Image generator added

app.py

@@ -1,7 +1,38 @@
 import streamlit as st
 
-st.title('This is my demo')
-st.markdown('This is my description')
+from utils import load_model, generate
 
-x = st.slider('Selecciona un valor')
-st.write(x, 'el cuadrado es ', x * x)
+st.title('Image Generator with GAN')
+st.markdown('Butterflies generator')
+
+# Sidebar
+st.sidebar.subheader('This butterfly does not exist, it is completely generated!')
+st.sidebar.image('assets/logo.png', width=200)
+st.sidebar.caption('Demo was just created.')
+
+# Loading model
+repo_id = 'ceyda/butterfly_cropped_uniq1K_512'
+gan_model = load_model(repo_id)
+
+# Generate 4 butterflies
+n_butterflies = 4
+
+def run():
+    with st.spinner('Generating...'):
+        ims = generate(gan_model, n_butterflies)
+        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(
+    'Generate butterflies',
+    on_click=run,
+    help='We are about to start to generate'
+)
+
+if ims is not None:
+    cols = st.columns(n_butterflies)

utils.py

@@ -0,0 +1,14 @@
+import numpy as np
+import torch 
+from huggan.pytorch.lightweight_gan.lighweight_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).detach().cpu().numpy().astype(np.uint8)
+    return ims