app.py

import numpy as np
import gradio as gr
import onnxruntime as ort
from matplotlib import pyplot as plt
from huggingface_hub import hf_hub_download

model = hf_hub_download(repo_id="matjesg/cFOS_in_HC", filename="ensemble.onnx")

def create_model_for_provider(model_path, provider="CPUExecutionProvider"): 
    options = ort.SessionOptions()
    options.intra_op_num_threads = 1
    options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
    session = ort.InferenceSession(str(model_path), options, providers=[provider])
    session.disable_fallback()
    return session

ort_session = create_model_for_provider(model)

def inference(img):
    
    img = img[...,:1]/255
    
    ort_inputs = {ort_session.get_inputs()[0].name: img.astype(np.float32)}

    ort_outs = ort_session.run(None, ort_inputs)
    
    return ort_outs[0]*255

  
title="deepflash2"
description="deepflash2 is a deep-learning pipeline for segmentation of ambiguous microscopic images."
examples=[['1599.tif']]

gr.Interface(inference,
             gr.inputs.Image(type="numpy"),
             gr.outputs.Image(),
             title=title,
             description=description,
             examples=examples
             ).launch(share=True)