import streamlit as st
from langchain_community.document_loaders import YoutubeLoader
from langchain.chat_models import ChatHuggingFace
from langchain.llms import HuggingFaceEndpoint
from langchain.schema import HumanMessage
import os

# Set page layout
st.set_page_config(page_title="YouTube Summarizer with DeepSeek", layout="centered")
st.title("📺 YouTube Video Summarizer with DeepSeek")

hf_token = os.getenv("HF_TOKEN")  # For local dev
if hf_token:
    os.environ["HUGGINGFACEHUB_API_KEY"] = hf_token
# Input
url = st.text_input("Enter YouTube Video URL:")

# Connect to DeepSeek-R1 via Nebius
@st.cache_resource
def load_deepseek_model():
    deep_seek_model = HuggingFaceEndpoint(
        repo_id="deepseek-ai/DeepSeek-R1",
        provider="nebius",
        temperature=0.7,
        max_new_tokens=30,
        task="conversational",
        huggingfacehub_api_token=hf_token  # ✅ Use your HF token here
    )
    return ChatHuggingFace(llm=deep_seek_model)

deepseek = load_deepseek_model()

if st.button("Extract and Summarize"):
    if url:
        try:
            # Step 1: Load transcript
            loader = YoutubeLoader.from_youtube_url(url)
            data = loader.load()
            transcript = data[0].page_content if data else "No transcript found."

            st.subheader("📖 Extracted Transcript")
            st.text_area("Transcript:", transcript, height=300)

            # Step 2: Prepare prompt
            prompt = f"Summarize the following video transcript:\n\n{transcript}"

            with st.spinner("Summarizing using DeepSeek..."):
                response = deepseek([HumanMessage(content=prompt)])
                summary = response.content.strip()

            st.subheader("🧠 Summary")
            st.success(summary)

        except Exception as e:
            st.error(f"❌ Error: {str(e)}")
    else:
        st.warning("Please enter a valid YouTube URL.")