handler.py

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):
        if 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.")

        # Prompt personalizado para guiar al modelo
        input_text = f"""
        Genera un JSON válido en el siguiente formato preentrenado:
        {{\"values\": [{\"id\": \"firstName\", \"value\": \"STRING\"},{\"id\": \"lastName\", \"value\": \"STRING\"},{\"id\": \"jobTitle\", \"value\": \"STRING\"},{\"id\": \"adress\", \"value\": [{\"id\": \"[MOBILE-WORK-PERSONAL-MAIN-OTHER]\", \"value\": \"STRING\"}]},{\"id\": \"email\", \"value\": [{\"id\": \"[MOBILE-WORK-PERSONAL-MAIN-OTHER]\", \"value\": \"STRING\"}]},{\"id\": \"phone\", \"value\": [{\"id\": \"[MOBILE-WORK-PERSONAL-MAIN-OTHER]\", \"value\": \"STRING (ONLY NUMBERS)\"}]},{\"id\": \"notes\", \"value\": \"STRING\"},{\"id\": \"roleFunction\", \"value\": \"[BUYER-SELLER-SUPPLIER-PARTNER-COLLABORATOR-PROVIDER-CUSTOMER]\"}]}}
        Procesa el siguiente texto: "{data['inputs']}"
        """
        tokens = self.tokenizer(input_text, return_tensors="pt", truncation=True, padding="max_length", max_length=1000)
        return tokens

    def inference(self, tokens):
        generate_kwargs = {
            "max_length": 1000,
            "num_beams": 5,
            "do_sample": True,
            "temperature": 0.3,
            "top_k": 50,
            "top_p": 0.9,
            "repetition_penalty": 2.5
        }
        with torch.no_grad():
            outputs = self.model.generate(**tokens, **generate_kwargs)
        return outputs

    def clean_output(self, output):
        # Extraer el JSON dentro del texto generado
        try:
            start_index = output.index("{")
            end_index = output.rindex("}") + 1
            return output[start_index:end_index]
        except ValueError:
            # Si no hay un JSON válido en el texto
            return output

    def validate_json(self, json_text):
        # Validar el JSON generado
        try:
            json_data = json.loads(json_text)
            if "values" in json_data and isinstance(json_data["values"], list):
                return {"is_valid": True, "json_data": json_data}
            else:
                return {"is_valid": False, "error": "El JSON no contiene el formato esperado."}
        except json.JSONDecodeError as e:
            return {"is_valid": False, "error": f"Error decodificando JSON: {str(e)}"}

    def postprocess(self, outputs):
        decoded_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        cleaned_output = self.clean_output(decoded_output)

        # Imprimir siempre el texto generado para depuración
        print(f"Texto generado: {decoded_output}")
        print(f"JSON limpiado: {cleaned_output}")

        # Validar el JSON generado
        validation_result = self.validate_json(cleaned_output)

        if not validation_result["is_valid"]:
            print(f"Error en la validación: {validation_result['error']}")
            raise ValueError(f"JSON inválido: {validation_result['error']}")

        return {"response": validation_result["json_data"]}

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