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| import re | |
| import streamlit as st # HF spaces at v1.2.0 | |
| 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.write("Demo prep still in progress!!") | |
| def load_model_intocache(model_name): | |
| # model_name='ceyda/butterfly_512_base' | |
| gan = load_model(model_name) | |
| return gan | |
| def load_dataset(): | |
| dataset=get_dataset() | |
| return dataset | |
| 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) | |
| 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) | |