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import comet_llm

comet_llm.log_prompt(
    prompt="What is your name?",
    output=" My name is Alex.",
    api_key="<YOUR_COMET_API_KEY>",
)

import comet_llm

comet_llm.log_prompt(
    prompt="Answer the question and if the question can't be answered, say \"I don't know\"\n\n---\n\nQuestion: What is your name?\nAnswer:",
    prompt_template="Answer the question and if the question can't be answered, say \"I don't know\"\n\n---\n\nQuestion: {{question}}?\nAnswer:",
    prompt_template_variables={"question": "What is your name?"},
    metadata= {
        "usage.prompt_tokens": 7,
        "usage.completion_tokens": 5,
        "usage.total_tokens": 12,
    },
    output=" My name is Alex.",
    duration=16.598,
)

from comet_llm import Span, end_chain, start_chain
import datetime
from time import sleep


def retrieve_context(user_question):
    if "open" in user_question:
        return "Opening hours: 08:00 to 17:00 all days"


def llm_answering(user_question, current_time, context):
    prompt_template = """You are a helpful chatbot. You have access to the following context:
    {context}
    The current time is: {current_time}
    Analyze the following user question and decide if you can answer it, if the question can't be answered, say \"I don't know\":
    {user_question}
    """

    prompt = prompt_template.format(
        user_question=user_question, current_time=current_time, context=context
    )

    with Span(
        category="llm-call",
        inputs={"prompt_template": prompt_template, "prompt": prompt},
    ) as span:
        # Call your LLM model here
        sleep(0.1)
        result = "Yes we are currently open"
        usage = {"prompt_tokens": 52, "completion_tokens": 12, "total_tokens": 64}

        span.set_outputs(outputs={"result": result}, metadata={"usage": usage})

    return result


def main(user_question, current_time):
    start_chain(inputs={"user_question": user_question, "current_time": current_time})

    with Span(
        category="context-retrieval",
        name="Retrieve Context",
        inputs={"user_question": user_question},
    ) as span:
        context = retrieve_context(user_question)

        span.set_outputs(outputs={"context": context})

    with Span(
        category="llm-reasoning",
        inputs={
            "user_question": user_question,
            "current_time": current_time,
            "context": context,
        },
    ) as span:
        result = llm_answering(user_question, current_time, context)

        span.set_outputs(outputs={"result": result})

    end_chain(outputs={"result": result})


main("Are you open?", str(datetime.datetime.now().time()))