import gradio as gr
import pandas as pd
import io
from PIL import Image
from matplotlib import pyplot as plt
from bioprocess_model import BioprocessModel  # Asegúrate de que BioprocessModel esté en su propio archivo

def create_interface(process_fn):
    with gr.Blocks() as demo:
        gr.Markdown("# Interfaz de Usuario para el Procesamiento de Datos de Bioproceso")

        # Inputs de archivo y parámetros
        file_input = gr.File(label="Subir archivo Excel con los datos", file_types=[".xls", ".xlsx"])

        with gr.Row():
            with gr.Column():
                legend_position = gr.Radio(
                    choices=["upper left", "upper right", "lower left", "lower right", "best"],
                    label="Posición de la leyenda",
                    value="best"
                )
                show_legend = gr.Checkbox(label="Mostrar Leyenda", value=True)

            with gr.Column():
                params_positions = ["upper left", "upper right", "lower left", "lower right", "outside right"]
                params_position = gr.Radio(
                    choices=params_positions,
                    label="Posición de los parámetros",
                    value="upper right"
                )
                show_params = gr.Checkbox(label="Mostrar Parámetros", value=True)

        # Parámetros adicionales
        model_type = gr.Radio(["logistic", "luedeking-piret"], label="Tipo de Modelo", value="logistic")
        mode = gr.Radio(["independent", "average", "combinado"], label="Modo de Análisis", value="independent")

        # Estilo gráfico
        styles = ['white', 'dark', 'whitegrid', 'darkgrid', 'ticks']
        style_dropdown = gr.Dropdown(choices=styles, label="Selecciona el estilo de gráfico", value='whitegrid')

        line_color_picker = gr.ColorPicker(label="Color de la línea", value='#0000FF')
        point_color_picker = gr.ColorPicker(label="Color de los puntos", value='#000000')

        line_style_options = ['-', '--', '-.', ':']
        line_style_dropdown = gr.Dropdown(choices=line_style_options, label="Estilo de línea", value='-')

        marker_style_options = ['o', 's', '^', 'v', 'D', 'x', '+', '*']
        marker_style_dropdown = gr.Dropdown(choices=marker_style_options, label="Estilo de punto", value='o')

        # Botón de Procesar
        process_button = gr.Button("Procesar")

        # Salida
        output_gallery = gr.Gallery(label="Gráfico Generado", columns=2, height='auto')
        output_text = gr.Textbox(label="Análisis Generado", lines=10)

        # Conectar el botón con la función de procesamiento
        process_button.click(
            fn=process_fn,
            inputs=[file_input, legend_position, params_position, model_type, mode, style_dropdown,
                    line_color_picker, point_color_picker, line_style_dropdown, marker_style_dropdown,
                    show_legend, show_params],
            outputs=[output_gallery, output_text]
        )

    return demo

def process_and_plot(file, legend_position, params_position, model_type, mode, style,
                     line_color, point_color, line_style, marker_style, show_legend, show_params):
    # Leer el archivo Excel proporcionado
    excel_data = pd.ExcelFile(file.name)
    figures = []
    model = BioprocessModel()
    model.fit_model(model_type)

    for sheet_name in excel_data.sheet_names:
        df = pd.read_excel(excel_data, sheet_name=sheet_name)
        model.process_data(df)

        # Obtener los datos de tiempo, biomasa, sustrato y producto
        time = model.time
        biomass = model.dataxp[-1]
        substrate = model.datasp[-1]
        product = model.datapp[-1]

        # Generar los gráficos
        fig = model.plot_combined_results(time, biomass, substrate, product,
                                          model.dataxp[-1], model.datasp[-1], model.datapp[-1],
                                          model.datax_std[-1], model.datas_std[-1], model.datap_std[-1],
                                          experiment_name=sheet_name, legend_position=legend_position,
                                          params_position=params_position, show_legend=show_legend,
                                          show_params=show_params, style=style, line_color=line_color,
                                          point_color=point_color, line_style=line_style,
                                          marker_style=marker_style)
        figures.append(fig)

    # Convertir los gráficos en imágenes
    image_list = []
    for fig in figures:
        buf = io.BytesIO()
        fig.savefig(buf, format='png')
        buf.seek(0)
        image = Image.open(buf)
        image_list.append(image)

    # Análisis básico generado
    analysis = f"Se han procesado {len(excel_data.sheet_names)} hojas con el modelo {model_type}."

    return image_list, analysis

# Crear y lanzar la interfaz
demo = create_interface(process_and_plot)
demo.launch(share=True)