from core.models import get_llm
from prompts import SUMMARY_MAP_PROMPT, SUMMARY_REDUCE_PROMPT
from langchain_core.output_parsers import StrOutputParser
from langchain_text_splitters import CharacterTextSplitter

class Summarizer:
    def __init__(self):
        self.llm = get_llm()

    def generate_deep_summary(self, full_text):
        total_length = len(full_text)
        if total_length < 25000:
            chunk_size = 5000
        else:
            dynamic_chunk_size = total_length // 5
            chunk_size = max(dynamic_chunk_size, 20000)

        text_splitter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_size // 10)
        chunks = text_splitter.split_text(full_text)

        map_chain = SUMMARY_MAP_PROMPT | self.llm | StrOutputParser()
        
        inputs = [{"text": chunk} for chunk in chunks]
        summaries = map_chain.batch(inputs)

        all_summaries = "\n\n".join(summaries)
        reduce_chain = SUMMARY_REDUCE_PROMPT | self.llm | StrOutputParser()
        
        deep_summary = reduce_chain.invoke({"text": all_summaries})
        return deep_summary