import streamlit as st import tensorflow as tf import os import torch import cv2 import numpy as np #import requests import joblib import sklearn from PIL import Image from sklearn.decomposition import PCA from tensorflow.keras.models import load_model from transformers import pipeline token = os.environ['token'] st.set_page_config( page_title = 'Patacognition', layout = 'wide', menu_items = { "About" : 'Proyecto ideado para la investigación de "Clasificación de imágenes de una sola clase con algortimos de Inteligencia Artificial".', "Report a Bug" : 'mailto:contact@patacon.org' } ) st.sidebar.write("contact@patacon.org") cnn, vit, zero_shot, classic_ml = st.tabs(["CNN", "ViT", "Zero-Shot", "Machine Learning Clásico"]) classic_ml_root = "/home/user/app/classicML" @st.cache_resource def load_pca(): return joblib.load(os.path.join(classic_ml_root, "pca_model.pkl")) def _predict(_model_list, _img, sklearn = False): y_gorrito = 0 raw_img = cv2.cvtColor(_img, cv2.COLOR_BGR2RGB) img = cv2.resize(_img, (IMAGE_WIDTH, IMAGE_HEIGHT)) if sklearn: fl_img =[img.flatten()] data = pca.transform(fl_img) for model in _model_list: prediction = model.predict_proba(data) y_gorrito += prediction[0][Categories.index("Patacon-True")] else: 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), raw_img] #def _pca_predict(models, _img): # y_gorrito = 0 # raw_img = cv2.cvtColor(_img, cv2.COLOR_BGR2RGB) # img = cv2.resize(_img, (IMAGE_WIDTH, IMAGE_HEIGHT)) # fl_img =[img.flatten()] # data = pca.transform(fl_img) # for model in models: # prediction = model.predict_proba(data) # y_gorrito += prediction[0][Categories.index("Patacon-True")] # return [y_gorrito / len(models), raw_img] #def classic_ml_prediction(clfs, _img): # y_gorrito = 0 # raw_img = cv2.cvtColor(_img, cv2.COLOR_BGR2RGB) # img = cv2.resize(_img, (IMAGE_WIDTH, IMAGE_HEIGHT)).flatten() # data = pca.transform(img.reshape(1, -1)) # for clf in clfs: # y_gorrito += clf.predict(data) # return [y_gorrito / len(clfs), raw_img] def preprocess(file_uploader, module = 'cv2'): #makes the uploaded image readable img = np.frombuffer(uploaded_file.read(), np.uint8) if module == 'cv2': img = cv2.imdecode(img, cv2.IMREAD_COLOR) elif module == 'pil': img = Image.open(file_uploader) return img def multiclass_prediction(classifier, important_class): #made for hf zero-shot pipeline results score = (max([classifier[i]['score'] for i in range(len(classifier))])) labels = [predict['label'] for predict in classifier if score == predict['score']] for clase in classifier: if clase['label'] == important_class: class_score = clase['score'] return (labels[0] if len(labels) == 1 else labels, score, class_score) #API_URL = "https://api-inference.huggingface.co/models" #headers = {"Authorization": f"Bearer {st.secrets['token']}"} #def query(data, models): #HF API # response = requests.post(API_URL + "/" + model_name, headers=headers, data=data) # if response.json()["error"] == "Internal Server Error": # return -1 # while "error" in response.json(): # response = requests.post(API_URL + "/" + model_name, headers=headers, data=data) # return response.json()[1]["score"] #.json @st.cache_resource def load_clip(): classifier = pipeline("zero-shot-image-classification", model = 'openai/clip-vit-large-patch14-336') return classifier with cnn: col_a, col_b, = st.columns(2) ultra_flag = None with col_a: st.title("Redes neuronales convolucionales") st.caption("Los modelos no están en orden de eficacia, sino en orden de creación.") current_dir = os.getcwd() root_dir = os.path.dirname(current_dir) # Join the path to the models folder DIR = os.path.join(current_dir, "models") models = os.listdir(DIR) common_root = r"/home/user/app/models/ptctrn_v" common_end = ".h5" model_dict = dict() for model in models: #preprocessing