from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnableLambda

INTENT_TEMPLATE_STR = """
You are an assistant whose job is to identify the user's intent.
Given the message below, return a short phrase describing what they want.

User message:
{user_message}
"""

FORMAT_TEMPLATE_STR = """
You are a friendly book guide.
Using the raw book results below, create a clear and beginner friendly reply.

Rules:
Return up to three books,
Each line should contain the title, the author, and a short summary,
Keep the language simple.

Raw results:
{raw_results}
"""

intent_prompt = PromptTemplate.from_template(INTENT_TEMPLATE_STR)
format_prompt = PromptTemplate.from_template(FORMAT_TEMPLATE_STR)

intent_chain = RunnableLambda(
    lambda user_message: intent_prompt.format(user_message=user_message)
) | RunnableLambda(
    lambda prompt_text: call_ollama_direct(prompt_text)
)

format_chain = RunnableLambda(
    lambda raw_text: format_prompt.format(raw_results=raw_text)
) | RunnableLambda(
    lambda prompt_text: call_ollama_direct(prompt_text)
)

def extract_intent(user_message):
    result = intent_chain.invoke(user_message)
    return str(result).strip()

def format_recommendations(raw_results):
    cleaned  =pretty_raw_results(raw_results)
    result = format_chain.invoke(cleaned)
    return str(result).strip()

# to test intent #

from ai_agent import extract_intent
print(extract_intent("Find books similar to Atomic Habits"))

# to test recommendation #

from ai_agent import format_recommendations, fetch_openlibrary_by_title_or_subject

res = fetch_openlibrary_by_title_or_subject("mindfulness")
print(format_recommendations(res))