| from selenium import webdriver | |
| from selenium.webdriver.support.ui import WebDriverWait | |
| from selenium.webdriver.common.by import By | |
| import urllib.request | |
| from transformers import pipeline | |
| import streamlit as st | |
| import os | |
| import subprocess | |
| import chromedriver_autoinstaller | |
| chrome_options = webdriver.ChromeOptions() | |
| chrome_options.add_argument('--headless') | |
| chrome_options.add_argument('--no-sandbox') | |
| chrome_options.add_argument('--disable-dev-shm-usage') | |
| chromedriver_autoinstaller.install() | |
| driver = webdriver.Chrome(options=chrome_options) | |
| def determine_overall_sentiment(sentiments_list): | |
| flat_sentiments_list = [sentiment for sublist in sentiments_list for sentiment_list in sublist for sentiment in sentiment_list] | |
| label_counts = {'positive': 0, 'neutral': 0, 'negative': 0} | |
| for sentiment in flat_sentiments_list: | |
| label_counts[sentiment['label']] += 1 | |
| overall_sentiment = max(label_counts, key=label_counts.get) | |
| return overall_sentiment | |
| def get_sentiment_from_yt(query): | |
| driver.get(f"https://www.youtube.com/results?search_query={query}") | |
| wait = WebDriverWait(driver, 10) | |
| user_data = driver.find_elements(By.XPATH,'//*[@id="video-title"]') | |
| links = [] | |
| for i in user_data: | |
| attr = i.get_attribute("href") | |
| if attr is not None and "youtube.com/shorts" not in attr: | |
| video_id_start = attr.find("?v=") | |
| if video_id_start != -1: | |
| video_id_end = attr.find("&", video_id_start) | |
| if video_id_end == -1: | |
| video_id_end = None | |
| video_id = attr[video_id_start + 3: video_id_end] | |
| links.append(video_id) | |
| for i in range(len(links)): | |
| urllib.request.urlretrieve(f"https://i.ytimg.com/vi/{links[i]}/maxresdefault.jpg", f"{query}/{links[i]}.jpg") | |
| pipe_Image2Text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device = 0) | |
| pipe_GrammarCorrection = pipeline("text2text-generation", model="pszemraj/flan-t5-large-grammar-synthesis", device = 0) | |
| pipe_SentimentAnalysis = pipeline("text-classification", model="lxyuan/distilbert-base-multilingual-cased-sentiments-student", return_all_scores=True, device = 0) | |
| descriptions = [] | |
| for i in range(len(links)): | |
| filepath = f"{query}/{links[i]}.jpg" | |
| if os.path.exists(filepath): | |
| descriptions.append(pipe_Image2Text(filepath)[0]['generated_text']) | |
| sentiments = [] | |
| for i in range(len(descriptions)): | |
| res = pipe_GrammarCorrection(descriptions[i]) | |
| descriptions[i] = res[0]['generated_text'] | |
| sentiments.append(pipe_SentimentAnalysis(descriptions[i])) | |
| overall_sentiment = determine_overall_sentiment(sentiments) | |
| return links, descriptions, sentiments, overall_sentiment | |
| st.title("YouTube Sentiment Analysis") | |
| query = st.text_input("Enter YouTube search query:") | |
| if st.button("Get Sentiments"): | |
| links, descriptions, sentiments, overall_sentiment = get_sentiment_from_yt(query) | |
| st.write(f"Overall Sentiment: {overall_sentiment}") | |
| st.write("Thumbnails, Descriptions, and Sentiments:") | |
| for i in range(len(links)): | |
| st.image(f"{query}/{links[i]}.jpg", caption=f"Video {i+1}") | |
| st.write(f"**Description {i+1}:** {descriptions[i]}") | |
| st.write(f"**Sentiments {i+1}:** {sentiments[i]}") | |
| st.write("---") | |