import gradio as gr
from fastai.vision.all import *
import skimage
import pathlib

plt = platform.system()
if plt == 'Linux' : pathlib.WindowsPath = pathlib.PosixPath

learn = load_learner('xres18.pkl')

labels = learn.dls.vocab
def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return {labels[i] : float(probs[i]) for i in range(len(labels))}

title = 'Cell Identifier'
description = 'A cell identifier detector trained on custom dataset with fastai. Created using Gradio and HuggingFace Spaces.'
examples = []
enable_queue = True

gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512,512)), outputs=gr.outputs.Label(num_top_classes=3), title=title, description=description, examples=examples, enable_queue=enable_queue).launch(share=True)