app.py

import gradio as gr
from pathlib import Path
from transformers import pipeline
from PIL import Image
import os, os.path

pipeline = pipeline("image-classification", model="Ant-Onion/dummy-model", feature_extractor = "Ant-Onion/dummy-model")



imgs = []
path = "./images"

lista = os.listdir(path)
examples =list(map(lambda x: os.path.join(path,x), lista))

def predict(image):
  predictions = pipeline(image)
  return {p["label"]: p["score"] for p in predictions}

gr.Interface(
    predict,
    inputs=gr.inputs.Image(label="Upload THE BIRDOOOOO", type="filepath"),
    outputs=gr.outputs.Label(num_top_classes=2),
    title="CUTE BIRD OR UGRLY BORD",
    examples=examples,
).launch()