| import gradio as gr | |
| import pickle | |
| import pandas as pd | |
| from colormap import rgb2hex | |
| from PIL import Image | |
| def predict(m_cloth, v_w, v_m, v_k, v_b, m_al, m_cu, m_fe, m_tan, time, pH): | |
| xgb = pickle.load(open('xgb.ckpt', 'rb')) | |
| v_sum = v_w + v_m + v_k + v_b | |
| X = pd.DataFrame({'m_cloth': m_cloth, 'v_w': v_w / v_sum, 'v_m': v_m / v_sum, 'v_k': v_k / v_sum, 'v_b': v_b / v_sum, 'm_al': m_al, 'm_cu': m_cu, 'm_fe': m_fe, 'm_tan': m_tan, 'time': time, 'pH': pH}, index = [0]) | |
| y = xgb.predict(X) | |
| r, g, b = y[0] | |
| img = Image.new('RGB',(200,200),(int(r),int(g),int(b))) | |
| hex_color = rgb2hex(int(r), int(g), int(b)) | |
| return hex_color, int(r), int(g), int(b), hex_color, img | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| gr.Markdown( | |
| """ | |
| # Predicting the conditions of dyeing of cotton fabric with natural dyes to obtain a given color | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| gr.Markdown( | |
| """ | |
| This service was created as part of the project **"Predicting the conditions of dyeing of cotton fabric with natural dyes to obtain a given color"** under the **Sirius.Leto** program. The purpose of this service is to determine in advance the result of dyeing the fabric with natural dyes, without conducting experiments. This is achieved by using the **Extreme Gradient Boosting Regressor** model, which allows to predict the color in **RGB** format based on the initial dyeing parameters. | |
| To build this model, **240** fabric dyeing experiments were conducted at **ITMO University**. These experiments formed the dataset on which the models were built with **R<sup>2</sup> = 0.82** on the test set. Additionally, **8** experiments close but different from the experiments in the original dataset were performed, on which the algorithm was validated. The accuracy on the validation set was **R<sup>2</sup> = 0.88**. | |
| Dataset, code and detailed slides: **TBA**. | |
| Limitations of the algorithm's performance: | |
| * Limited palette of colors for which high prediction accuracy is preserved (see *Figure 1*) | |
| * Limited investigation of the influence of additional factors such as additives, pH, temperature (many of these parameters are less likely to determine the final color) | |
| * As in many machine learning models, borderline values with less data are predicted worse (e.g. in the case of very dilute solutions) | |
| Authors: | |
| Ekaterina Veselyaeva \*, | |
| Alisa Pigulevskaya \*, | |
| Sofia Ryakhovskaya \*. | |
| Supervisor: | |
| Ivan Dubrovsky \*\*. | |
| \* Lyceum № 226, St. Petersburg | |
| \*\* Artificial Intelligence in Chemistry Center, ITMO University, St. Petersburg | |
| """) | |
| with gr.Column(scale=3): | |
| gr.Image("https://drive.usercontent.google.com/u/1/uc?id=1Bokju7A-owxh3cc3C623YqmAqKSSrdtO&export=download", height = 500, width = 500) | |
| gr.Markdown(""" | |
| *Figure 1. A palette of colors that the algorithm is able to predict with high accuracy (mostly the colors of the original dyes and their combinations).* | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| inp= [gr.Number(label = 'Weight of fabric, g', minimum = 0, maximum = 1000, value = 0.1), | |
| gr.Number(label = 'Water volume, ml', minimum = 0, maximum = 1000, value = 5), | |
| gr.Number(label = 'Volume of madder dye solution, ml', minimum = 0, maximum = 1000, value = 5), | |
| gr.Number(label = 'Volume of turmeric dye solution, ml', minimum = 0, maximum = 1000, value = 5), | |
| gr.Number(label = 'Volume of elderberry dye solution, ml', minimum = 0, maximum = 1000), | |
| gr.Number(label = 'Weight of added aluminum salt, g', minimum = 0, maximum = 1000), | |
| gr.Number(label = 'Weight of added copper salt, g', minimum = 0, maximum = 1000), | |
| gr.Number(label = 'Weight of added iron salt, g', minimum = 0, maximum = 1000), | |
| gr.Number(label = 'Weight of added tannin, g', minimum = 0, maximum = 1000), | |
| gr.Number(label = 'Dyeing time, min', minimum = 0, maximum = 5000, value = 60), | |
| gr.Number(label = 'pH', minimum = 0, maximum = 14, value = 6)] | |
| with gr.Column(scale=3): | |
| out = [gr.ColorPicker(label="Color"), | |
| gr.Number(label = 'R', minimum = 0, maximum = 255), | |
| gr.Number(label = 'G', minimum = 0, maximum = 255), | |
| gr.Number(label = 'B', minimum = 0, maximum = 255), | |
| gr.Textbox(label="Hexadecimal color"), | |
| gr.Image(label="Color image", height = 500, width = 500)] | |
| button = gr.Button() | |
| button.click(fn=predict, inputs=inp, outputs=out) | |
| demo.launch() |