app.py

from transformers import pipeline
import gradio as gr
import base64
import io

# calling pipeline to get_completion
get_completion = pipeline("image-classification",model="carolcarneiro/keras-dummy-sequential-demo")

def classify_image(image):
    result = get_completion(image)
    return result[0]['label']

# Criando a interface do Gradio
demo = gr.Interface(fn=classify_image,
                    inputs=gr.Image(label="Upload image", type="pil"),
                    outputs=gr.Textbox(label="Classification"),
                    title="Fashion MNIST Classification",
                    description="Classify the uploaded image as one of the Fashion MNIST categories",
                    allow_flagging="never")

# Iniciando a aplicação
demo.launch()