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", #Classifição geral "timm/resnet50.a1_in1k", #Classifição geral "timm/mobilenetv3_large_100.ra_in1k", #Classifição geral "trpakov/vit-face-expression", #Classifição de expressão facial "rizvandwiki/gender-classification", #Classifição de gênero "#q-future/one-align", #Classifição geral "LukeJacob2023/nsfw-image-detector", #Classifição NSFW "vit-base-patch16-224-in21k", #Classifição geral "not-lain/deepfake", #Classifição deepfake "carbon225/vit-base-patch16-224-hentai", #Classifição hentai "facebook/convnext-base-224-22k-1k", #Classifição geral "facebook/convnext-large-224", #Classifição geral "facebook/convnext-tiny-224",#Classifição geral "nvidia/mit-b0", #Classifição geral "microsoft/resnet-18", #Classifição geral "microsoft/swinv2-base-patch4-window16-256", #Classifição geral "andupets/real-estate-image-classification", #Classifição de imóveis "timm/tf_efficientnetv2_s.in21k", #Classifição geral "timm/convnext_tiny.fb_in22k", "DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition", #Classifição de atividade humana "FatihC/swin-tiny-patch4-window7-224-finetuned-eurosat-watermark", #Classifição geral "aalonso-developer/vit-base-patch16-224-in21k-clothing-classifier", #Classifição de roupas "RickyIG/emotion_face_image_classification", #Classifição de emoções "shadowlilac/aesthetic-shadow" #Classifição de estética ] 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("If you like this project, please consider liking it or buying me a coffee. It will help me to keep working on this and other projects. Thank you!") # Buy me a Coffee Setup bmc_link = "https://www.buymeacoffee.com/nuno.tome" # image_url = "https://helloimjessa.files.wordpress.com/2021/06/bmc-button.png?w=150" # Image URL image_url = "https://i.giphy.com/RETzc1mj7HpZPuNf3e.webp" # Image URL image_size = "150px" # Image size #image_link_markdown = f"" image_link_markdown = f"[![Buy Me a Coffee]({image_url})]({bmc_link})" #image_link_markdown = f"[![Buy Me a Coffee]({image_url})]({bmc_link})" # Create a clickable image link st.markdown(image_link_markdown, unsafe_allow_html=True) # Display the image link # Buy me a Coffee Setup #st.markdown("", unsafe_allow_html=True) input_image = st.file_uploader("Upload Image") shosen_model = st.selectbox("Select the model to use", MODELS) if input_image is not None: image_to_classify = Image.open(input_image) st.image(image_to_classify, caption="Uploaded Image") 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()