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import streamlit as st |
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from selenium import webdriver |
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from selenium.webdriver.common.by import By |
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from selenium.webdriver.chrome.options import Options |
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from selenium.webdriver.chrome.service import Service |
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import pandas as pd |
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from selenium.webdriver.common.keys import Keys |
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from selenium.webdriver.support.ui import WebDriverWait |
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from selenium.webdriver.support import expected_conditions as EC |
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import time |
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import sys |
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from datetime import datetime |
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from webdriver_manager.chrome import ChromeDriverManager |
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from selenium.webdriver.chrome.service import Service as ChromeService |
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from webdriver_manager.core.os_manager import ChromeType |
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import re |
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import transformers |
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from transformers import DistilBertTokenizer, DistilBertForSequenceClassification |
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import io |
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import plotly.express as px |
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import zipfile |
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import torch |
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import matplotlib.pyplot as plt |
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from wordcloud import WordCloud |
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from nltk.corpus import stopwords |
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import nltk |
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nltk.download('stopwords') |
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with st.sidebar: |
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st.button("DEMO APP", type="primary") |
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expander = st.expander("**Important notes on the YouTube Comments Sentiment Analysis App**") |
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expander.write(''' |
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**How to Use** |
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This app works with a YouTube URL. Paste the URL and press the 'Sentiment Analysis' button to perform sentiment analysis on your YouTube Comments. |
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**Usage Limits** |
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You can perform sentiment analysis on YouTube Comments up to 5 times. |
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**Subscription Management** |
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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] |
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**Customization** |
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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. |
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**Charts** |
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Hover to interact with and download the charts. |
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**File Handling and Errors** |
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For any errors or inquiries, please contact us at [email protected] |
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''') |
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st.subheader("YouTube Comments Sentiment Analysis", divider="red") |
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tokenizer = transformers.DistilBertTokenizer.from_pretrained("tabularisai/robust-sentiment-analysis") |
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model = transformers.DistilBertForSequenceClassification.from_pretrained("tabularisai/robust-sentiment-analysis") |
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if 'url_count' not in st.session_state: |
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st.session_state['url_count'] = 0 |
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max_attempts = 2 |
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def update_url_count(): |
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st.session_state['url_count'] += 1 |
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def clear_question(): |
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st.session_state["url"] = "" |
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url = st.text_input("Enter YouTube URL:", key="url") |
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st.button("Clear question", on_click=clear_question) |
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if st.button("Sentiment Analysis", type="secondary"): |
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if st.session_state['url_count'] < max_attempts: |
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if url: |
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with st.spinner("Wait for it...", show_time=True): |
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options = Options() |
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options.add_argument("--headless") |
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options.add_argument("--disable-gpu") |
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options.add_argument("--no-sandbox") |
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options.add_argument("--disable-dev-shm-usage") |
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options.add_argument("--start-maximized") |
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service = Service(ChromeDriverManager(chrome_type=ChromeType.CHROMIUM).install()) |
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driver = webdriver.Chrome(service=service, options=options) |
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data = [] |
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wait = WebDriverWait(driver, 30) |
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driver.get(url) |
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placeholder = st.empty() |
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progress_bar = st.progress(0) |
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for item in range(30): |
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try: |
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body = WebDriverWait(driver, 30).until(EC.visibility_of_element_located((By.TAG_NAME, "body"))) |
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body.send_keys(Keys.END) |
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placeholder.text(f"Scrolled {item + 1} times") |
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progress_bar.progress((item + 1) / 150) |
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time.sleep(0.5) |
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except Exception as e: |
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st.error(f"Exception during scrolling: {e}") |
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break |
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placeholder.text("Scrolling complete.") |
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progress_bar.empty() |
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data = [] |
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try: |
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wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "#contents #contents"))) |
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comments = driver.find_elements(By.CSS_SELECTOR, "#content #content-text") |
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user_id = 1 |
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for comment in comments: |
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timestamp = datetime.now().strftime("%Y-%m-%d") |
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data.append({"User ID": user_id, "Comment": comment.text, "comment_date": timestamp}) |
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user_id += 1 |
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data = [dict(t) for t in {tuple(d.items()) for d in data}] |
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except Exception as e: |
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st.error(f"Exception during comment extraction: {e}") |
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driver.quit() |
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df = pd.DataFrame(data, columns=["User ID", "Comment", "comment_date"]) |
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st.dataframe(df) |
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if tokenizer and model: |
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inputs = tokenizer(df['Comment'].tolist(), return_tensors="pt", padding=True, truncation=True) |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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predicted_probabilities = torch.nn.functional.softmax(logits, dim=-1) |
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predicted_labels = predicted_probabilities.argmax(dim=1) |
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results = [] |
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for i, label in enumerate(predicted_labels): |
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results.append({'Review Number': i + 1, 'Sentiment': model.config.id2label[label.item()]}) |
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sentiment_df = pd.DataFrame(results) |
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value_counts1 = sentiment_df['Sentiment'].value_counts().rename_axis('Sentiment').reset_index(name='count') |
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final_df = value_counts1 |
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tab1, tab2 = st.tabs(["Pie Chart", "Bar Chart"]) |
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with tab1: |
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fig1 = px.pie(final_df, values='count', names='Sentiment', hover_data=['count'], labels={'count': 'count'}) |
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fig1.update_traces(textposition='inside', textinfo='percent+label') |
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st.plotly_chart(fig1) |
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result = pd.concat([df, sentiment_df], axis=1) |
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st.dataframe(result) |
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with tab2: |
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fig2 = px.bar(result, x="Sentiment", y="comment_date", color="Sentiment") |
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st.plotly_chart(fig2) |
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text = " ".join(review for review in df['Review']) |
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stopwords = set(stopwords.words('english')) |
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text = re.sub('[^A-Za-z]+', ' ', text) |
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words = text.split() |
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clean_text = [word for word in words if word.lower() not in stopwords] |
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clean_text = ' '.join(clean_text) |
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stopwords = set(stopwords.words('english')) |
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wc = WordCloud(width=800, height=400, background_color='white').generate(clean_text) |
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fig = plt.figure(figsize=(12,6)) |
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plt.imshow(wc, interpolation='bilinear') |
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plt.axis('off') |
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st.pyplot(fig) |
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csv = result.to_csv(index=False) |
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st.download_button( |
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label="Download data as CSV", |
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data=csv, |
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file_name='Summary of the results.csv', |
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mime='text/csv', |
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) |
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else: |
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st.warning("Please enter a URL.") |
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else: |
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st.warning(f"You have reached the maximum URL attempts ({max_attempts}).") |
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if 'url_count' in st.session_state: |
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st.write(f"URL pasted {st.session_state['url_count']} times.") |
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