from typing import Dict, List, Any
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

class EndpointHandler():
    def __init__(self, path=""):
        model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
        model = PeftModel.from_pretrained(model, "srmorfi/phi3-mini-med-adapter")
        tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
        self.model = model
        self.tokenizer = tokenizer
        self.model.eval()

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Process input data and generate predictions using the model.
        
        Args:
            data (Dict[str, Any]): Input data containing either an "inputs" key
                                  or the input directly in the data dictionary.
        
        Returns:
            List[Dict[str, Any]]: Processed model predictions that will be serialized and returned.
        """
        inputs = data.pop("inputs", data)
        inputs = self.tokenizer(inputs, return_tensors="pt")

        print(inputs)

        with torch.no_grad():
            outputs = self.model.generate(input_ids=inputs["input_ids"], max_new_tokens=20)
            output = self.tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
            predictions = [{"generated_text": output}]
        
            return predictions