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import os
import yt_dlp
import gradio as gr
from faster_whisper import WhisperModel
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
import openai
import torch
# Optional: Set your OpenAI API key (use env var for security)
openai.api_key = "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" # Replace with your real key or set as os.environ["OPENAI_API_KEY"]
def download_and_extract_audio(youtube_url):
output_path = "downloads"
os.makedirs(output_path, exist_ok=True)
ydl_opts = {
'format': 'bestaudio/best',
'outtmpl': os.path.join(output_path, '%(id)s.%(ext)s'),
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'mp3',
'preferredquality': '192',
}],
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
info_dict = ydl.extract_info(youtube_url, download=True)
video_id = info_dict.get("id", None)
filename = os.path.join(output_path, f"{video_id}.mp3")
return filename
def transcribe_audio(audio_path):
model = WhisperModel("base", compute_type="int8", device="cuda" if torch.cuda.is_available() else "cpu")
segments, _ = model.transcribe(audio_path)
transcript = " ".join([seg.text for seg in segments])
return transcript
# Preload FLAN-T5 model offline
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
local_gen = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
def generate_response(transcript, user_prompt, use_online=False):
prompt = f"""You are a helpful AI assistant. Based on the transcript of a video, please {user_prompt.strip().lower()}.
Transcript:
{transcript[:3000]}"""
if use_online:
try:
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
max_tokens=1000
)
return response.choices[0].message["content"]
except Exception as e:
return f"⚠️ Online API failed: {str(e)}"
else:
result = local_gen(prompt, max_length=1024, do_sample=False)
return result[0]['generated_text']
def enhanced_ai_study_pipeline(video_source, youtube_url, upload_file, user_prompt, use_online_api):
try:
if video_source == "YouTube URL":
audio_path = download_and_extract_audio(youtube_url)
elif video_source == "Upload File" and upload_file is not None:
audio_path = upload_file.name
else:
return "No valid input provided.", ""
transcript = transcribe_audio(audio_path)
ai_response = generate_response(transcript, user_prompt, use_online=use_online_api)
return transcript, ai_response
except Exception as e:
return "Error occurred", str(e)
video_input = gr.Radio(["YouTube URL", "Upload File"], label="Video Source")
youtube_url = gr.Textbox(label="Enter YouTube URL")
upload_file = gr.File(label="Upload a Video File", file_types=[".mp4", ".mp3", ".wav"])
user_prompt = gr.Textbox(label="What do you want from the transcript?", placeholder="e.g., Prepare a diet plan based on this video")
use_online_api = gr.Checkbox(label="Use Online API (GPT-4)", value=False)
gr.Interface(
fn=enhanced_ai_study_pipeline,
inputs=[video_input, youtube_url, upload_file, user_prompt, use_online_api],
outputs=[
gr.Textbox(label="Transcript"),
gr.Textbox(label="AI Response")
],
title="📚 AI Transcription Assistant (Offline + Online GPT)",
description="Upload or paste a YouTube video. Enter your goal and get a smart AI answer. Works offline with FLAN-T5 or online with GPT-4."
).launch(share=True)
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