app.py

from transformers import ViTFeatureExtractor, ViTForImageClassification
import gradio as gr
from datasets import load_dataset
import torch

dataset = load_dataset("cifar100")
image = dataset["train"]["fine_label"]

def classify(image):
    feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
    model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
    inputs = feature_extractor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
    # model predicts one of the 1000 ImageNet classes
    predicted_class_idx = logits.argmax(-1).item()
    return model.config.id2label[predicted_class_idx]

def image2speech(image):
    txt = classify(image)
    return fastspeech(txt), txt

fastspeech = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech")

app = gr.Interface(fn=image2speech,
                   inputs="image",
                   title="Image to speech",
                   description="Classifies and image and tell you what is it, intended to help the visually impaired",
                   examples=["remotecontrol.jpg", "calculator.jpg", "cellphone.jpg"],
                   allow_flagging="never",
                   outputs=["audio", "text"])

app.launch(cache_examples=True)