from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import json

class EndpointHandler:
    def __init__(self, model_dir):
        self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
        self.model.eval()

    def preprocess(self, data):
        # Validar entrada
        if not data or not isinstance(data, dict) or "inputs" not in data or data["inputs"] is None:
            raise ValueError("La entrada debe ser un diccionario con la clave 'inputs' y un valor válido")

        input_text = "Generate a valid JSON capturing data from this text: " + data["inputs"]
        if not input_text.strip():
            raise ValueError("El texto de entrada no puede estar vacío")

        # Tokenización de la entrada
        tokens = self.tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
        return tokens

    def inference(self, tokens):
        with torch.no_grad():
            outputs = self.model.generate(**tokens)
        return outputs

    def postprocess(self, outputs):
        decoded_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return {"generated_text": decoded_output}

    def __call__(self, data):
        tokens = self.preprocess(data)
        outputs = self.inference(tokens)
        result = self.postprocess(outputs)
        return result