Describe a snippet that predicts an emoji using pre-trained CLIP model.

zero-shot-emoji-predictor.py

# install these dependencies
# pip install torch transformers pillow

# import the dependencies
from PIL import Image
from transformers import CLIPProcessor, CLIPModel
import torch

# emoji images: https://public-assets-vincent-claes.s3.eu-west-1.amazonaws.com/emoji-precitor/emojis.zip
path_to_emoji_folder = "<path to the folder with images of emojis>" 
# read images
emojis_as_images = [Image.open(f"{path_to_emoji_folder}/{i}.png") for i in range(31)]
# provide text
text = "provide-some-text"

# load model and processor
checkpoint = "openai/clip-vit-base-patch32"
model = CLIPModel.from_pretrained(checkpoint)
processor = CLIPProcessor.from_pretrained(checkpoint)

# process inputs and make a prediction
inputs = processor(text=text, images=emojis_as_images, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)

# we want the probability for each emoji per sentence.
logits_per_text = outputs.logits_per_text
# we take the softmax to get the label probabilities.
prob = logits_per_text.softmax(dim=1)

# find back the label, which is the position in the list 
# of images we feed the processor.
label = torch.argmax(prob).item()

# print the label of the emoji that best describes the tweet.
print(label) # 1