File size: 2,827 Bytes
ed149a7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | import gradio as gr
import pytube
from transformers import pipeline
import os
from textblob import TextBlob
# Initialize sentiment analysis pipeline
sentiment_analyzer = pipeline("sentiment-analysis")
def analyze_youtube_content(youtube_url, transcript_text=""):
"""Main function to analyze YouTube content"""
results = {}
# If URL is provided, get video info
if youtube_url:
try:
# Create a YouTube object
yt = pytube.YouTube(youtube_url)
results["video_info"] = {
"title": yt.title,
"status": "success"
}
except Exception as e:
results["video_info"] = {
"status": "error",
"message": str(e)
}
# If transcript is provided, analyze it
if transcript_text:
# Analyze sentiment with TextBlob
blob = TextBlob(transcript_text)
textblob_sentiment = blob.sentiment
# Analyze sentiment with Hugging Face
hf_result = sentiment_analyzer(transcript_text[:512])[0]
results["sentiment"] = {
"textblob": {
"polarity": round(textblob_sentiment.polarity, 2),
"assessment": "positive" if textblob_sentiment.polarity > 0 else "negative" if textblob_sentiment.polarity < 0 else "neutral"
},
"huggingface": {
"label": hf_result["label"],
"score": round(hf_result["score"], 4)
}
}
# Identify key moments based on sentiment
sentences = [str(sentence) for sentence in blob.sentences]
key_moments = []
for i, sentence in enumerate(sentences):
sentiment = TextBlob(sentence).sentiment.polarity
if abs(sentiment) > 0.5:
key_moments.append({
"text": sentence,
"sentiment": sentiment
})
results["key_moments"] = key_moments[:5] # Top 5 moments
return results
# Create Gradio interface
with gr.Blocks(title="YouTube Viral Moment Analyzer") as demo:
gr.Markdown("# YouTube Viral Moment Analyzer")
with gr.Row():
youtube_url = gr.Textbox(label="YouTube URL")
with gr.Row():
transcript_text = gr.Textbox(label="Transcript Text", lines=10)
analyze_button = gr.Button("Analyze Content")
output = gr.JSON(label="Analysis Results")
analyze_button.click(
fn=analyze_youtube_content,
inputs=[youtube_url, transcript_text],
outputs=output,
api_name="analyze_content"
)
# Launch the app with server name and port for proper deployment
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |