Create app.py

app.py

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+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 
+from streamlit_extras.stylable_container import stylable_container
+
+
+
+
+
+
+
+
+tokenizer = transformers.DistilBertTokenizer.from_pretrained("tabularisai/robust-sentiment-analysis")
+model = transformers.DistilBertForSequenceClassification.from_pretrained("tabularisai/robust-sentiment-analysis")
+
+def clear_question():
+    st.session_state["youtube_video_url"] = ""
+
+youtube_video_url = st.text_input("Enter Google Maps URL:", key="youtube_video_url")
+st.button("Clear question", on_click=clear_question)
+
+if st.button("Scrape Reviews"):
+    options = Options()
+    options.add_argument("--headless")
+    options.add_argument("--disable-gpu")
+    options.add_argument("--no-sandbox")
+    options.add_argument("--disable-dev-shm-usage")
+    options.add_argument("--start-maximized")
+    service = Service(ChromeDriverManager(chrome_type=ChromeType.CHROMIUM).install())
+    driver = webdriver.Chrome(service=service, options=options)
+    data = []
+    wait = WebDriverWait(driver, 30) 
+    driver.get(youtube_video_url)
+    
+   
+    placeholder = st.empty()  # Create an empty placeholder for dynamic text
+    progress_bar = st.progress(0)  # Create a progress bar
+
+    for item in range(150):
+        try:
+            body = WebDriverWait(driver, 30).until(EC.visibility_of_element_located((By.TAG_NAME, "body")))
+            body.send_keys(Keys.END)
+            placeholder.text(f"Scrolled {item + 1} times")  # Update placeholder text
+            progress_bar.progress((item + 1) / 150)  # Update progress bar
+            time.sleep(3)  # Increased sleep time for better loading
+        except Exception as e:
+            st.error(f"Exception during scrolling: {e}")
+            break
+
+    placeholder.text("Scrolling complete.") #show completion message.
+    progress_bar.empty() #remove progress bar.
+
+   
+    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  # Initialize unique user ID
+        for comment in comments:
+            timestamp = datetime.now().strftime("%Y-%m-%d")
+            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():
+            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
+            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)
+
+            
+            fig2 = px.bar(result, x="Sentiment", y="comment_date", color="Sentiment")
+         
+            st.plotly_chart(fig2)
+
+            buf = io.BytesIO()
+            with zipfile.ZipFile(buf, "w") as myzip:
+                myzip.writestr("Summary of the results.csv", result.to_csv(index=False))
+            with stylable_container(
+                    key="download_button",
+                    css_styles="""button { background-color: yellow; border: 1px solid black; padding: 5px; color: black; }""",):
+                st.download_button(
+                        label="Download zip file",
+                        data=buf.getvalue(),
+                        file_name="zip_file.zip",
+                        mime="application/zip",
+                    )
+
+            
+
+      
+
+            
+