feat: app.py and utils.py

app.py

@@ -0,0 +1,43 @@
+import streamlit as st
+
+from utils import load_model, generate
+
+## Main page
+st.title("Butterfly generator")
+st.write("Modelo Light GAN")
+
+## Lateral bar
+st.sidebar.subheader("No existing butterfly")
+st.sidebar.image("assets/logo.png", width=200)
+st.sidebar.caption("In live demo")
+
+## Loading the model
+repo_id = "ceyda/butterfly_croped_uniq1K_512"
+model_gan = load_model(repo_id)
+
+## Generate 4 butterflies
+n_butterflies = 4
+
+
+def execute():
+    with st.spinner("Generating..."):
+        ims = generate(model_gan, n_butterflies)
+        st.session_state["ims"] = ims
+
+if "ims" not in st.session_state:
+    st.session_state["ims"] = None
+    execute()
+
+ims = st.session_state["ims"]
+
+exec_button = st.button(
+    "Generate!",
+    on_click=execute,
+    help="Help message"
+)
+
+if ims is not None:
+    cols = st.columns(n_butterflies)
+    for j, im in enumerate(ims):
+        i = j % n_butterflies
+        cols[i].image(im, use_column_width=True)

utils.py

@@ -0,0 +1,14 @@
+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 generate(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).deatch().cpu().numpy().asType(np.uint8)
+    return ims