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| import streamlit as st | |
| import tensorflow as tf | |
| 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' | |
| } | |
| ) | |
| col_a, col_b, = st.columns(2) | |
| with col_a: | |
| st.title("Entorno de ejecución") | |
| 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 = .8 | |
| 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 = [] | |
| def ensemble_model(model_list, img): | |
| 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/255., 0))) | |
| #clear_session() | |
| return y_gorrito/len(model_list) | |
| 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] | |
| 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.') |