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import gradio as gr
import pandas as pd
from googleapiclient.discovery import build
import re
from transformers import pipeline
# ⛔ Replace with your actual API key
YOUTUBE_API_KEY = "AIzaSyAgdAAGU2ySpnsjx1lv6dJ4fmJOXYU0Ggw"
# Setup: Load Hugging Face sentiment analysis pipeline
sentiment_pipeline = pipeline("sentiment-analysis")
# Function to extract video ID from YouTube URL
def extract_video_id(url):
patterns = [
r"(?:youtube\.com\/watch\?v=|youtu\.be\/|youtube\.com\/embed\/)([^&\n?#]+)",
r"youtube\.com\/shorts\/([^&\n?#]+)"
]
for pattern in patterns:
match = re.search(pattern, url)
if match:
return match.group(1)
return None
# Function to fetch comments using YouTube API
def fetch_comments(video_url, max_results=10):
video_id = extract_video_id(video_url)
if not video_id:
return pd.DataFrame({"error": ["Invalid YouTube URL"]})
youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
request = youtube.commentThreads().list(
part="snippet",
videoId=video_id,
maxResults=max_results,
textFormat="plainText"
)
comments = []
try:
response = request.execute()
for item in response["items"]:
comment = item["snippet"]["topLevelComment"]["snippet"]["textDisplay"]
comments.append(comment)
return pd.DataFrame({"Comment": comments})
except Exception as e:
return pd.DataFrame({"error": [str(e)]})
# Main analysis function
def analyze_video(video_url, max_comments=10):
df = fetch_comments(video_url, max_comments)
if "error" in df.columns:
return df.to_string(index=False)
results = []
for comment in df["Comment"]:
result = sentiment_pipeline(comment[:512])[0]
results.append({
"Comment": comment,
"Sentiment": result["label"],
"Score": round(result["score"], 3)
})
result_df = pd.DataFrame(results)
return result_df
# Gradio UI
with gr.Blocks(title="YouTube Comment Sentiment Analyzer") as demo:
gr.Markdown("# 📊 YouTube Comment Sentiment Analyzer")
video_url = gr.Textbox(label="📺 YouTube Video URL", placeholder="Paste the video link here")
max_comments = gr.Slider(1, 100, value=10, step=1, label="Number of Comments")
btn = gr.Button("Analyze")
output = gr.Dataframe(label="Sentiment Analysis Result", interactive=False)
btn.click(fn=analyze_video, inputs=[video_url, max_comments], outputs=output)
demo.launch() |