import gradio as gr
import torch
from huggingface_hub import from_pretrained_fastai
from pathlib import Path

examples = ["llama.jpg"]
repo_id = "osanseviero/is_it_a_llama"
path = Path("./")

def get_y(r):
    return r["label"]
    
def get_x(r):
    return path/r["fname"]
    
learner = from_pretrained_fastai(repo_id)
labels = learner.dls.vocab

def inference(image):
    label_predict,_,probs = learner.predict(image)
    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
    return labels_probs

gr.Interface(
    fn=inference,
    title="Llama image classification",
    description = "Predict if this image has a llama (or a forest)",
    inputs="image",
    outputs="label",
    examples=examples,
).launch(debug=True, enable_queue=True)