# interface.py

from models import BioprocessModel
import io
from PIL import Image
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from sympy import symbols, sympify, lambdify
import copy
from config import DEVICE, MODEL_PATH, MAX_LENGTH, TEMPERATURE

# Configuración del dispositivo
device = DEVICE

# Cargar el modelo
model_path = MODEL_PATH  # Reemplaza con la ruta real de tu modelo
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
model.to(device)
model.eval()

@torch.no_grad()
def generate_analysis(prompt, max_length=MAX_LENGTH):
    try:
        input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
        max_gen_length = min(max_length + input_ids.size(1), model.config.max_position_embeddings)

        generated_ids = model.generate(
            input_ids=input_ids,
            max_length=max_gen_length,
            temperature=TEMPERATURE,
            num_return_sequences=1,
            no_repeat_ngram_size=2,
            early_stopping=True
        )

        output_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        analysis = output_text[len(prompt):].strip()
        return analysis
    except Exception as e:
        return f"Ocurrió un error durante el análisis: {e}"

def parse_bounds(bounds_str, num_params):
    try:
        bounds = eval(f"[{bounds_str}]")
        if len(bounds) != num_params:
            raise ValueError
        lower_bounds = [b[0] for b in bounds]
        upper_bounds = [b[1] for b in bounds]
        return lower_bounds, upper_bounds
    except:
        lower_bounds = [-np.inf] * num_params
        upper_bounds = [np.inf] * num_params
        return lower_bounds, upper_bounds

# Aquí incluye la función process_and_plot completa, asegurándote de que no haya referencias a decorators o @spaces.GPU

def process_and_plot(
    # Your parameters here
):
    # Your implementation here
    # ...
    return [image], analysis

# Importar la función create_interface desde UI.py
from UI import create_interface

# Si deseas ejecutar la interfaz desde este archivo, asegúrate de que este bloque no cause conflictos al importar
if __name__ == "__main__":
    demo = create_interface()
    demo.launch()