app.py

import gradio as gr
from transformers import pipeline,WhisperProcessor, WhisperForConditionalGeneration
import torch
import librosa
import datasets
from transformers.pipelines.pt_utils import KeyDataset
from tqdm.auto import tqdm

image_to_text_model = pipeline("image-classification",model="microsoft/beit-base-patch16-224-pt22k-ft22k")


def image_to_text(input_image):
    # Convertir la imagen a texto
    text_output = image_to_text_model(input_image)[0]['label']
    print(text_output)
    #texts = transcriber(text_output)
    return text_output

gr.Interface.from_pipeline(pipe, 
                           title="22k Image Classification",
                           description="Object Recognition using Microsoft BEIT",
                           examples = [],
                           article = "Author: <a href=\"https://huggingface.co/rowel\">Rowel Atienza</a>",
                           ).launch()