app.py

from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import requests
import os 
import gradio as gr
from timeit import default_timer as timer
from typing import Tuple, Dict


def predict(img) -> Tuple[Dict, float]:
    start_time = timer()
    processor = ViTImageProcessor.from_pretrained('bazyl/gtsrb-model')
    model = ViTForImageClassification.from_pretrained('bazyl/gtsrb-model')
    inputs = processor(images=img, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_idx = logits.argmax(-1).item()
    print("Predicted class:", model.config.id2label[predicted_class_idx])

title = "GTSRB - German Traffic Sign Recognition by Bazyl Horsey"
description = "CNN created for the GTSRB Dataset, achieved 99.93% test accuracy"

# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]

# Create Gradio interface 
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Label(num_top_classes=5, label="Predictions"),
        gr.Number(label="Prediction time (s)"),
    ],
    examples=example_list,
    title=title,
    description=description,
)

# Launch the app!
demo.launch()