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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 | |
| "-- Repescados --", | |
| "timm/vit_large_patch14_clip_224.openai_ft_in12k_in1k", #Classifição geral | |
| "-- New --", | |
| ] | |
| 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") | |
| 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", 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() |