app.py

# from distutils.command.build import build
import streamlit as st # HF spaces at v1.2.0
# from utils import load_model,generate,get_dataset,embed,make_meme
from utils import load_model,generate
# import streamlit.components.v1 as components
# import io
# import os

# root_dir=os.path.dirname(os.path.abspath(__file__))
# build_dir = os.path.join(root_dir, "custom_component/frontend/build")
# _component_func = components.declare_component("release_butterflies", path=build_dir)
# def release_butterflies(name, key=None):
#     component_value = _component_func(name=name, key=key, default=0)
#     return component_value


## Configuración de nuestro demo
st.title("ButterflyGAN")
st.write("Light-GAN model trained on 1000 butterfly images taken from the Smithsonian Museum collection. \n \
Based on [paper:](https://openreview.net/forum?id=1Fqg133qRaI) *Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis*")

st.sidebar.subheader("This butterfly does not exist! ")
st.sidebar.image("assets/logo.png", width=200)
st.sidebar.caption(f"[Model](https://huggingface.co/ceyda/butterfly_cropped_uniq1K_512) & [Dataset](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset) used")
st.sidebar.caption(f"Made during the [huggan](https://github.com/huggingface/community-events) hackathon")
st.sidebar.caption(f"Contributors:")
st.sidebar.caption(f"[Ceyda Cinarel](https://github.com/cceyda) & [Jonathan Whitaker](https://datasciencecastnet.home.blog/)")


## Cargamos modelo
repo_id = 'ceyda/butterfly_cropped_uniq1K_512'
version_modelo ='57d36a15546909557d9f967f47713236c8288838'
modelo_gan = load_model(repo_id, version_modelo)

# @st.experimental_singleton
# def load_model_intocache(model_name,model_version):
#     # model_name='ceyda/butterfly_512_base'
#     gan = load_model(model_name,model_version)
#     return gan

# @st.experimental_singleton
# def load_dataset():
#     dataset=get_dataset()
#     return dataset

# @st.experimental_singleton
# def load_variables():# Don't want to open read files over and over. not sure if it makes a diff
#     latent_walk_code=open("assets/code_snippets/latent_walk.py").read()
#     latent_walk_code_music=open("assets/code_snippets/latent_walk_music.py").read()
#     return latent_walk_code,latent_walk_code_music

# def img2download(image):
#         imgByteArr = io.BytesIO()
#         image.save(imgByteArr, format="JPEG")
#         imgByteArr = imgByteArr.getvalue()
#         return imgByteArr

# model_name='ceyda/butterfly_cropped_uniq1K_512'
# model_version='57d36a15546909557d9f967f47713236c8288838'
# model_version=None


# model=load_model_intocache(model_name,model_version)
# dataset=loadk_dataset()
# latent_walk_code, latent_walk_code_music=load_variables()

# generate_menu="🦋 Make butterflies"
# latent_walk_menu="🎧 Take a latent walk"
# make_meme_menu="🐦 Make a meme"
# mosaic_menu="👀 See the mosaic"
# fun_menu="🙌 Release the butterflies"

# screen = st.sidebar.radio("Pick a destination",[generate_menu,latent_walk_menu,make_meme_menu,mosaic_menu,fun_menu])

## Generamos 4 mariposas
n_mariposas =4 

## Función que genera mariposas y lo guarda como un estado de la sesión
def corre():
    with st.spinner("Generando, espera un poco..."):
        ims=generate(modelo_gan,n_mariposas)
        st.session_state['ims'] = ims

## Si no hay una imagen generada entonces generala
if 'ims' not in st.session_state:
    st.session_state['ims'] = None
    corre()

## ims contiene las imágenes generadas
ims=st.session_state["ims"]

## Si la usuaria da click en el botón entonces corremos la función genera()
runb=st.button("Genera mariposas por favor", on_click=corre ,help="generated on the fly maybe slow")

if ims is not None:
    cols=st.columns(n_mariposas)
    # picks=[False]*n_mariposas
    for j,im in enumerate(ims):
        i=j%n_mariposas
        cols[i].image(im, use_column_width=True)
        # picks[j]=cols[i].button("Find Nearest",key="pick_"+str(j))
        
    # 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, use_column_width=True)
# st.write("Nearest neighbors found in the training set according to L2 distance on 'microsoft/beit-base-patch16-224' embeddings")
# st.write(f"Latent dimension: {model.latent_dim}, image size:{model.image_size}")

# elif screen == latent_walk_menu:

#     st.write("Take a latent walk :musical_note: with cute butterflies")

#     cols=st.columns(3)

#     cols[0].caption("A regular walk (no music)")
#     cols[0].video("assets/latent_walks/regular_walk.mp4")
    
#     cols[1].caption("Walk with music :butterfly:")
#     cols[1].video("assets/latent_walks/walk_happyrock.mp4")
#     cols[2].caption("Walk with music :butterfly:")
#     cols[2].video("assets/latent_walks/walk_cute.mp4")
    
#     st.caption("Royalty Free Music from Bensound")
#     st.write("🎧Did those butterflies seem to be dancing to the music?!Here is the secret:")
#     with st.expander("See the Code Snippets"):
#         st.write("A regular latent walk:")
#         st.code(latent_walk_code, language='python')
#         st.write(":musical_note: latent walk with music:")
#         st.code(latent_walk_code_music, language='python')


# elif screen == make_meme_menu:
#     if "pigeon" not in st.session_state:
#         st.session_state['pigeon'] = generate(model,1)[0]

#     def get_pigeon():
#         st.session_state['pigeon'] = generate(model,1)[0]
    
#     cols= st.columns(2)
#     cols[0].button("change pigeon",on_click=get_pigeon)
#     no_bg=cols[1].checkbox("Remove background?",True,help="Remove the background from pigeon")
#     show_text=cols[1].checkbox("Show text?",True)
    
#     meme_text=st.text_input("Enter text","Is this a pigeon?")
   
    
#     meme=make_meme(st.session_state['pigeon'],text=meme_text,show_text=show_text,remove_background=no_bg)
#     st.image(meme)
#     coly=st.columns(2)
#     coly[0].download_button("Download", img2download(meme),mime="image/jpeg")
#     coly[1].write("Made a cool one? [Share](https://twitter.com/intent/tweet?text=Check%20out%20the%20demo%20for%20Butterfly%20GAN%20%F0%9F%A6%8Bhttps%3A//huggingface.co/spaces/huggan/butterfly-gan%0Amade%20by%20%40ceyda_cinarel%20%26%20%40johnowhitaker%20) on Twitter")


# elif screen == mosaic_menu:
#     cols=st.columns(2)
#     cols[0].markdown("These are all the butterflies in our [training set](https://huggingface.co/huggan/smithsonian_butterflies_subset)")
#     cols[0].image("assets/train_data_mosaic_lowres.jpg")
#     cols[0].write("🔎 view the high-res version [here](https://www.easyzoom.com/imageaccess/0c77e0e716f14ea7bc235447e5a4c397)")

#     cols[1].markdown("These are the butterflies our model generated.")
#     cols[1].image("assets/gen_mosaic_lowres.jpg")
#     cols[1].write("🔎 view the high-res version [here](https://www.easyzoom.com/imageaccess/cbb04e81106c4c54a9d9f9dbfb236eab)")

# elif screen == fun_menu:
    
#     cols=st.columns([1,2])
#     cols[0].write("While working on this project")
#     cols[0].image("assets/butterflies_everywhere.jpg")
    
#     with cols[1]:
#         release_butterflies("Hello World")
    

## Feel free to add more & change stuff ^