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| import os | |
| from PIL import Image | |
| import base64 | |
| import torch | |
| import torch.nn as nn | |
| from safetensors.torch import load_model | |
| import gradio as gr | |
| from imports import Model, image_transform, classes | |
| # Variables | |
| base_path = os.path.dirname(__file__) | |
| model_file_path = f'{base_path}/data/model/emoji.safetensors' | |
| learned_emojis_base64 = '' | |
| with open(f'{base_path}/data/learned_emojis.png', 'rb') as f: | |
| learned_emojis_base64 = base64.b64encode(f.read()).decode() | |
| example_images = [f'{base_path}/data/test_images/smile.png'] | |
| description = '' | |
| article = f''' | |
| <h3>Description</h3> | |
| This project focuses on classifying hand-drawn emojis. | |
| Interestingly, the model was trained exclusively on outline images, yet it's capable of interpreting hand-drawn input. | |
| This approach demonstrates the model's ability to generalize from simplified representations to more varied, real-world examples. | |
| Curious about the development process? You can explore the project's creation in detail here: | |
| <a href="https://www.kaggle.com/code/krayc81/emoji-doodle">Click here</a> | |
| <h3>How to Use:</h3> | |
| 1. Choose one of the emojis in the picture underneath<br> | |
| 2. Using the drawing interface on the left, sketch your interpretation of the chosen emoji.<br> | |
| 3. When you are satisfied with your drawing, click the "Submit" button.<br><br> | |
| Your hand-drawn emoji will then be analyzed by our classification model.<br> | |
| This interactive process allows you to test the model's ability to recognize various emoji styles and interpretations.<br> | |
| <img src="data:image/png;base64,{learned_emojis_base64}"> | |
| ''' | |
| # Functions | |
| def classify(data): | |
| image = data['composite'] | |
| if image.size[0] > 256: # 256 is example images | |
| image = data['layers'][0] | |
| image = Image.composite(image, Image.new('RGB', image.size, 'white'), image) | |
| with torch.no_grad(): | |
| inputs = image_transform(image, False).unsqueeze(1) | |
| outputs = model(inputs) | |
| outputs = nn.functional.softmax(outputs, 1).squeeze() | |
| return dict(zip(classes, map(float, outputs))) | |
| # Model loading | |
| model = Model(1, len(classes)) | |
| load_model(model, model_file_path) | |
| #model.load_state_dict(torch.load(model_file_path)) | |
| model.eval() | |
| # Run | |
| image = gr.Sketchpad( | |
| type = 'pil', | |
| image_mode = 'RGBA', | |
| layers = False, | |
| brush = gr.Brush(colors=['#000000'], color_mode='fixed'), | |
| #canvas_size = (512, 512), | |
| transforms=() | |
| ) | |
| label = gr.Label() | |
| iface = gr.Interface( | |
| fn = classify, | |
| inputs = image, | |
| outputs = label, | |
| examples = example_images, | |
| title = '🤩 Emoji Doodle', | |
| description = description, | |
| article = article, | |
| allow_flagging = 'never', | |
| theme = gr.themes.Default(), | |
| js = ''' | |
| function refresh() { | |
| const url = new URL(window.location); | |
| if (url.searchParams.get('__theme') !== 'light') { | |
| url.searchParams.set('__theme', 'light'); | |
| window.location.href = url.href; | |
| } | |
| } | |
| ''' | |
| ) | |
| iface.launch() |