import gradio as gr
from transformers import pipeline

model_names = [
    "apple/mobilevit-small",
    "facebook/deit-base-patch16-224",
    "facebook/convnext-base-224",
    "google/vit-base-patch16-224",
    "google/mobilenet_v2_1.4_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):
    labels = []
    for m in model_names:
        p = pipeline("image-classification", model=m)
        pred = p(image_file)
        labels.append({x["label"]: x["score"] for x in pred[:top_k]})
    return labels


# Inputs
image = gr.Image(type="filepath", label="Upload an image")
top_k = gr.Slider(minimum=1, maximum=5, step=1, value=5, label="Top k classes")

# Output
labels = [gr.Label(label=m) for m in model_names]

description = "This Space lets you quickly compare the most popular image classifiers available on the hub, including the recent NAT and DINAT models. 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,
    layout="horizontal",
    fn=process,
    inputs=[image, top_k],
    outputs=labels,
    examples=[
        ["bike.jpg", 5],
        ["car.jpg", 5],
        ["food.jpg", 5],
    ],
    allow_flagging="never",
)

iface.launch()