of strings so the name is readable in the multiselect bar model_dir = os.path.join(DIR, model) model_name = 'Patacotrón ' + model_dir.split(common_root)[-1].split(common_end)[0] model_dict[model_name] = model_dir #weight_list = [] # Create a dropdown menu to select the model model_choice = st.multiselect("Seleccione uno o varios modelos de clasificación", model_dict.keys()) threshold = st.slider('¿Cuál va a ser el límite donde se considere patacón? (el valor recomendado es de 75%-80%)', 0, 100, 50, key = 'threshold_convnet') selected_models = [] # Set the image dimensions IMAGE_WIDTH = IMAGE_HEIGHT = 224 executed = False with col_b: uploaded_file = st.file_uploader(key = 'conv_upload', label = 'Sube la imagen a clasificar',type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic']) if st.button(key = 'convnet_button', label ='¿Hay un patacón en la imagen?'): if len(model_choice) < 1: st.write('Debe elegir como mínimo un modelo.') elif uploaded_file is not None: img = preprocess(uploaded_file) with st.spinner('Cargando predicción...'): selected_models = [load_model(model_dict[model_name]) for model_name in model_choice if model_name not in selected_models] #final_weights = weight_list if len(weight_list) >= 1 else [1 for i in range(len(selected_models))] y_gorrito, raw_img = _predict(selected_models, img) if round(float(y_gorrito*100)) >= threshold: st.success("¡Patacón Detectado!") else: st.error("No se considera que haya un patacón en la imagen") st.caption(f'La probabilidad de que la imagen tenga un patacón es del: {round(float(y_gorrito * 100), 2)}%') st.image(raw_img) else: st.write('Revisa haber seleccionado los modelos y la imagen correctamente.') with vit: col_a, col_b = st.columns(2) with col_a: st.title('Visual Transformers') st.caption('One class is all you need!') model_dict = { 'google/vit-base-patch16-224-in21k' : 'frncscp/patacoptimus-prime', 'facebook/dinov2-base' : 'frncscp/dinotron', 'facebook/convnext-large-224' : 'frncscp/pataconxt', 'microsoft/focalnet-small' : 'frncscp/focalnet-small-patacon', 'microsoft/swin-tiny-patch4-window7-224' : 'frncscp/patacoswin' } model_choice = st.multiselect("Seleccione un modelo de clasificación", model_dict.keys(), key = 'ViT_multiselect') uploaded_file = st.file_uploader(key = 'ViT_upload', label = 'Sube la imagen a clasificar',type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic']) flag = False threshold = st.slider('¿Cuál va a ser el límite desde donde se considere patacón? (se recomienda por encima del 80%)', 0, 100, 80, key = 'threshold_vit') with col_b: if st.button(key = 'ViT_button', label ='¿Hay un patacón en la imagen?'): if len(model_choice) < 1: print('Recuerda seleccionar al menos un modelo de clasificación') elif uploaded_file is not None: with st.spinner('Cargando predicción...'): classifiers = [pipeline("image-classification", model= model_dict[model_choice[i]], token = token) for i in range(len(model_choice))] #classifier = pipeline("image-classification", model= model_dict[model_choice[0]]) img = preprocess(uploaded_file, module = 'pil') models = [model_dict[model] for model in model_choice] #st.write(models) def vit_ensemble(classifier_list, img): y_gorrito = 0 for classifier in classifier_list: classifier = classifier(img) for clase in classifier: if clase['label'] == 'Patacon-True': y_gorrito += clase["score"] return y_gorrito / len(classifier_list) #models = [model_dict[i] for i in range(len(model_choice))] #st.write(type(models), models) #st.write(model_choice) #y_gorrito = 0 #y_gorritoo = query(uploaded_file.read(), model_choice[0])#[1]["score"] #i = -1 #st.write("loop iniciado") #for model in models: # i+=1 # st.write("y gorrito a cargar") # a = query(uploaded_file.read(), model) # if a == -1: # st.write("Los servidores