import streamlit as st

import streamlit as st
import tensorflow as tf
import numpy as np
from keras.models import load_model
from tensorflow.keras.backend import clear_session
import cv2
import os

st.set_page_config(
    page_title = 'Patacotrón',
    initial_sidebar_state = 'collapsed',
    menu_items = {
        "About" : 'Proyecto ideado para la investigación de "Clasificación de imágenes de una sola clase con algortimos de Inteligencia Artificial".',
        "Report a Bug" : 'https://docs.google.com/forms/d/e/1FAIpQLScH0ZxAV8aSqs7TPYi86u0nkxvQG3iuHCStWNB-BoQnSW2V0g/viewform?usp=sf_link'
    }
)


st.title("Entorno de ejecución")
st.markdown("Los modelos no están en orden de eficacia, sino en orden de creación. En la pestaña de Estadísticas podrá encontrar más información.")

# Get the absolute path to the current directory
current_dir = os.path.abspath(os.path.dirname(__file__))

# Get the absolute path to the parent directory of the current directory
root_dir = os.path.abspath(os.path.join(current_dir, os.pardir))

# Join the path to the models folder
DIR = os.path.join(root_dir, "models")


models = os.listdir(DIR)

model_dict = dict()
#for model in models:
#    model_name = model.split(DIR)
#    model_dict[model_name] = 0
#model_list = []

for model in models:
    model_name = model.split(DIR)
    model_name = str(model.split('.h5')[0])
    model_dir = os.path.join(DIR, model)
    model_dict[model_name] = model_dir
    #model_list.append(os.path.join(DIR, model))

# Create a dropdown menu to select the model
model_choice = st.multiselect("Seleccione un modelo de clasificación", model_dict.keys())

selected_models = []
for model in model_choice:
    selected_models.append(model)

def ensemble_model(model_list, img):
    average = len(model_list)
    y_gorrito = np.zeros((1, 1))
    for model in model_list:
        instance_model = load_model(model_dict[model])
        y_gorrito += float(instance_model.predict(np.expand_dims(img, 0)))
        clear_session()
    return y_gorrito/average

# Set the image dimensions
IMAGE_WIDTH = IMAGE_HEIGHT = 224

# Create a file uploader widget
uploaded_file = st.file_uploader("Elige una imagen...", type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic'])

if uploaded_file is not None:
    # Load the image and resize it to the required dimensions
    img = np.frombuffer(uploaded_file.read(), np.uint8)
    img = cv2.imdecode(img, cv2.IMREAD_COLOR)
    raw_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (IMAGE_WIDTH, IMAGE_HEIGHT))

    # Convert the image to RGB and preprocess it for the model
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = img / 255

    # Pass the image to the model and get the prediction
    with st.spinner('Cargando predicción...'):
        y_gorrito = ensemble_model(selected_models, img)
    #y_gorrito = model.predict(np.expand_dims(img, 0))

    # Show the image
    col1, col2, col3 = st.columns(3)

    with col1:
        st.write(' ')

    with col2:
        #check_type = isinstance(y_gorrito, (int, float))
        #if not check_type:
            #st.write('No se ha escogido ningún modelo.')
    #else:
        st.write(f'La probabilidad de que la imagen tenga un patacón es del: {round(float(y_gorrito), 2)*100}%')
        

    with col3:
        st.write('Si los resultados no fueron los esperados, por favor, despliga la barra lateral y entra al botón "Report a Bug"')
        st.image(raw_img)