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import streamlit as st
import tensorflow as tf
from tensorflow.keras.models import load_model
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'
}
)
with st.sidebar:
st.write("contact@patacotron.tech")
st.title("Entorno de ejecución")
cnn, autoencoder, svm, iforest, gan, docc = st.tabs(["CNN", "Autoencoder", "OC-SVM", 'iForest', 'GAN', 'DOCC'])
with cnn:
col_a, col_b, = st.columns(2)
with col_a:
st.title("Redes neuronales convolucionales")
st.caption("Los modelos no están en orden de eficacia, sino en orden de creación.")
# 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")
threshold = .75
models = os.listdir(DIR)
model_dict = dict()
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
ultraversions = ['ptctrn_v1.4', 'ptctrn_v1.5', 'ptctrn_v1.6', 'ptctrn_v1.12']
ultra_button = st.checkbox('Ultra-Patacotrón (mejores resultados)')
ultra_flag = False
if ultra_button:
ultra_flag = True
# Create a dropdown menu to select the model
model_choice = st.multiselect("Seleccione uno o varios modelos de clasificación", model_dict.keys())
selected_models = []
@tf.function
def predict(model_list, img):
y_gorrito = 0
for model in model_list:
y_gorrito += tf.cast(model(tf.expand_dims(img/255., 0)), dtype=tf.float32)
return y_gorrito / len(model_list)
# Set the image dimensions
IMAGE_WIDTH = IMAGE_HEIGHT = 224
uploaded_file = st.file_uploader(label = '',type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic'])
executed = False
with col_b:
if st.button('¿Hay un patacón en la imagen?'):
if len(selected_models) > 0 and ultra_flag:
st.write('Debe elegir un solo método: Ultra-Patacotrón o selección múltiple.')
elif uploaded_file is not None:
raw_img = tf.image.decode_image(uploaded_file.read(), channels=3)
img = tf.image.resize(raw_img,(IMAGE_WIDTH, IMAGE_HEIGHT))
# Pass the image to the model and get the prediction
if ultra_flag:
with st.spinner('Cargando ultra-predicción...'):
if not executed:
ultraptctrn = [load_model(model_dict[model]) for model in ultraversions]
executed = True
y_gorrito = predict(ultraptctrn, img)
else:
with st.spinner('Cargando predicción...'):
selected_models = [load_model(model_dict[model]) for model in model_choice if model not in selected_models]
y_gorrito = predict(selected_models, img)
if y_gorrito >= threshold:
st.success("¡Patacón Detectado!")
else:
st.error("No se encontró rastro de patacón.")
st.caption(f'La probabilidad de que la imagen tenga un patacón es del: {round(float(y_gorrito), 2)*100}%')
st.image(raw_img.numpy())
st.caption('Si los resultados no fueron los esperados, por favor, [haz click aquí](https://docs.google.com/forms/d/e/1FAIpQLScH0ZxAV8aSqs7TPYi86u0nkxvQG3iuHCStWNB-BoQnSW2V0g/viewform?usp=sf_link)')
else:
st.write('Revisa haber seleccionado los modelos y la imagen correctamente.')
with autoencoder:
st.write('Próximamente')
with svm:
st.write('Próximamente')
with iforest:
st.write('Próximamente')
with gan:
st.write('Próximamente')
with docc:
st.write('Próximamente') |