add new version

README.md

@@ -4,6 +4,6 @@ emoji: 🐴
 colorFrom: yellow
 colorTo: orange
 sdk: gradio
-app_file: cyclegan_inference.py
+app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,25 @@
+from fastai.vision.all import *
+from fastai.basics import *
+from upit.models.cyclegan import *
+from upit.train.cyclegan import *
+from upit.data.unpaired import *
+import torchvision
+import gradio as gr
+
+dls = get_dls_from_hf("huggan/horse2zebra", load_size=286)
+cycle_gan = CycleGAN.from_pretrained('tmabraham/horse2zebra_cyclegan')
+
+totensor = torchvision.transforms.ToTensor()
+normalize_fn = torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+topilimage = torchvision.transforms.ToPILImage()
+
+model = cycle_gan.G_B.cpu().eval()
+def predict(input):
+    im = normalize_fn(totensor(input))
+    print(im.shape)
+    preds = model(im.unsqueeze(0))/2 + 0.5
+    print(preds.shape)
+    return topilimage(preds.squeeze(0).detach())
+
+gr_interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(256, 256)), outputs="image", title='Horse-to-Zebra CycleGAN with UPIT', description='[This](https://huggingface.co/tmabraham/horse2zebra_cyclegan) CycleGAN model trained on [this dataset](https://huggingface.co/datasets/huggan/horse2zebra), using the [UPIT package](https://github.com/tmabraham/UPIT)', examples=['horse.jpg'])
+gr_interface.launch(inline=False,share=False)

requirements.txt

@@ -1,2 +1,3 @@
-torch
+upit
+gradio
 torchvision