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| import gradio as gr | |
| # processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224') | |
| # model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') | |
| from transformers import pipeline | |
| import base64 | |
| import os | |
| with open("Iso_Logotipo_Ceibal.png", "rb") as image_file: | |
| encoded_image = base64.b64encode(image_file.read()).decode() | |
| classifier = pipeline(model="google/vit-base-patch16-224") | |
| # classifier("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png") | |
| def clasificador(image): | |
| results = classifier(image) | |
| result = {} | |
| for item in results: | |
| result[translate_text(item['label'])] = item['score'] | |
| return result | |
| es_en_translator = pipeline("translation",model = "Helsinki-NLP/opus-mt-es-en") | |
| def translate_text(text): | |
| print(text) | |
| text = es_en_translator(text)[0].get("translation_text") | |
| print(text) | |
| return text | |
| with gr.Blocks(title = "Uso de AI para la clasificación de imágenes.") as demo: | |
| gr.Markdown(""" | |
| <center> | |
| <h1> | |
| Uso de AI para la clasificación de imágenes. | |
| </h1> | |
| <img src='data:image/jpg;base64,{}' width=200px> | |
| <h3> | |
| Con este espacio podrás clasificar imágenes y objetos a partir de una imagen. | |
| </h3> | |
| </center> | |
| """.format(encoded_image)) | |
| with gr.Row(): | |
| with gr.Column(): | |
| inputt = gr.Image(type="pil", label="Ingresá la imagen a clasificar.") | |
| button = gr.Button(value="Clasificar") | |
| examples = gr.Examples(examples=[os.path.join(os.path.dirname(__file__), "palacio.jpeg")],inputs=[inputt]) | |
| with gr.Column(): | |
| output = gr.Label() | |
| button.click(clasificador,inputt,output) | |
| demo.launch() | |