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import streamlit as st
from transformers import pipeline
from PIL import Image
from datasets import load_dataset, Image
from PIL import Image, ExifTags
MODELS = [
"google/vit-base-patch16-224", #Classifição geral
"nateraw/vit-age-classifier" #Classifição de idade
]
DATASETS = [
"Nunt/testedata",
"Nunt/backup_leonardo",
"Nunt/backup_leonardo_2024-02-06"
]
MAX_N_LABELS = 5
SPLIT_TO_CLASSIFY = 'pasta'
COLS = st.columns([0.60, 0.40])
SCROLLABLE_TEXT = COLS[1].container(height=500)
def extract_file_name(image_object):
file_name = image_object.filename
return file_name
def extract_index(file_name):
return "flag: todo"
def classify_full_dataset(shosen_dataset_name, chosen_model_name):
image_count = 0
#modle instance
classifier_pipeline = pipeline('image-classification', model=chosen_model_name)
#dataset
dataset = load_dataset(shosen_dataset_name,"testedata_readme")
for i in range(len(dataset)):
SCROLLABLE_TEXT.write("i-1:" + str(i-1))
image_object = dataset['pasta'][i-1]["image"]
SCROLLABLE_TEXT.image(image_object, caption="Uploaded Image", width=300)
file_name = extract_file_name(image_object)
SCROLLABLE_TEXT.write(f"file_name: {file_name}")
image_index = extract_index(file_name)
SCROLLABLE_TEXT.write(f"image_index: {image_index}")
image_object_texte_data = dataset['pasta'][i-1]
SCROLLABLE_TEXT.write(f"image_object_texte_data: {image_object_texte_data}")
SCROLLABLE_TEXT.write(f"teste1 (image_object._getexif()): {teste1}")
teste2 = image_object._getexif().items()
SCROLLABLE_TEXT.write(f"teste2 (image_object._getexif().items()): {teste2}")
#extract_metadata = { ExifTags.TAGS[k]: v for k, v in image_object._getexif().items() if k in ExifTags.TAGS }
#SCROLLABLE_TEXT.write(f"extract_metadata: {extract_metadata}")
#classification
classification_result = classifier_pipeline(image_object)
SCROLLABLE_TEXT.write(classification_result)
#TODO save classification result in dataset
image_count += 1
SCROLLABLE_TEXT.write(f"Image count" + str(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("--- END ---")
#COLS[0].write(classification_result)
if __name__ == "__main__":
main() |