import os
import logging

import google.generativeai as genai
from openai import OpenAI
import anthropic


DEFAULT_PROVIDER = "openai"
DEFAULT_MODEL = "gpt-4o-mini"

# Load API keys
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
AI_STUDIO_API_KEY = os.getenv("AI_STUDIO_API_KEY")
ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")

assert (
    OPENAI_API_KEY
), "OPENAI_API_KEY is not set, please set it in your environment variables."
assert (
    AI_STUDIO_API_KEY
), "AI_STUDIO_API_KEY is not set, please set it in your environment variables."
assert (
    ANTHROPIC_API_KEY
), "ANTHROPIC_API_KEY is not set, please set it in your environment variables."

# Initialize clients
openai_client = OpenAI(api_key=OPENAI_API_KEY)
genai.configure(api_key=AI_STUDIO_API_KEY)
anthropic_client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY)


SYSTEM_PROMPT = "You are a programming assistant. You are solving the 2024 advent of code challenge."
PROMPT_TEMPLATE = """You are solving the 2024 advent of code challenge.
You will be provided the description of each challenge. You are to provide the solution to each given challenge.
1) You can reason and explain your logic before writing the code.
2) You must write the code such that it can be parsed into an actual python file.
3) It will be parsed by the evaluator, so it must be valid python code.
4) All of the code must be in a single code block, delimited by ```python and ```.
5) To count as a proper submission, the code must print the result to each question asked.
6) Each question will have a single string as an answer. Make sure to print it that string, and nothing else.
7) The actual input to the question will be provided in a file relative to the python file, e.g. "./input.txt". You must read and parse from the file accordingly. You can safely assume the file will always be relative to the python file.

Here is an example of a proper submission:

You reasoning goes here ...

```python


file = "input.txt"

def your_function(...)
    ...

...
print(result1)


def your_other_function(...)
    ...

...
print(result2)

```

Here is today's challenge description:
{problem_description}
"""


def build_prompt(
    problem_description: str, prompt_template: str = PROMPT_TEMPLATE
) -> str:
    return prompt_template.format(problem_description=problem_description)


def get_completion(
    provider: str,
    user_prompt: str,
    system_prompt: str,
    model: str,
    temperature: float,
) -> str:
    """
    Unified function to get completions from various LLM providers.
    """
    if provider == "openai":
        completion = openai_client.chat.completions.create(
            model=model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt},
            ],
            temperature=temperature,
        )
        # logger.info("Completion: %s", completion)
        return completion.choices[0].message.content

    elif provider == "gemini":
        model = genai.GenerativeModel(
            model_name=model,
            system_instruction=system_prompt,
        )
        response = model.generate_content(
            user_prompt,
            generation_config=genai.types.GenerationConfig(temperature=temperature),
        )

        # logger.info("reponse: %s", response)
        return response.text

    elif provider == "anthropic":
        response = anthropic_client.messages.create(
            model=model,
            max_tokens=2048,
            temperature=temperature,
            system=system_prompt,
            messages=[{"role": "user", "content": user_prompt}],
        )
        # logger.info("Response: %s", response)
        return response.content[0].text

    else:
        raise ValueError(f"Unknown provider: {provider}")