import os
import streamlit as st
from langchain_community.document_loaders import YoutubeLoader
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace

# ✅ Set Hugging Face token from environment variable (or directly here for testing)
os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("Utube")
os.environ["HF_TOKEN"] = os.getenv("Utube")

# 🔧 Load the LLaMA 3.1 model
def load_model():
    endpoint = HuggingFaceEndpoint(
        repo_id="meta-llama/Llama-3.1-8B-Instruct",
        provider="nebius",
        temperature=0.5,
        max_new_tokens=150,
        task="conversational"
    )
    return ChatHuggingFace(
        llm=endpoint,
        repo_id="meta-llama/Llama-3.1-8B-Instruct",
        provider="nebius",
        temperature=0.5,
        max_new_tokens=50,
        task="conversational"
    )

# 🎥 Get transcript from YouTube video
def fetch_transcript(url):
    loader = YoutubeLoader.from_youtube_url(url)
    docs = loader.load()
    return docs[0].page_content

# ✨ Generate summary
def summarize(transcript, model):
    if len(transcript) > 3000:
        transcript = transcript[:3000]  # Trim for token safety
    prompt = f"Summarize the following YouTube transcript:\n\n{transcript}"
    response = model.invoke(prompt)
    return response.content

# 🖥️ Streamlit UI
def main():
    st.set_page_config(page_title="🎬 YouTube Video Summarizer", layout="centered")
    st.title("🎬 YouTube Video Summarizer")
    st.write("Paste a YouTube video URL below and get a smart summary using LLaMA 3.1 🧠")

    url = st.text_input("📥 Enter YouTube video URL:")

    if st.button("Summarize"):
        if not url.strip():
            st.warning("🚫 Please enter a valid YouTube video URL.")
            return

        with st.spinner("🔄 Fetching transcript and generating summary..."):
            try:
                model = load_model()
                transcript = fetch_transcript(url)
                summary = summarize(transcript, model)
                st.success("✅ Summary:")
                st.write(summary)
            except Exception as e:
                st.error(f"❌ Error: {e}")

# 🚀 Run the app
if __name__ == "__main__":
    main()