initial commit

app.py

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+import gradio as gr
+import numpy as np
+import torch
+from torchvision.utils import make_grid
+from utils import (
+    ConditionalVariationalAutoEncoder,
+    classes,
+    species,
+    genders
+)
+
+N = 16
+
+cvae = ConditionalVariationalAutoEncoder()
+cvae.load_state_dict(torch.load('cvae.pth'))
+
+def greet(class_, species_, gender_):
+    c_i = classes.index(class_)
+    s_i = species.index(species_)
+    g_i = genders.index(gender_)
+
+    c_ = torch.LongTensor([c_i] * N)
+    s_ = torch.LongTensor([s_i] * N)
+    g_ = torch.LongTensor([g_i] * N)
+
+    latent = torch.randn(N, 1024)
+    imgs = cvae.dec(latent, s_, c_, g_)
+
+    Z = make_grid(imgs, nrow=4)
+    
+    if Z.min() < 1:
+        Z = (Z + 1) / 2
+    
+    Z = np.transpose(Z.detach().cpu().numpy(), (1, 2, 0))    
+    return Z
+
+demo = gr.Interface(
+    fn=greet,
+    inputs=[
+        gr.Dropdown(classes),
+        gr.Dropdown(species),
+        gr.Dropdown(genders)
+    ],
+    outputs="image",
+    live=True
+)
+
+demo.launch(share=True)