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import gradio as gr
import random
import numpy as np
from PIL import Image
import io
import matplotlib.pyplot as plt
import plotly.graph_objects as go
questions = [
{
"question": "How often do you feel overwhelmed by your daily tasks?",
"options": ["Rarely", "Sometimes", "Often", "Very Often"]
},
{
"question": "How would you rate your sleep quality?",
"options": ["Excellent", "Good", "Fair", "Poor"]
},
{
"question": "How often do you feel anxious or worried?",
"options": ["Rarely", "Sometimes", "Often", "Very Often"]
},
{
"question": "How do you typically handle stressful situations?",
"options": ["Very Well", "Moderately Well", "With Difficulty", "Poorly"]
},
{
"question": "How satisfied are you with your work-life balance?",
"options": ["Very Satisfied", "Satisfied", "Dissatisfied", "Very Dissatisfied"]
},
{
"question": "How often do you engage in relaxing activities?",
"options": ["Daily", "Few times a week", "Rarely", "Never"]
},
{
"question": "How would you describe your energy levels throughout the day?",
"options": ["Consistently High", "Moderate", "Fluctuating", "Usually Low"]
},
{
"question": "How often do you feel supported by others?",
"options": ["Always", "Usually", "Occasionally", "Rarely"]
},
{
"question": "How do you rate your ability to concentrate?",
"options": ["Excellent", "Good", "Fair", "Poor"]
},
{
"question": "How often do you experience physical tension or pain?",
"options": ["Rarely", "Sometimes", "Often", "Very Often"]
}
]
def calculate_stress_score(answers):
stress_values = {
"Rarely": 0, "Sometimes": 1, "Often": 2, "Very Often": 3,
"Excellent": 0, "Good": 1, "Fair": 2, "Poor": 3,
"Very Well": 0, "Moderately Well": 1, "With Difficulty": 2, "Poorly": 3,
"Very Satisfied": 0, "Satisfied": 1, "Dissatisfied": 2, "Very Dissatisfied": 3,
"Daily": 0, "Few times a week": 1, "Rarely": 2, "Never": 3,
"Consistently High": 0, "Moderate": 1, "Fluctuating": 2, "Usually Low": 3,
"Always": 0, "Usually": 1, "Occasionally": 2, "Rarely": 3
}
total = sum(stress_values[ans] for ans in answers)
percentage = (total / (3 * 10)) * 100
return percentage
def get_recommendations(stress_score):
if stress_score < 30:
return [
"Maintain your current stress management practices",
"Continue regular exercise and relaxation routines",
"Keep up with your healthy sleep schedule"
]
elif stress_score < 60:
return [
"Consider incorporating meditation or mindfulness practices",
"Take regular breaks during work hours",
"Establish a consistent sleep routine"
]
else:
return [
"Seek professional support or counseling",
"Practice deep breathing exercises daily",
"Prioritize self-care and stress reduction activities"
]
def create_stress_gauge(score):
fig = go.Figure(go.Indicator(
mode = "gauge+number",
value = score,
title = {'text': "Stress Level"},
gauge = {
'axis': {'range': [0, 100]},
'bar': {'color': "red" if score > 60 else "yellow" if score > 30 else "green"},
'steps': [
{'range': [0, 30], 'color': 'lightgreen'},
{'range': [30, 60], 'color': 'lightyellow'},
{'range': [60, 100], 'color': 'lightcoral'}
]
}
))
return fig
def process_assessment(*answers):
score = calculate_stress_score(answers)
recommendations = get_recommendations(score)
gauge_plot = create_stress_gauge(score)
result_html = f"""
<div style='padding: 20px; background: white; border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1);'>
<h2 style='color: #2c3e50;'>Assessment Results</h2>
<p style='font-size: 18px;'>Your Stress Level: {score:.1f}%</p>
<h3 style='color: #2c3e50; margin-top: 20px;'>Recommendations:</h3>
<ul style='list-style-type: none; padding: 0;'>
"""
for rec in recommendations:
result_html += f"<li style='margin: 10px 0; padding: 10px; background: #f8f9fa; border-radius: 5px;'>🌟 {rec}</li>"
result_html += "</ul></div>"
return gauge_plot, result_html
with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="purple")) as iface:
gr.Markdown("""
# Psychological Stress Assessment
Answer these questions to evaluate your current stress levels and receive personalized recommendations.
""")
with gr.Group():
questions_components = []
for i, q in enumerate(questions):
gr.Markdown(f"### {i+1}. {q['question']}")
questions_components.append(gr.Radio(choices=q['options'], label=""))
submit_btn = gr.Button("Submit Assessment", variant="primary")
with gr.Row():
gauge_output = gr.Plot()
results_output = gr.HTML()
submit_btn.click(
fn=process_assessment,
inputs=questions_components,
outputs=[gauge_output, results_output]
)
iface.launch()
if __name__ == '__main__':
demo.launch() |