformers.DistilBertTokenizer.from_pretrained("tabularisai/robust-sentiment-analysis")
@@ -109,8 +54,8 @@ if st.button("Sentiment Analysis", type="secondary"):
 
                 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) / 30)
                         time.sleep(0.5)
@@ -121,26 +66,58 @@ if st.button("Sentiment Analysis", type="secondary"):
                 placeholder.text("Scrolling complete.")
                 progress_bar.empty()
 
-                data = []
                 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_elements = comment.find_elements(By.XPATH, './/a[@id="time"]') #Correct XPATH syntax.
-                        timestamp = "Timestamp not found"
-                        if timestamp_elements:
-                            timestamp = timestamp_elements[0].text
-                            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}]
-                    
+                        try:
+                            # Robust XPath to find the timestamp
+                            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:
+                            timestamp = "Timestamp not found"
+                        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)
-
-               
-
-            
+                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
+                        predicted_probabilities = torch.nn.functional.softmax(logits, dim=-1)
+                        predicted_labels = predicted_probabilities.argmax(dim=1)
+                        results = []
+                        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"])
+                    with tab1:
+                        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')
+                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.")