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import streamlit as st
from transformers import pipeline
from PIL import Image
MODEL_1 = "google/vit-base-patch16-224"
MIN_ACEPTABLE_SCORE = 0.1
MAX_N_LABELS = 5
MODEL_2 = "nateraw/vit-age-classifier"
MODELS = [
"google/vit-base-patch16-224", #Classifição geral
"nateraw/vit-age-classifier", #Classifição de idade
"microsoft/resnet-50", #Classifição geral
"Falconsai/nsfw_image_detection", #Classifição NSFW
"cafeai/cafe_aesthetic", #Classifição de estética
"microsoft/resnet-18", #Classifição geral
"microsoft/resnet-34", #Classifição geral escolhida pelo copilot
"microsoft/resnet-101", #Classifição geral escolhida pelo copilot
"microsoft/resnet-152", #Classifição geral escolhida pelo copilot
"microsoft/swin-tiny-patch4-window7-224",#Classifição geral
"-- Reinstated on testing--",
"microsoft/beit-base-patch16-224-pt22k-ft22k", #Classifição geral
"-- New --"
"-- Still in the testing process --"
"facebook/convnext-large-224"
"timm/resnet50.a1_in1k"
"timm/mobilenetv3_large_100.ra_in1k"
"trpakov/vit-face-expression"
"rizvandwiki/gender-classification"
"#q-future/one-align"
"LukeJacob2023/nsfw-image-detector"
"vit-base-patch16-224-in21k"
"not-lain/deepfake"
"carbon225/vit-base-patch16-224-hentai"
"facebook/convnext-base-224-22k-1k"
"facebook/convnext-large-224"
"facebook/convnext-tiny-224"
"nvidia/mit-b0"
"microsoft/resnet-18"
"microsoft/swinv2-base-patch4-window16-256"
"andupets/real-estate-image-classification"
"timm/tf_efficientnetv2_s.in21k"
"timm/convnext_tiny.fb_in22k"
"DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition"
"FatihC/swin-tiny-patch4-window7-224-finetuned-eurosat-watermark"
"aalonso-developer/vit-base-patch16-224-in21k-clothing-classifier"
"RickyIG/emotion_face_image_classification"
"shadowlilac/aesthetic-shadow"
]
def classify(image, model):
classifier = pipeline("image-classification", model=model)
result= classifier(image)
return result
def save_result(result):
st.write("In the future, this function will save the result in a database.")
def print_result(result):
comulative_discarded_score = 0
for i in range(len(result)):
if result[i]['score'] < MIN_ACEPTABLE_SCORE:
comulative_discarded_score += result[i]['score']
else:
st.write(result[i]['label'])
st.progress(result[i]['score'])
st.write(result[i]['score'])
st.write(f"comulative_discarded_score:")
st.progress(comulative_discarded_score)
st.write(comulative_discarded_score)
def main():
st.title("Image Classification")
st.write("This is a simple web app to test and compare different image classifier models using Hugging Face's image-classification pipeline.")
st.write("From time to time more models will be added to the list. If you want to add a model, please open an issue on the GitHub repository.")
st.write("The models available are:")
shosen_model = st.selectbox("Select the model to use", MODELS)
st.write("Upload an image and click on the 'Classify' button to classify the image.")
input_image = st.file_uploader("Upload Image")
if input_image is not None:
image_to_classify = Image.open(input_image)
st.image(image_to_classify, caption="Uploaded Image", use_column_width=True)
if st.button("Classify"):
image_to_classify = Image.open(input_image)
classification_obj1 =[]
avable_models = st.selectbox
classification_result = classify(image_to_classify, shosen_model)
classification_obj1.append(classification_result)
print_result(classification_result)
save_result(classification_result)
if __name__ == "__main__":
main() |