Upload app.py

app.py

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+"""
+hugging face app.py file
+"""
+
+
+#import functions
+
+import matplotlib.pylab as plt
+import PIL.Image as Image
+import gradio as gr
+import torch
+import torchvision
+from torch import nn
+from einops import rearrange, reduce
+from pytorch_lightning import LightningModule, Trainer, Callback
+from pytorch_lightning.loggers import WandbLogger
+from torch.optim import Adam
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from denoiseCIFAR100 import DenoiseCIFAR100Model
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+
+
+
+modelcheck = DenoiseCIFAR100Model.load_from_checkpoint("./autoencoder_model.ckpt")
+modelcheck=modelcheck.eval()
+
+def denoise(image):
+  in_im=tinp = transforms.ToTensor()(image).unsqueeze(0)
+  with torch.no_grad():
+      out_im = modelcheck(in_im)
+
+  out = out_im[0].permute(1, 2, 0)
+  out = out.numpy()
+  im = Image.fromarray((out * 255).astype(np.uint8))
+  im.save("./output.jpeg")
+  return "./output.jpeg"
+  
+
+
+iface = gr.Interface(denoise, inputs=gr.inputs.Image(shape=(32,32), image_mode="RGB", type="numpy",label="Input"),
+             outputs=gr.outputs.Image(type="file",label="Output"),
+            examples=["panda.jpeg","outside.jpeg"],
+            live=False,layout="horizontal", interpretation=None,
+            title="CIFAR100 Denoising",
+            #description=description,
+            #article=article
+            )
+iface.launch()