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| import gradio as gr | |
| import torch | |
| import os | |
| from PIL import Image | |
| import pandas as pd | |
| from transformers import CLIPProcessor, CLIPModel | |
| checkpoint = "vincentclaes/emoji-predictor" | |
| # x_, _, files = next(os.walk("./emojis")) | |
| # no_of_emojis = range(len(files)) | |
| # emojis_as_images = [Image.open(f"emojis/{i}.png") for i in no_of_emojis] | |
| adjectives = pd.read_table("./adjectives.txt", header=None)[0].to_list() | |
| K = 4 | |
| processor = CLIPProcessor.from_pretrained(checkpoint) | |
| model = CLIPModel.from_pretrained(checkpoint) | |
| def concat_images(*images): | |
| """Generate composite of all supplied images. | |
| https://stackoverflow.com/a/71315656/1771155 | |
| """ | |
| # Get the widest width. | |
| width = max(image.width for image in images) | |
| # Add up all the heights. | |
| height = max(image.height for image in images) | |
| # set the correct size of width and heigtht of composite. | |
| composite = Image.new("RGB", (2 * width, 2 * height)) | |
| assert K == 4, "We expect 4 suggestions, other numbers won't work." | |
| for i, image in enumerate(images): | |
| if i == 0: | |
| composite.paste(image, (0, 0)) | |
| elif i == 1: | |
| composite.paste(image, (width, 0)) | |
| elif i == 2: | |
| composite.paste(image, (0, height)) | |
| elif i == 3: | |
| composite.paste(image, (width, height)) | |
| return composite | |
| def get_tag(emoji, tags, model=model, processor=processor, K=4): | |
| if tags: | |
| tags = tags.strip().split(",") | |
| else: | |
| tags = adjectives | |
| inputs = processor( | |
| text=tags, images=emoji, return_tensors="pt", padding=True, truncation=True | |
| ) | |
| outputs = model(**inputs) | |
| # we take the softmax to get the label probabilities | |
| probs = outputs.logits_per_text.softmax(dim=0) | |
| probs_formatted = torch.tensor([prob[0] for prob in probs]) | |
| values, indices = probs_formatted.topk(K) | |
| return "Tag (confidence): " + ", ".join( | |
| [f"{tags[i]} ({round(v.item(), 2)})" for v, i in zip(values, indices)] | |
| ) | |
| title = "Tagging an Emoji" | |
| description = """You provide an Emoji and our few-shot fine tuned CLIP model will suggest some tags that are appropriate.\n | |
| We use the 228 most common adjectives in english](https://grammar.yourdictionary.com/parts-of-speech/adjectives/list-of-adjective-words.html).\n | |
| You can also specify your own custom tags, for example; love,hate,fun,bitterness. | |
| """ | |
| examples = [[f"emojis/{i}.png"] for i in range(32)] | |
| text = gr.inputs.Textbox( | |
| placeholder="Enter a text and we will try to predict an emoji..." | |
| ) | |
| gr.Interface( | |
| fn=get_tag, | |
| inputs=[ | |
| gr.components.Image(type="pil", label="emoji"), | |
| gr.components.Textbox( | |
| label="tags", | |
| placeholder="Provide a comma seperated list of tags; tag1,tag2,tag3,...", | |
| ), | |
| ], | |
| outputs=gr.Textbox(), | |
| examples=examples, | |
| examples_per_page=32, | |
| title=title, | |
| description=description, | |
| ).launch() | |