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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-en-es")
def translate_text(text):
text = es_en_translator(text)[0].get("translation_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()
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