app.py

from transformers import pipeline
from PIL import Image
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()
    classifier = pipeline("image-classification", model="bazyl/gtsrb-model")
    result = classifier(img, top_k=3)
    response = {result[i]["label"]: result[i]["score"] for i in range(len(result))}
    pred_time = round(timer() - start_time, 5)
    return response, pred_time

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=3, label="Predictions"),
        gr.Number(label="Prediction time (s)"),
    ],
    examples=example_list,
    title=title,
    description=description,
)

# Launch the app!
demo.launch()