Spaces:
Sleeping
Sleeping
File size: 1,723 Bytes
c8f645f b16e0f5 c8f645f 24798c6 c8f645f 24798c6 c8f645f 24798c6 c8f645f c26df0d c8f645f b16e0f5 c8f645f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
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()
|