app.py

import gradio as gr
from youtube_transcript_api import YouTubeTranscriptApi
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig
import re
import os
import torch

# import dotenv
# dotenv.load_dotenv()

if torch.cuda.is_available():
    device = torch.device("cuda")
    print(f"Using GPU: {torch.cuda.get_device_name(device)}")

    # Install the Flash Attention library
    print("Installing Flash Attention library...")
    import subprocess
    subprocess.run(
        "pip install flash_attn --no-build-isolation --break-system-packages",
        env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
        shell=True,
    )
    print("Flash Attention library installed")
    
    # Configure 4-bit quantization for model loading
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.bfloat16,
        attn_implementation="flash_attention_2",
    )

else:
    device = torch.device("cpu")
    bnb_config = None
    print("Using CPU")

# Uncomment and set your Hugging Face token if needed
token = os.environ["HF_TOKEN"]


# Load the Phi-3 model and tokenizer
print("Loading model and tokenizer...")
model_id = "microsoft/Phi-3-mini-128k-instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    trust_remote_code=True,
    
).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Define the system prompt and generation pipeline
system_prompt = "Summarize this YouTube video. Give a brief summary of the video content with the key points and main takeaways."
messages = [{"role": "system", "content": system_prompt}]

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 32767,
    "return_full_text": False,
    "do_sample": True,
    "temperature": 0.2,
}

# Function to extract the video ID from a YouTube URL
def extract_video_id(url):
    video_id_match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11}).*", url)
    if video_id_match:
        print(f"Extracted video ID: {video_id_match.group(1)}")
        return video_id_match.group(1)
    return None

# Function to get the transcript of a YouTube video
def get_transcript(video_id):
    try:
        transcript = YouTubeTranscriptApi.get_transcript(video_id)
        transcription = [entry['text'] for entry in transcript]
        print(f"Transcript: {transcription}")
        return " ".join(transcription)
    except Exception as e:
        return f"Error fetching transcript: {str(e)}"

# Function to summarize the text using the model
def summarize_text(text):
    messages.append({"role": "user", "content": text})
    output = pipe(messages, **generation_args)
    output = output[0]['generated_text'].strip() # type: ignore
    print(f"Summary: {output}")
    return output

# Main function to process the video URL
def process_video(url):
    video_id = extract_video_id(url)
    if not video_id:
        print("Invalid YouTube URL")
        return "Invalid YouTube URL"
    
    transcript = get_transcript(video_id)
    if transcript.startswith("Error"):
        return transcript
    
    summary = summarize_text(transcript)
    return summary, transcript

# Function to update the embedded video player
def update_embed(url):
    video_id = extract_video_id(url)
    if video_id:
        embed_url = f"https://www.youtube.com/embed/{video_id}"
        return f"<div class='gradio-embed-container'><iframe class='gradio-embed' src='{embed_url}' frameborder='0' allowfullscreen></iframe></div>"
    return "<div class='gradio-embed-container'><iframe class='gradio-embed' src='' frameborder='0' allowfullscreen></iframe></div>"

# Gradio UI setup
with gr.Blocks(css="""
    .gradio-embed-container { position: relative; width: 100%; padding-bottom: 56.25%; height: 0; }
    .gradio-embed { position: absolute; top: 0; left: 0; width: 100%; height: 100%; }
    .small-font { font-size: 0.6em; }
    """) as demo:
    
    gr.Markdown("""
    # YouTube Video Summarizer using Phi-3-mini-128k-instruct
    Summarize any YouTube video using the Phi-3-mini-128k-instruct model.
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            url = gr.Textbox(
                label="YouTube URL", 
                placeholder="https://www.youtube.com/watch?v=dQw4w9WgXcQ", 
                max_lines=1
            )
            summary = gr.Textbox(
                label="Summary", 
                placeholder="Summary will appear here...", 
                lines=10, 
                show_label=True, 
                show_copy_button=True, 
                elem_classes="small-font"
            )
            transcript = gr.Textbox(
                label="Transcript", 
                placeholder="Transcript will appear here...", 
                lines=1, 
                show_label=True, 
                show_copy_button=True, 
                elem_classes="small-font"
            )
            btn = gr.Button("Summarize")
            btn.click(fn=process_video, inputs=url, outputs=[summary, transcript])
        
        with gr.Column(scale=1):
            video_embed = gr.HTML("<div class='gradio-embed-container'><iframe class='gradio-embed' src='' frameborder='0' allowfullscreen></iframe></div>")
    
    url.change(fn=update_embed, inputs=url, outputs=video_embed)

demo.launch()