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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'
# COL1, COL2 = st.columns([3, 1])
# CONTAINER_TOP = st.container()
# CONTAINER_BODY = st.container()
# CONTAINER_FULL = st.container()
# CONTAINER_LOOP = st.container()
COL1=""
COL2=""
COLS=""
CONTAINER_TOP=""
CONTAINER_BODY=""
CONTAINER_FULL=""
CONTAINER_LOOP=""
#(image_object, classifier_pipeline)
#def classify_one_image(classifier_model, dataset_to_classify):
#classify_one_image(image_object, classifier_pipeline)
def classify_one_image(classifier_model, dataset_to_classify):
#image_object = dataset[SPLIT_TO_CLASSIFY][i]["image"]
#st.image(image_object, caption="Uploaded Image", width=300)
#for i in range(len(dataset_to_classify)):
#for image in dataset_to_classify:
#image_object = dataset[SPLIT_TO_CLASSIFY][i]["image"]
#st.image(image_object, caption="Uploaded Image", width=300)
#st.write(f"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("----")
return "done"
def classify_full_dataset(shosen_dataset_name, chosen_model_name):
image_count = 0
#dataset
dataset = load_dataset(shosen_dataset_name,"testedata_readme")
with CONTAINER_LOOP:
#Image teste load
image_object = dataset['pasta'][0]["image"]
st.image(image_object, caption="Uploaded Image", width=300)
st.write("### FLAG 3")
#modle instance
classifier_pipeline = pipeline('image-classification', model=chosen_model_name)
CONTAINER_LOOP.write("### FLAG 4")
#classification
classification_result = classifier_pipeline(image_object)
CONTAINER_LOOP.write(classification_result)
CONTAINER_LOOP.write("### FLAG 5")
#classification_array.append(classification_result)
#save classification
image_count += 1
CONTAINER_LOOP.write(f"Image count: {image_count}")
return image_count
def make_template():
COLS = st.columns([3, 1])
CONTAINER_FULL = st.container()
with CONTAINER_FULL:
CONTAINER_TOP = st.container()
CONTAINER_BODY = st.container()
with CONTAINER_BODY:
#COL1, COL2 = st.columns([3, 1])
with COLS[1]:
CONTAINER_LOOP = st.container()
def main():
make_template()
with CONTAINER_TOP:
st.title("Bulk Image Classification DEMO")
# TODO Restart or reset your app
# if st.button("Restart"):
# # Code to restart or reset your app goes here
# import subprocess
# subprocess.call(["shutdown", "-r", "-t", "0"])
with CONTAINER_BODY:
with COL1:
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 = st.selectbox("Select the model to use", MODELS, index=0)
if chosen_model_name is not None:
COL1.st.write("You selected", chosen_model_name)
#Dataset
shosen_dataset_name = st.selectbox("Select the dataset to use", DATASETS, index=0)
if shosen_dataset_name is not None:
COL1.st.write("You selected", shosen_dataset_name)
#click to classify
#image_object = dataset['pasta'][0]
if chosen_model_name is not None and shosen_dataset_name is not None:
if COL1.button("Classify images"):
#classification_array =[]
classification_result = classify_full_dataset(shosen_dataset_name, chosen_model_name)
CONTAINER_LOOP.write(f"Classification result: {classification_result}")
#classification_array.append(classification_result)
#st.write("# FLAG 6")
#st.write(classification_array)
if __name__ == "__main__":
main() |