from langchain.prompts import PromptTemplate
from transformers import pipeline
from langchain_core.documents import Document

def generate_summary(full_text):
    summarizer = pipeline("summarization")
    result = summarizer(full_text[:4000], max_length=150, min_length=30, do_sample=False)
    return result[0]['summary_text']

def generate_challenge_questions(qa_chain):
    prompt = "Generate 3 logic/comprehension-based questions from the uploaded document."
    result = qa_chain({ "question": prompt, "chat_history": [] })
    output = result["answer"]
    questions = output.split("\n")[:3]
    return [q.strip() for q in questions if q.strip()]

def evaluate_responses(qa_chain, questions, answers):
    combined = "\n".join([f"Q: {q}\nA: {a}" for q, a in zip(questions, answers)])
    prompt = f"Evaluate the user's answers below. For each, say if it's correct or not and justify:\n\n{combined}"
    result = qa_chain({ "question": prompt, "chat_history": [] })
    return result["answer"]

def extract_highlight_snippets(source_docs: list[Document]):
    refs = [doc.page_content.strip()[:300] + "..." for doc in source_docs[:3]]
    return refs + [""] * (3 - len(refs))