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''' ----------------------------------------
* Creation Time : Sun Aug 28 21:38:58 2022
* Last Modified : Sun Aug 28 21:41:36 2022
* Author : Charles N. Christensen
* Github : github.com/charlesnchr
----------------------------------------'''
from turtle import title
import gradio as gr
import numpy as np
from PIL import Image
import io
import base64
from NNfunctions import *
opt = GetOptions_allRnd_0317()
net = LoadModel(opt)
def predict(image):
img = np.array(image)
img = np.concatenate((img,img,img),axis=2)
img = np.transpose(img, (2,0,1))
# sr,wf,out = EvaluateModel(net,opt,img,outfile)
sr_img = EvaluateModel(net,opt,img)
return sr_img
title = '<h1 style="text-align: center;">ML-SIM: Reconstruction of SIM images with deep learning</h1>'
description = """
## About
This space demonstrates the use of a semantic segmentation model to segment pets and classify them
according to the pixels.
## 🚀 To run
Upload a pet image and hit submit or select one from the given examples
"""
inputs = gr.inputs.Image(label="Upload a TIFF image", type = 'pil', optional=False)
outputs = [
gr.outputs.Image(label="SIM Reconstruction")
# , gr.outputs.Textbox(type="auto",label="Pet Prediction")
]
examples = [
"./examples/dogcat.jpeg",
]
interface = gr.Interface(fn=predict,
inputs=inputs,
outputs=outputs,
title = title,
description=description,
examples=examples
)
interface.launch()
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