import streamlit as st # HF spaces at v1.2.0
from demo import load_model,generate,get_dataset,embed,make_meme
from PIL import Image
import numpy as np
import io
# TODOs
# Add markdown short readme project intro
# Add link to wandb logs


st.sidebar.subheader("This butterfly does not exist! ")
st.sidebar.image("assets/logo.png", width=200)

st.title("ButterflyGAN")

@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
    st.session_state['latent_walk_code']=open("assets/code_snippets/latent_walk.py").read()
    st.session_state['latent_walk_code_music']=open("assets/code_snippets/latent_walk_music.py").read()

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='0edac54b81958b82ce9fd5c1f688c33ac8e4f223'
model_version=None ##TBD
model=load_model_intocache(model_name,model_version)
dataset=load_dataset()
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])

if screen == generate_menu:
    
    batch_size=4 #generate 4 butterflies 
    col_num=4
    def run():
        with st.spinner("Generating..."):
            ims=generate(model,batch_size)
            st.session_state['ims'] = ims
    if 'ims' not in st.session_state:
        st.session_state['ims'] = None
        run()
    ims=st.session_state["ims"]
    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*")

    runb=st.button("Generate", on_click=run ,help="generated on the fly maybe slow")
    if ims is not None:
        cols=st.columns(col_num)
        picks=[False]*batch_size
        for j,im in enumerate(ims):
            i=j%col_num
            cols[i].image(im)
            picks[j]=cols[i].button("Find Nearest",key="pick_"+str(j))
            # meme_it=cols[i].button("What is this?",key="meme_"+str(j))
            # if meme_it:
            #     no_bg=st.checkbox("Remove background?",True)
            #     meme_text=st.text_input("Meme text","Is this a pigeon?")
            #     meme=make_meme(im,text=meme_text,show_text=True,remove_background=no_bg)
            #     st.image(meme)
            # if picks[j]:
            #     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("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:

    latent_walk_code=open("assets/code_snippets/latent_walk.py").read()
    latent_walk_music_code=open("assets/code_snippets/latent_walk_music.py").read()
    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(st.session_state['latent_walk_code'], language='python')
        st.write(":musical_note: latent walk with music:")
        st.code(st.session_state['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)")


# footer stuff
st.sidebar.caption(f"[Model](https://huggingface.co/ceyda/butterfly_cropped_uniq1K_512) & [Dataset](https://huggingface.co/huggan/smithsonian_butterflies_subset) used")
# Link project repo( scripts etc )

# Credits
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/)")

## Feel free to add more & change stuff ^