import gradio as gr

from huggingface_hub import HfApi, hf_hub_download
from transformers import model

def get_model_ids():
    api = HfApi()
    models = api.list_models(filter="llama-leaderboard")
    model_ids = [x.modelId for x in models]
    return model_ids
    
models = {}
for model_id in get_model_ids():
  models[model_id] = pipeline("image-classification", model=model_id)
    
def predict(img, model_id):
  return models[model_id](img)
  
gr.Interface(
  fn=predict, 
   inputs=[
     gr.inputs.Image(type="pil"),
     gradio.inputs.Dropdown(get_model_ids())
   ]
   outputs=gr.outputs.Label(num_top_classes=3),
   examples=["llama.jpg", "potato.jpg"]
).launch()