add logo and menus

app.py

@@ -4,22 +4,19 @@ from demo import load_model,generate,get_dataset,embed
 
 # TODOs
 # Add markdown short readme project intro
-# project setup:
-
-# git clone https://github.com/huggingface/community-events.git
-# cd community-events
-# pip install .
- 
-st.title("ButterflyGAN")
-st.write("## This butterfly does not exist! ")
+
+
+st.sidebar.image("assets/logo.png", use_column_width=True)
+st.header("ButterflyGAN")
+st.caption("This butterfly does not exist! ")
 st.write("Demo prep still in progress!!")
 
+
 @st.experimental_singleton
 def load_model_intocache(model_name):
     
     # model_name='ceyda/butterfly_512_base'
     gan = load_model(model_name)
-
     return gan
 
 @st.experimental_singleton
@@ -31,37 +28,51 @@ model_name='ceyda/butterfly_cropped_uniq1K_512'
 model=load_model_intocache(model_name)
 dataset=load_dataset()
 
-st.write(f"Model {model_name} is loaded")
-st.write(f"Latent dimension: {model.latent_dim}, Image size:{model.image_size}")
-
-if 'ims' not in st.session_state:
-    st.session_state['ims'] = None
-
-ims=st.session_state["ims"]
-batch_size=4 #generate 4 butterflies 
-def run():
-    with st.spinner("Generating..."):
-        ims=generate(model,batch_size)
-        st.session_state['ims'] = ims
-
-runb=st.button("Generate", on_click=run)
-if ims is not None:
-    cols=st.columns(batch_size)
-    picks=[False]*batch_size
-    for i,im in enumerate(ims):
-        cols[i].image(im)
-        picks[i]=cols[i].button("Find Nearest",key="pick_"+str(i))
-        # if picks[i]:
-        #     scores, retrieved_examples=dataset.get_nearest_examples('beit_embeddings', embed(im), k=5)
-        #     for r in retrieved_examples["image"]:
-        #         st.image(r)
+screen = st.sidebar.radio("Pick a destination",["Make butterflies","Take a latent walk", "See the data mosaic"])
+
+if screen == "Make butterflies":
+
     
-    if any(picks):
-        # st.write("Nearest butterflies:")
-        for i,pick in enumerate(picks):
-            if pick:
-                scores, retrieved_examples=dataset.get_nearest_examples('beit_embeddings', embed(ims[i]), k=5)
-                for r in retrieved_examples["image"]:
-                    cols[i].image(r)
+    
+    if 'ims' not in st.session_state:
+        st.session_state['ims'] = None
+
+    ims=st.session_state["ims"]
+    batch_size=4 #generate 4 butterflies 
+    def run():
+        with st.spinner("Generating..."):
+            ims=generate(model,batch_size)
+            st.session_state['ims'] = ims
+
+    runb=st.button("Generate", on_click=run)
+    if ims is not None:
+        cols=st.columns(batch_size)
+        picks=[False]*batch_size
+        for i,im in enumerate(ims):
+            cols[i].image(im)
+            picks[i]=cols[i].button("Find Nearest",key="pick_"+str(i))
+            # if picks[i]:
+            #     scores, retrieved_examples=dataset.get_nearest_examples('beit_embeddings', embed(im), k=5)
+            #     for r in retrieved_examples["image"]:
+            #         st.image(r)
+        
+        if any(picks):
+            # st.write("Nearest butterflies:")
+            for i,pick in enumerate(picks):
+                if pick:
+                    scores, retrieved_examples=dataset.get_nearest_examples('beit_embeddings', embed(ims[i]), k=5)
+                    for r in retrieved_examples["image"]:
+                        cols[i].image(r)
+
+    st.write(f"Latent dimension: {model.latent_dim}, Image size:{model.image_size}")
+
+elif screen == "Take a latent walk":
+    st.write("Take a latent walk")
+
+elif screen == "Input data mosaic":
+    st.markdown("Todo add explanation about data")
+    st.image("assets/training_data_lowres.png")
 
 
+# footer stuff
+st.sidebar.info(f"Model {model_name} is loaded")