se encuentrar caídos, intente más tarde") # st.write("query terminado") # y_gorritoo += a # st.write("y gorrito cargado") #y_gorritoo /= i #st.write(y_gorritoo) #st.write("loop terminado") #st.write("y gorrito calculado", len(model_choice)) #classifier = classifier(img) #for clase in classifier: # if clase['label'] == 'Patacon-True': # y_gorrito = clase["score"] #y_gorrito = classifier[0]["score"] y_gorrito = vit_ensemble(classifiers, img) # if round(float(y_gorrito * 100)) >= threshold: st.success("¡Patacón Detectado!") else: st.error("No se considera que haya un patacón en la imagen") st.caption(f'La probabilidad de que la imagen tenga un patacón es del: {round(float(y_gorrito * 100), 2)}%') st.image(img) else: st.write("Asegúrate de haber subido correctamente la imagen.") with zero_shot: col_a, col_b = st.columns(2) zsloaded = [] with col_a: st.title("Clasificación Zero-Shot") st.caption("Usando Clip de OpenAI") labels_for_classification = ["A yellow deep fried smashed plantain", "A yellow corn dough", "A stuffed fried dough", "Fried food", "Fruit", "Anything"] uploaded_file = st.file_uploader(key = 'ZS_upload', label = 'Sube la imagen a clasificar',type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic']) with col_b: if st.button(key = 'ZS_button', label ='¿Hay un patacón en la imagen?'): if uploaded_file is not None: with st.spinner('Cargando el modelo (puede demorar hasta un minuto, pero después predice rápido)'): classifier = load_clip() with st.spinner('Cargando predicción...'): img = preprocess(uploaded_file, module = 'pil') zs_classifier = classifier(img, candidate_labels = labels_for_classification) label, _, y_gorrito = multiclass_prediction(zs_classifier, labels_for_classification[0]) if label == "A yellow deep fried smashed plantain": st.success("¡Patacón Detectado!") else: st.error("No se considera que haya un patacón en la imagen") st.caption(f'La probabilidad de que la imagen tenga un patacón es del: {round(float(y_gorrito * 100), 2)}%') st.image(img) else: st.write("Asegúrate de haber subido correctamente la imagen.") with classic_ml: pca = load_pca() Categories=['Patacon-True','Patacon-False'] col_a, col_b = st.columns(2) with col_a: st.title("Machine Learning Clásico") st.caption("Usando análisis por componentes principales") model_dict = { 'Máquina de vectores de soporte' : 'pca_svm.sav', 'K-Nearest Neighbors' : 'pca_knn.sav', 'Bosques Aleatorios' : 'pca_random_forest.sav', } for model_name, filename in model_dict.items(): model_dict[model_name] = os.path.join(classic_ml_root, filename) model_choice = st.multiselect("Seleccione un modelo de clasificación", model_dict.keys(), key = 'cML_multiselect') uploaded_file = st.file_uploader(key = 'cML_upload', label = 'Sube la imagen a clasificar',type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic']) threshold = st.slider('¿Cuál va a ser el límite desde donde se considere patacón? (se recomienda por encima del 70%)', 0, 100, 70, key = 'threshold_cML') with col_b: if st.button(key = 'cML_button', label ='¿Hay un patacón en la imagen?'): if len(model_choice) < 1: print('Recuerda seleccionar al menos un modelo de clasificación') elif uploaded_file is not None: with st.spinner('Cargando predicción...'): img = preprocess(uploaded_file) selected_models = [joblib.load(model_dict[model_name]) for model_name in model_choice] y_gorrito, raw_img = _predict(selected_models, img, sklearn = True) if round(float(y_gorrito*100)) >= threshold: st.success("¡Patacón Detectado!") else: st.error("No se considera que haya un patacón en la imagen") st.caption(f'La probabilidad de que la imagen tenga un patacón es del: {round(float(y_gorrito * 100), 2)}%') st.image(raw_img) else: st.write('Revisa haber seleccionado los modelos y la imagen correctamente.')