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| import gradio as gr | |
| import math | |
| import numpy as np | |
| import os | |
| import matplotlib.pyplot as plt | |
| import random | |
| from skimage import io as skio | |
| def evolution_plot(current_age): | |
| n = 10000 | |
| maxi = 100 | |
| x = np.linspace(0, maxi, 10000) | |
| max_state = 7 | |
| final_state = 5 | |
| y = 1 / (1 + np.exp(-(x / 8) + 5)) | |
| y = (y - np.min(y)) / (np.max(y - np.min(y))) * (final_state - 1) + 1 | |
| plt.title("Hair Loss Evolution Prediction") | |
| plt.xlabel("Age") | |
| plt.ylabel("Nordwood State") | |
| plt.ylim(1, max_state) | |
| actual = np.where(x < int(current_age))[0][-1] | |
| plt.plot(x[:actual], y[:actual]) | |
| plt.plot(x[actual:], y[actual:], '--') | |
| im_save_path = f'tmp/{random.randint(0, 10000)}.png' | |
| plt.savefig(im_save_path) | |
| plt.clf() | |
| plot = skio.imread(im_save_path) | |
| return plot | |
| def pad(arr): | |
| arr = list(map(str, arr)) | |
| max_len = 0 | |
| for i in arr: | |
| if len(i)>max_len: | |
| max_len = len(i) | |
| for i in range(len(arr)): | |
| for j in range(max_len-len(arr[i])): | |
| arr[i] = arr[i] + " " | |
| return arr | |
| def predict(file, age, parent, gp, shampoo): | |
| product = ['Computer', 'Monitor ', 'Laptop ', 'Printer ', 'Tablet '] | |
| quantity = pad(np.array([320, 450, 300, 120, 280]) / 500) | |
| min_normal = pad(np.array([250, 200, 210, 100, 250]) / 500) | |
| max_normal = pad(np.array([400, 300, 450, 150, 300]) / 500) | |
| variants = ["Y2TTB9", "9KKGH7", "ML0JH9"] | |
| risks = ['Seborrheic eczema', 'Folliculitis', "Pityriasis amiantacea"] | |
| percents = ['30', "21", '9'] | |
| geneticRisks = "" | |
| for variant, risk, percent in zip(variants, risks, percents): | |
| geneticRisks += f"Variant {variant} detected, risk of {risk} is {percent}% higher.\n" | |
| useful_products = ("Hydrating Shampoo : https://www.thisisthebestshampooforyou.fr/fr-fr/\n" | |
| "Light Hat : https://www.amazon.com/light-therapy-hat/s?k=light+therapy+hat") | |
| return skio.imread("results.png"), 'Dermatitis\nDryness\nDandruff', geneticRisks, evolution_plot(age), useful_products | |
| # GUI | |
| title = 'Hair loss prediction' | |
| description = 'Metagenomics Scalp Analysis for Hair Loss Prediction' | |
| # examples = [[f'examples/{name}', 3] for name in sorted(os.listdir('examples'))] | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.File(value="tmp/metagenome.txt", type='file', label='Scalp sample'), | |
| gr.Textbox(label='Age'), | |
| gr.CheckboxGroup(choices=["Yes", "No", "Do not know"], label="Has the father experienced hair loss ?"), | |
| gr.CheckboxGroup(choices=["0", "1", "2", "Do not know"], label="How many grand-parents have experienced hair loss ?"), | |
| gr.Textbox(label='How many times a week do you wash your hair ?'), | |
| ], | |
| outputs=[ | |
| gr.Image(label='Scalp Metagenomics Analysis Results'), | |
| gr.Text(label='Current issues :'), | |
| gr.Text(label='Genetic Risks :'), | |
| gr.Image(label='Future Evolution'), | |
| gr.Text(label='Useful Care Products') | |
| ], | |
| allow_flagging='never', | |
| cache_examples=False, | |
| title=title, | |
| description=description | |
| ) | |
| iface.launch() | |