Summarize the content of an HTML page using Llama Index

page_summarizer.py

from llama_index.core import Document, VectorStoreIndex
from llama_index.core import Settings
import trafilatura

class PageSummarizer:
    def __init__(self, llm, embed_model):
        self.llm = llm
        self.embed_model = embed_model
        
    def summarize(self, html: str) -> str:
        Settings.llm = self.llm
        Settings.embed_model = self.embed_model
        
        page_content = trafilatura.extract(html)
        
        documents = [Document(text=page_content)]
        index = VectorStoreIndex.from_documents(documents)
        query_engine = index.as_query_engine()
        instruction = "Provide a detailled summary of this text"
        page_content_summary = query_engine.query(instruction).response
        return page_content_summary

from llama_index.llms.groq import Groq

model = "llama3-8b-8192"
llm = Groq(model=model, temperature=0.1)
embed_model = context.embedding

page_summarizer = PageSummarizer(llm, embed_model)