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import gradio as gr
import requests
from googleapiclient.discovery import build
from transformers import pipeline
import re
# Setup Sentiment Analysis
sentiment_analyzer = pipeline("sentiment-analysis")
# YouTube API Key
YOUTUBE_API_KEY = "AIzaSyBuNxsm0LnHF0OkbYgMSNHnwu8iVUVi5gc"
def extract_video_id(url):
match = re.search(r"(?:v=|youtu\.be/)([a-zA-Z0-9_-]{11})", url)
return match.group(1) if match else None
def get_youtube_comments(video_url, max_results=20):
video_id = extract_video_id(video_url)
if not video_id:
return None, "Invalid YouTube URL."
youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
request = youtube.commentThreads().list(
part="snippet",
videoId=video_id,
maxResults=max_results,
order="relevance",
textFormat="plainText"
)
response = request.execute()
comments_data = []
for item in response["items"]:
comment_text = item["snippet"]["topLevelComment"]["snippet"]["textDisplay"]
like_count = item["snippet"]["topLevelComment"]["snippet"].get("likeCount", 0)
comments_data.append((comment_text, like_count))
return comments_data, None
def analyze_comments(video_url):
comments_data, error = get_youtube_comments(video_url)
if error:
return error
table_md = "| Comment | Sentiment | Likes |\n|---|---|---|\n"
summary_data = {
"positive": 0,
"neutral": 0,
"negative": 0,
"max_likes": -1,
"top_comment": "",
"top_sentiment": "",
"most_positive": "",
"most_negative": ""
}
for comment, likes in comments_data:
result = sentiment_analyzer(comment)[0]
sentiment = result["label"]
if sentiment == "POSITIVE":
summary_data["positive"] += 1
elif sentiment == "NEGATIVE":
summary_data["negative"] += 1
else:
summary_data["neutral"] += 1
table_md += f"| {comment} | {sentiment} | {likes} |\n"
if likes > summary_data["max_likes"]:
summary_data["max_likes"] = likes
summary_data["top_comment"] = comment
summary_data["top_sentiment"] = sentiment
if sentiment == "POSITIVE":
summary_data["most_positive"] = comment
if sentiment == "NEGATIVE":
summary_data["most_negative"] = comment
summary = (
f"\n\n### Summary:\n"
f"- Most liked comment: \"{summary_data['top_comment']}\" ({summary_data['max_likes']} likes, {summary_data['top_sentiment']})\n"
f"- Most positive comment: \"{summary_data['most_positive']}\"\n"
f"- Most negative comment: \"{summary_data['most_negative']}\"\n"
f"- Sentiment Count: {summary_data['positive']} Positive, {summary_data['neutral']} Neutral, {summary_data['negative']} Negative\n"
)
return table_md + summary
interface = gr.Interface(
fn=analyze_comments,
inputs=gr.Textbox(label="Enter the Youtube Link:"),
outputs=gr.Markdown(),
title="YouTube Comment Sentiment Analyzer",
description="Paste a YouTube video link to analyze top comments for sentiment (Positive, Negative, Neutral)."
)
interface.launch() |