Add some generation

app.py

@@ -1,7 +1,38 @@
 import streamlit as st
 
-st.title("This is a demo")
-st.markdown("This is a description")
-x = st.slider("Put a number")
+from utils import generation,load_model
 
-st.write(x, "The square is", x*x)
+## Site
+st.title("Gen of butterfly")
+st.markdown("This is lightweight_gan")
+
+# Sidebar
+
+st.sidebar.subheader("This is generated")
+st.sidebar.image("assets/logo.png", width=200)
+st.sidebar.caption("https://wgcv.me")
+
+# Values
+model_id="ceyda/butterfly_cropped_uniq1K_512"
+model = load_model(model_id)
+n_gen = 16
+
+def run():
+    with st.spinner("Loading the model"):
+
+        ims = generation(model,batch_size=n_gen)
+        st.session_state["ims"] = ims
+
+if("ims" not in st.session_state):
+    st.session_state["ims"] = None
+    run()
+
+
+ims = st.session_state["ims"]
+run_button = st.button("Gen AI butterfly", on_click=run,help="This would run the model")
+
+if(ims is not None):
+    cols = st.columns(n_gen)
+    for j,im in enumerate(ims):
+        i = j % n_gen
+        cols[i].image(im, use_column_width=True)

utils.py

@@ -0,0 +1,17 @@
+import streamlit as st
+
+import numpy as np
+import torch
+
+from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN
+
+def load_model(model_name="ceyda/butterfly_cropped_uniq1K_512", model_version=None):
+    gan = LightweightGAN.from_pretrained(model_name, version=model_version)
+    gan.eval()
+    return gan
+
+def generation(gan, batch_size=1):
+    with torch.no_grad():
+        ims = gan.G(torch.randn(batch_size, gan.latent_dim)).clamp_(0.0, 1.0) * 255
+        ims = ims.permute(0, 2, 3, 1).detach().cpu().numpy().astype(np.uint8)
+    return ims

Ω

@@ -0,0 +1 @@
+