from langchain_ollama.chat_models import ChatOllama
from langchain_core.prompts import HumanMessagePromptTemplate, ChatPromptTemplate, SystemMessagePromptTemplate
from langchain.tools import Tool
from libs.test_class import Test

def system_human_answer_match(test: Test, answer: str, base_url: str) -> bool:

    def rate(rating: bool) -> None:
        """Rate answer as correct (True) or as incorrect (False)."""

    prompt = ChatPromptTemplate.from_messages([
        SystemMessagePromptTemplate.from_template(template="""Rate the answer as correct, if the answer is  
                                                  {validation_input}
                                                  
                                                  else as incorrect. Only use the rate tool. Do not answer conversationally."""),
#         SystemMessagePromptTemplate.from_template(template="""You are a rating machine. You are given 3 things: The system message, the Human query, and the AI response. You evaluate the response as correct if 
# {validation_input}

# If the answer does not match these criteria, rate the answer as incorrect. If the answer is a "refusal" or a "declaration of incapability", the answer is automatically incorrect.

# **Only use the rate tool. Do not under any circumstances answer conversationally**.
# DO NOT ANSWER WITH <I'm sorry but I do not have the capability to perform this task for you...> or anything like it.
# Use the rate tool!"""),
        HumanMessagePromptTemplate.from_template(template="""System Message:
{system_msg}

Query:
{human_msg}

Answer:
{answer}
""")
    ]).invoke({
        "validation_input": test.validation_input,
        "system_msg": test.runnable_input['system_msg'],
        "human_msg": test.runnable_input['human_msg'],
        "answer": answer
    })

    llm = ChatOllama(
        model="llama3.1:70b",
        # model="llama3-groq-tool-use:70b",
        base_url=base_url
    ).bind_tools([rate])

    ai_msg = llm.invoke(prompt)

    try:
        return ai_msg.tool_calls[0]['args']['rating']
    except IndexError as e:
        print(f"\033[0;31mValidation Error \033[0mof {test.name} <{ai_msg.content[:20]}...> Retrying...")
        return system_human_answer_match(test=test, answer=answer)

from re import search

def regex_match_any(test: Test, answer: str, base_url: str) -> bool:
    match = False
    for pattern in test.validation_input['patterns']:
        if search(pattern, answer):
            match = True
    return match