Spaces:
Build error
Build error
File size: 2,684 Bytes
4be7b9f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import gradio as gr
import numpy as np
import urllib.request
# Downloading the image and saving it in the local folder
# urllib.request.urlretrieve('https://cdn.discordapp.com/attachments/682736875483430985/1089711182094553108/Dopeshott_young_black_femal_doctor_19e26be2-6801-4aed-b481-793b1e77a984.png', 'doctor.png')
# Dummy data
data = np.array([
[65, 1, 1, 1, 0.5],
[45, 0, 0, 0, 0.2],
[70, 1, 0, 1, 0.8],
[55, 0, 1, 0, 0.6],
[50, 1, 1, 1, 0.7]
])
features = ['Age', 'Sex', 'Smoking', 'Treatment', 'Chemotherapy']
sex_options = ['Male', 'Female']
smoking_options = ['No', 'Yes']
treatment_options = ['No', 'Yes']
chemo_options = ['No', 'Yes']
# Function to predict cancer
def predict_cancer(age, sex, smoking, treatment, chemotherapy):
# Format input
sex = 0 if sex == 'Male' else 1
smoking = 0 if smoking == 'No' else 1
treatment = 0 if treatment == 'No' else 1
chemotherapy = 0 if chemotherapy == 'No' else 1
input_data = np.array([[age, sex, smoking, treatment, chemotherapy]])
# Dummy prediction
prob_alive = np.random.choice([0.8, 0.7, 0.65, 0.548, 0.78])
prediction = 'Alive' if prob_alive >= 0.5 else 'Dead'
return f'{prediction} with a survival probability of {prob_alive:.3f}'
# Interface
age_slider = gr.inputs.Slider(minimum=20, maximum=90, step=1, label='Age')
sex_dropdown = gr.inputs.Dropdown(choices=sex_options, label='Sex')
smoking_dropdown = gr.inputs.Dropdown(choices=smoking_options, label='Smoking')
treatment_dropdown = gr.inputs.Dropdown(choices=treatment_options, label='Treatment')
chemo_dropdown = gr.inputs.Dropdown(choices=chemo_options, label='Chemotherapy')
inputs = [age_slider, sex_dropdown, smoking_dropdown, treatment_dropdown, chemo_dropdown]
outputs = gr.outputs.Textbox(label='Prediction')
css = """
.sidebar-content {
padding: 20px;
background-color: #f5f5f5;
box-shadow: 1px 1px 10px #ccc;
margin-bottom: 20px;
border-radius: 10px;
}
.doctor-img-container {
display: flex;
align-items: center;
justify-content: center;
}
.doctor-img {
height: auto;
width: auto;
max-width: 100%;
max-height: 100%;
}
"""
sidebar = [
# Adding image of a doctor to the sidebar
'<div class="doctor-img-container"><img class="doctor-img" src="doctor.png" alt="A medical doctor"></div>',
'This is a cancer prediction app',
]
iface = gr.Interface(
fn=predict_cancer,
inputs=inputs,
outputs=outputs,
title='Cancer Predictor',
description='Predicts the risk of survival in cancer patients.\nChoose the input values and the prediction will be displayed automatically.',
sidebar=sidebar,
css=css
)
iface.launch() |