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import streamlit as st
from transformers import pipeline
from PIL import Image
from datasets import load_dataset, Image, list_datasets
from PIL import Image
MODELS = [
"",
"google/vit-base-patch16-224", #Classifição geral
"nateraw/vit-age-classifier" #Classifição de idade
]
DATASETS = [
"",
"Nunt/testedata",
"Nunt/backup_leonardo_2024-02-01"
]
MAX_N_LABELS = 5
def classify_images(classifier_model, dataset_to_classify):
for image in dataset:
st("Image classification: ", image['file'])
'''
image_path = image['file']
img = Image.open(image_path)
st.image(img, caption="Original image", use_column_width=True)
results = classifier(image_path, top_k=MAX_N_LABELS)
st.write(results)
st.write("----")
'''
def main():
st.title("Bulk Image Classification")
st.markdown("This app uses several 🤗 models to classify images stored in 🤗 datasets.")
st.write("Soon we will have a dataset template")
'''
Model
'''
shosen_model_name = st.selectbox("Select the model to use", MODELS)
if shosen_model_name is not None:
st.write("You selected", shosen_model_name)
'''
Dataset
'''
shosen_dataset_name = st.radio("Select the model to use", DATASETS)
if shosen_dataset_name is not None:
st.write("You selected", shosen_dataset_name)
image_object = dataset['pasta'][0]["image"]
'''
click to classify
'''
if shosen_model_name is not None and shosen_dataset_name is not None:
st.image(shosen_dataset[0], caption="Uploaded Image", use_column_width=True)
if st.button("Classify images"):
dataset = load_dataset("Nunt/testedata","testedata_readme")
classifier = pipeline('image-classification', model=model_name, device=0)
classify_images(classifier, dataset)
if __name__ == "__main__":
main() |