from langchain_core.prompts import PromptTemplate
from typing import List
import app.models as models
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")


def format_prompt(prompt) -> PromptTemplate:
    # TODO: format the input prompt by using the model specific instruction template
    # TODO: return a langchain PromptTemplate
    chat = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ]
    # TODO: apply the chat template to the prompt
    formatted_prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
    return PromptTemplate.from_template(formatted_prompt) # type: ignore

def format_chat_history(messages: List[models.Message]):
    # TODO:  implement format_chat_history to format 
    # the list of Message into a text of chat history.
    chat_history = ""
    for message in messages:
        chat_history += f"{message.type}: {message.message}\n"
    return chat_history

def format_context(docs: List[str]):
    # TODO:  the output of the DataIndexer.search is a list of text, 
    # so we need to concatenate that list into a text that can fit into 
    # the rag_prompt_formatted. Implement format_context that takes a 
    # like of strings and returns the context as one string.
    raise NotImplemented

raw_prompt = "{question}"

# TODO: Create the history_prompt prompt that will capture the question and the conversation history. 
# The history_prompt needs a {chat_history} placeholder and a {question} placeholder.
history_prompt: str = """Given the following conversation provide a helpful answer to the follow up question.
Chat History:
{chat_history}
Follow Up question: {question}
helpful answer:"""

# TODO: Create the standalone_prompt prompt that will capture the question and the chat history
# to generate a standalone question. It needs a {chat_history} placeholder and a {question} placeholder,
standalone_prompt: str = None

# TODO: Create the rag_prompt that will capture the context and the standalone question to generate
# a final answer to the question.
rag_prompt: str = None

# TODO: create raw_prompt_formatted by using format_prompt
raw_prompt_formatted = format_prompt(raw_prompt)
raw_prompt = PromptTemplate.from_template(raw_prompt)

# TODO: use format_prompt to create history_prompt_formatted
history_prompt_formatted: PromptTemplate = format_prompt(history_prompt)
# TODO: use format_prompt to create standalone_prompt_formatted
standalone_prompt_formatted: PromptTemplate = format_prompt(standalone_prompt)
# TODO: use format_prompt to create rag_prompt_formatted
rag_prompt_formatted: PromptTemplate = format_prompt(rag_prompt)