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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
SPLIT_TO_CLASSIFY = 'pasta'
COLS = st.columns([0.75, 0.25])
SCROLLABLE_TEXT = COLS[1].container(height=500)
def classify_full_dataset(shosen_dataset_name, chosen_model_name):
image_count = 0
for i in range(len(dataset)):
image_object = dataset['pasta'][i]["image"]
SCROLLABLE_TEXT.image(image_object, caption="Uploaded Image", width=300)
#dataset
dataset = load_dataset(shosen_dataset_name,"testedata_readme")
#Image teste load
image_object = dataset['pasta'][0]["image"]
SCROLLABLE_TEXT.image(image_object, caption="Uploaded Image", width=300)
#modle instance
classifier_pipeline = pipeline('image-classification', model=chosen_model_name)
#classification
classification_result = classifier_pipeline(image_object)
SCROLLABLE_TEXT.write(classification_result)
#TODO save classification
image_count += 1
SCROLLABLE_TEXT.write("Image count")
SCROLLABLE_TEXT.write(image_count)
def main():
COLS[0].write("# Bulk Image Classification App")
#with CONTAINER_BODY:
with COLS[0]:
st.markdown("This app uses several 🤗 models to classify images stored in 🤗 datasets.")
st.write("Soon we will have a dataset template")
#Model
chosen_model_name = COLS[0].selectbox("Select the model to use", MODELS, index=0)
if chosen_model_name is not None:
COLS[0].write("You selected")
COLS[0].write(chosen_model_name)
#Dataset
shosen_dataset_name = COLS[0].selectbox("Select the dataset to use", DATASETS, index=0)
if shosen_dataset_name is not None:
COLS[0].write("You selected")
COLS[0].write(shosen_dataset_name)
#click to classify
if chosen_model_name is not None and shosen_dataset_name is not None:
if COLS[0].button("Classify images"):
classify_full_dataset(shosen_dataset_name, chosen_model_name)
COLS[0].write("Classification result {classification_result}")
COLS[0].write(classification_result)
if __name__ == "__main__":
main() |