Spaces:
Sleeping
Sleeping
File size: 5,230 Bytes
c277c70 |
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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import os
import gradio as gr
import yt_dlp
import whisper
from pydub import AudioSegment
from transformers import pipeline
from youtube_transcript_api import YouTubeTranscriptApi
from urllib.parse import urlparse, parse_qs
import openai
import json
import tempfile
import re
import torch
from googleapiclient.discovery import build # Add the import for Google API client
# Function to extract YouTube video ID
def extract_video_id(url):
try:
parsed_url = urlparse(url)
if "youtube.com" in parsed_url.netloc:
query_params = parse_qs(parsed_url.query)
return query_params.get('v', [None])[0]
elif "youtu.be" in parsed_url.netloc:
return parsed_url.path.strip("/")
return None
except Exception:
return None
# Function to get video duration
def get_video_duration(video_id, api_key):
try:
youtube = build("youtube", "v3", developerKey=api_key)
request = youtube.videos().list(part="contentDetails", id=video_id)
response = request.execute()
if response["items"]:
duration = response["items"][0]["contentDetails"]["duration"]
match = re.match(r'PT(?:(\d+)H)?(?:(\d+)M)?(?:(\d+)S)?', duration)
hours = int(match.group(1)) if match.group(1) else 0
minutes = int(match.group(2)) if match.group(2) else 0
seconds = int(match.group(3)) if match.group(3) else 0
return hours * 60 + minutes + seconds / 60
return None
except Exception:
return None
# Download and transcribe with Whisper
def download_and_transcribe_with_whisper(youtube_url):
try:
with tempfile.TemporaryDirectory() as temp_dir:
temp_audio_file = os.path.join(temp_dir, "audio.mp3")
ydl_opts = {
'format': 'bestaudio/best',
'outtmpl': temp_audio_file,
'extractaudio': True,
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
ydl.download([youtube_url])
audio = AudioSegment.from_file(temp_audio_file)
wav_file = os.path.join(temp_dir, "audio.wav")
audio.export(wav_file, format="wav")
model = whisper.load_model("large")
result = model.transcribe(wav_file)
return result['text']
except Exception:
return None
# Function to summarize using Hugging Face
def summarize_text_huggingface(text):
summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=0 if torch.cuda.is_available() else -1)
max_input_length = 1024
chunk_overlap = 100
text_chunks = [
text[i:i + max_input_length]
for i in range(0, len(text), max_input_length - chunk_overlap)
]
summaries = [
summarizer(chunk, max_length=100, min_length=50, do_sample=False)[0]['summary_text']
for chunk in text_chunks
]
return " ".join(summaries)
# Function to generate optimized content with OpenAI
def generate_optimized_content(api_key, summarized_transcript):
openai.api_key = api_key
prompt = f"""
Analyze the following summarized YouTube video transcript and:
1. Extract the top 10 keywords.
2. Generate an optimized title (less than 65 characters).
3. Create an engaging description.
4. Generate related tags for the video.
Summarized Transcript:
{summarized_transcript}
Provide the results in the following JSON format:
{{
"keywords": ["keyword1", "keyword2", ..., "keyword10"],
"title": "Generated Title",
"description": "Generated Description",
"tags": ["tag1", "tag2", ..., "tag10"]
}}
"""
try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are an SEO expert."},
{"role": "user", "content": prompt}
]
)
response_content = response['choices'][0]['message']['content']
return json.loads(response_content)
except Exception:
return None
# Main Gradio function
def process_video(youtube_url, youtube_api_key, openai_api_key):
video_id = extract_video_id(youtube_url)
if not video_id:
return "Invalid YouTube URL.", "", ""
video_length = get_video_duration(video_id, youtube_api_key)
if not video_length:
return "Error fetching video duration.", "", ""
transcript = download_and_transcribe_with_whisper(youtube_url)
if not transcript:
return "Error fetching transcript.", "", ""
summary = summarize_text_huggingface(transcript)
optimized_content = generate_optimized_content(openai_api_key, summary)
return summary, json.dumps(optimized_content, indent=4), transcript
# Gradio Interface
youtube_api_key = os.getenv("YOUTUBE_API_KEY")
openai_api_key = os.getenv("OPENAI_API_KEY")
gr.Interface(
fn=lambda youtube_url: process_video(youtube_url, youtube_api_key, openai_api_key),
inputs="text",
outputs=["text", "text", "text"],
title="YouTube Transcript Summarizer",
description="Enter a YouTube URL to extract, summarize, and optimize content.",
).launch()
|