app.py

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()