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| import streamlit as st | |
| from dotenv import load_dotenv | |
| import googleapiclient.errors | |
| import googleapiclient.discovery | |
| import os | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| 'nlptown/bert-base-multilingual-uncased-sentiment') | |
| model = AutoModelForSequenceClassification.from_pretrained( | |
| 'nlptown/bert-base-multilingual-uncased-sentiment') | |
| load_dotenv() | |
| api_key = os.getenv("API_KEY") | |
| def get_comments(youtube, **kwargs): | |
| comments = [] | |
| results = youtube.commentThreads().list(**kwargs).execute() | |
| while results: | |
| for item in results['items']: | |
| comment = item['snippet']['topLevelComment']['snippet']['textDisplay'] | |
| comments.append(comment) | |
| # check if there are more comments | |
| if 'nextPageToken' in results: | |
| kwargs['pageToken'] = results['nextPageToken'] | |
| results = youtube.commentThreads().list(**kwargs).execute() | |
| else: | |
| break | |
| return comments | |
| def get_video_comments(video_id, api_key): | |
| os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1" | |
| youtube = googleapiclient.discovery.build( | |
| "youtube", "v3", developerKey=api_key) | |
| comments = get_comments(youtube, part="snippet", | |
| videoId=video_id, textFormat="plainText") | |
| return comments | |
| def makevideoid(url): | |
| if "?v=" in url: | |
| video_id = url.split("=")[1].split("&")[0] | |
| return video_id | |
| else: | |
| video_id = url.split("/")[3].split("?")[0] | |
| return video_id | |
| st.title("YouTube Comment Sentiment Analysis") | |
| # User input for video URL | |
| video_url = st.text_input("Enter YouTube Video URL") | |
| # Create a radio button | |
| # Content to be displayed based on the radio button state | |
| if video_url: | |
| videoid = makevideoid(video_url) | |
| comments = get_video_comments(videoid, api_key) | |
| # Display comments and sentiment analysis | |
| num = 0 | |
| with st.expander("Comment with sentiment analysis"): | |
| for i, comment in enumerate(comments, 1): | |
| tokens = tokenizer.encode( | |
| comment, return_tensors='pt', max_length=512) | |
| result = model(tokens) | |
| sentiment = int(torch.argmax(result.logits)) + 1 | |
| num += sentiment | |
| st.write(f"Comment {i}: {comment} (Sentiment: {sentiment})") | |
| st.title(num/len(comments)) | |