first commit

app.py

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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
+
+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() 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")
+        with gr.Column():
+            output = gr.Label()
+    button.click(clasificador,inputt,output)
+    
+demo.launch()

requirements.txt

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+gradio
+transformers