import gradio as gr
from transformers import pipeline

model_names = [
    "facebook/deit-base-patch16-224",
    "facebook/convnext-base-224",
    "google/vit-base-patch16-224",
    "microsoft/resnet-50",
    "microsoft/swin-base-patch4-window7-224",
    "microsoft/beit-base-patch16-224",
    "nvidia/mit-b0",
    "shi-labs/nat-base-in1k-224",
    "shi-labs/dinat-base-in1k-224"
]


def process(image_file, top_k, model_name):
    p = pipeline("image-classification", model=model_name)
    pred = p(image_file)
    return {x["label"]: x["score"] for x in pred[:top_k]}

# Inputs
image = gr.Image(type="filepath", label="Upload an image")
top_k = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Top k classes")
model_selection = gr.Dropdown(model_names, label="Pick a model")

# Output
labels = gr.Label()

description = "This Space lets you quickly compare the most popular image classifier models available on the hub. All of them have been fine-tuned on the ImageNet-1k dataset. Anecdotally, the three sample images have been generated with a Stable Diffusion model :)"

iface = gr.Interface(
    theme="huggingface",
    description=description,
    fn=process,
    inputs=[image, top_k, model_selection],
    outputs=[labels],
    examples=[
	["bike.jpg", 5, "google/vit-base-patch16-224"],
	["car.jpg", 5, "microsoft/swin-base-patch4-window7-224"],
	["food.jpg", 5, "facebook/convnext-base-224"]
    ],
    allow_flagging="never",
)

iface.launch()