clip_chat.py

import torch
import clip
from PIL import Image
import glob
import os
from random import choice


device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-L/14@336px", device=device)
COCO = glob.glob(os.path.join(os.getcwd(), "images", "*"))
# with open('coco_paths.txt', 'r') as file:
#     COCO = file.read().split('\n')[:-1]
# COCO = [c.replace("/media/bonilla/My Book/coco/train2017/", "D:\\coco\\train2017\\") for c in COCO]


def load_random_image():
    image_path = choice(COCO)
    image = Image.open(image_path)
    return image


def next_image():
    global image_org, image
    image_org = load_random_image()
    image = preprocess(Image.fromarray(image_org)).unsqueeze(0).to(device)


last = -1
best = -1

goal = 21

image_org = load_random_image()
image = preprocess(image_org).unsqueeze(0).to(device)


def answer(message):
    global last, best

    text = clip.tokenize([message]).to(device)

    with torch.no_grad():
        logits_per_image, _ = model(image, text)
        logits = logits_per_image.cpu().numpy().flatten()[0]

    if last == -1:
        is_better = -1
    elif last > logits:
        is_better = 0
    elif last < logits:
        is_better = 1
    elif logits > goal:
        is_better = 2
    else:
        is_better = -1

    last = logits
    if logits > best:
        best = logits
        is_better = 3

    return logits, is_better  # logit2sentence(logits) + " " + is_better + " " + f"({logits})"


def reset_everything():
    global last, best, goal, image, image_org
    last = -1
    best = -1
    goal = 21
    image_org = load_random_image()
    image = preprocess(image_org).unsqueeze(0).to(device)