app.py

DEVICE = 'cpu'

import gradio as gr
import numpy as np
from sklearn.svm import LinearSVC
from sklearn import preprocessing
import pandas as pd

import random
import time

import replicate
import torch
from urllib.request import urlopen

from PIL import Image
import requests
from io import BytesIO, StringIO

prompt_list = [p for p in list(set(
                pd.read_csv('./twitter_prompts.csv').iloc[:, 1].tolist())) if type(p) == str]

calibrate_prompts = [
    "4k photo",
    'surrealist art',
    # 'a psychedelic, fractal view',
    'a beautiful collage',
    'abstract art',
    'an eldritch image',
    'a sketch',
    # 'a city full of darkness and graffiti',
    '',
]

start_time = time.time()

# TODO add to state instead of shared across all
glob_idx = 0

def next_image(embs, ys):
    global glob_idx
    glob_idx = glob_idx + 1
    with torch.no_grad():
        if len(calibrate_prompts) > 0:
            print('######### Calibrating with sample prompts #########')
            prompt = calibrate_prompts.pop(0)
            print(prompt)

            output = replicate.run(
            "rynmurdock/zahir:42c58addd49ab57f1e309f0b9a0f271f483bbef0470758757c623648fe989e42",
            input={"prompt": prompt,}
            )
            response = requests.get(output['file1'])
            image = Image.open(BytesIO(response.content))

            embs.append(torch.tensor([float(i) for i in urlopen(output['file2']).read().decode('utf-8').split(', ')]).unsqueeze(0))
            return image, embs, ys
        else:
            print('######### Roaming #########')

            # sample only as many negatives as there are positives
            indices = range(len(ys))
            pos_indices = [i for i in indices if ys[i] == 1]
            neg_indices = [i for i in indices if ys[i] == 0]
            lower = min(len(pos_indices), len(neg_indices))
            neg_indices = random.sample(neg_indices, lower)
            pos_indices = random.sample(pos_indices, lower)

            cut_embs = [embs[i] for i in neg_indices] + [embs[i] for i in pos_indices]
            cut_ys = [ys[i] for i in neg_indices] + [ys[i] for i in pos_indices]

            feature_embs = torch.stack([e[0].detach().cpu() for e in cut_embs])
            scaler = preprocessing.StandardScaler().fit(feature_embs)
            feature_embs = scaler.transform(feature_embs)
            print(np.array(feature_embs).shape, np.array(ys).shape)

            lin_class = LinearSVC(max_iter=50000, dual='auto', class_weight='balanced').fit(np.array(feature_embs), np.array(cut_ys))
            lin_class.coef_ = torch.tensor(lin_class.coef_, dtype=torch.double)
            lin_class.coef_ = (lin_class.coef_.flatten() / (lin_class.coef_.flatten().norm())).unsqueeze(0)


            rng_prompt = random.choice(prompt_list)

            w = 1# if len(embs) % 2 == 0 else 0
            im_emb = w * lin_class.coef_.to(device=DEVICE, dtype=torch.float16)
            prompt= 'an image' if glob_idx % 2 == 0 else rng_prompt
            print(prompt)

            im_emb_st = str(im_emb[0].cpu().detach().tolist())[1:-1]
            

            output = replicate.run(
            "rynmurdock/zahir:42c58addd49ab57f1e309f0b9a0f271f483bbef0470758757c623648fe989e42",
            input={"prompt": prompt, 'im_emb': im_emb_st}
            )
            response = requests.get(output['file1'])
            image = Image.open(BytesIO(response.content))


            im_emb = torch.tensor([float(i) for i in urlopen(output['file2']).read().decode('utf-8').split(', ')]).unsqueeze(0)
            embs.append(im_emb)

            torch.save(lin_class.coef_, f'./{start_time}.pt')
            return image, embs, ys









def start(_):
    return [
            gr.Button(value='Like', interactive=True), 
            gr.Button(value='Neither', interactive=True), 
            gr.Button(value='Dislike', interactive=True),
            gr.Button(value='Start', interactive=False),
            next_image()
            ]


def choose(choice, embs, ys):
    if choice == 'Like':
        choice = 1
    elif choice == 'Neither':
        _ = embs.pop(-1)
        return next_image(embs, ys)
    else:
        choice = 0
    ys.append(choice)
    return next_image(embs, ys)

css = "div#output-image {height: 768px !important; width: 768px !important; margin:auto;}"
with gr.Blocks(css=css) as demo:
    embs = gr.State([])
    ys = gr.State([])
    with gr.Row(elem_id='output-image'):
        img = gr.Image(interactive=False, elem_id='output-image',)
    with gr.Row(equal_height=True):
        b3 = gr.Button(value='Dislike', interactive=False,)
        b2 = gr.Button(value='Neither', interactive=False,)
        b1 = gr.Button(value='Like', interactive=False,)
        b1.click(
        choose, 
        [b1, embs, ys],
        [img, embs, ys]
        )
        b2.click(
        choose, 
        [b2, embs, ys],
        [img, embs, ys]
        )
        b3.click(
        choose, 
        [b3, embs, ys],
        [img, embs, ys]
        )
    with gr.Row():
        b4 = gr.Button(value='Start')
        b4.click(start,
                 [b4, embs, ys],
                 [b1, b2, b3, b4, img,])
    with gr.Row():
        html = gr.HTML('''<div style='text-align:center; font-size:32'>You will callibrate for several prompts and then roam.</ div>''')

demo.launch()  # Share your demo with just 1 extra parameter 🚀