"""Load models to use them as a narrator and a common-sense oracle in the PAYADOR pipeline."""
import google.generativeai as genai
import requests
import os


class GeminiModel():
    def __init__ (self, api_key_file:str, model_name:str = "gemini-pro") -> None:
        """"Initialize the Gemini model using an API key."""
        self.safety_settings = [
            {
                "category": "HARM_CATEGORY_DANGEROUS",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_HARASSMENT",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_HATE_SPEECH",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
                "threshold": "BLOCK_NONE",
            },
        ]
        genai.configure(api_key=os.getenv(api_key_file))
        self.model = genai.GenerativeModel(model_name)

    def prompt_model(self,prompt: str) -> str:
        """Prompt the Gemini model."""
        return self.model.generate_content(prompt, safety_settings=self.safety_settings).text


def prompt_HF_API (prompt: str, model: str = "microsoft/Phi-3-mini-4k-instruct", api_key_file: str = "HF_API_key"):
    API_URL = f"https://api-inference.huggingface.co/models/{model}"

    headers = {"Authorization": f"Bearer {get_api_key(api_key_file)}"}
    payload = {"inputs": prompt}

    output = requests.post(API_URL, headers=headers, json=payload).json()
    
    return output[0]["generated_text"]

def get_api_key(path: str) -> str:
    """Load an API key from path."""
    key = ""
    with open(path) as f:
        key = f.readline()
    return key