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import gradio as gr
import numpy as np
import random # Simulating real-time health data
from sklearn.linear_model import LogisticRegression # Example of advanced AI model
from sklearn.preprocessing import StandardScaler
# Simulated dataset and AI model training
def train_ai_model():
"""
Trains a simple Logistic Regression model to classify fabric adjustments
based on heart rate, temperature, and activity level.
"""
# Example training data: [heart_rate, body_temp, activity_level (encoded)]
X = np.array([
[60, 35.5, 0], # Low activity, low temperature
[90, 36.8, 1], # Medium activity, normal temperature
[110, 38.0, 2], # High activity, high temperature
[75, 37.0, 0], # Low activity, normal temperature
[100, 38.5, 2], # High activity, very high temperature
])
# Fabric adjustment labels: 0 = "Maintaining", 1 = "Cooling", 2 = "Warming"
y = np.array([2, 0, 1, 0, 1])
# Scale the features for better model performance
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train the Logistic Regression model
model = LogisticRegression()
model.fit(X_scaled, y)
return model, scaler
# Train the AI model
ai_model, feature_scaler = train_ai_model()
# Function to simulate live health data from wearable sensors
def fetch_real_health_data():
"""
Simulates real-time health data. Replace this with actual hardware integration
for wearable devices using libraries like pyserial or APIs from wearables.
"""
heart_rate = random.randint(60, 120) # Random BPM
body_temp = round(random.uniform(35.5, 38.5), 1) # Random temperature in °C
activity_level = random.choice(["Low", "Medium", "High"]) # Simulated activity level
return heart_rate, body_temp, activity_level
# Function to adjust fabric based on health metrics and user preferences
def adjust_fabric(real_time, heart_rate_pref, temp_pref, cooling_pref, warming_pref):
if real_time:
# Fetch live health data
heart_rate, body_temp, activity_level = fetch_real_health_data()
else:
# Default values for demo purposes
heart_rate, body_temp, activity_level = 70, 36.5, "Medium"
# Map activity level to numeric values for AI model
activity_map = {"Low": 0, "Medium": 1, "High": 2}
activity_level_num = activity_map[activity_level]
# Prepare input for AI model
features = np.array([[heart_rate, body_temp, activity_level_num]])
features_scaled = feature_scaler.transform(features)
# Get fabric adjustment prediction from AI model
fabric_adjustment_pred = ai_model.predict(features_scaled)[0]
adjustment_map = {0: "Maintaining temperature", 1: cooling_pref, 2: warming_pref}
fabric_adjustment = adjustment_map[fabric_adjustment_pred]
# Heart rate feedback based on user preference
if heart_rate > heart_rate_pref:
heart_rate_feedback = f"Warning: High Heart Rate ({heart_rate} BPM)! Adjusting fabric for {cooling_pref}."
else:
heart_rate_feedback = f"Heart rate is normal ({heart_rate} BPM)."
# Body temperature feedback based on user preference
if body_temp > temp_pref:
temp_feedback = f"High body temperature ({body_temp}°C)! Activating {cooling_pref} mechanism."
else:
temp_feedback = f"Body temperature is normal ({body_temp}°C)."
# Activity level feedback
if activity_level == "High":
activity_feedback = f"High activity level detected. Increasing {cooling_pref}."
elif activity_level == "Medium":
activity_feedback = "Moderate activity level detected. Maintaining fabric temperature."
else:
activity_feedback = f"Low activity level detected. Activating {warming_pref} if needed."
# Return the feedback and fabric adjustment details
return (f"Heart Rate: {heart_rate} BPM\n{heart_rate_feedback}",
f"Body Temperature: {body_temp}°C\n{temp_feedback}",
f"Activity Level: {activity_level}\n{activity_feedback}",
fabric_adjustment)
# Create Gradio Interface
iface = gr.Interface(
fn=lambda real_time, heart_rate_pref, temp_pref, cooling_pref, warming_pref:
adjust_fabric(real_time, heart_rate_pref, temp_pref, cooling_pref, warming_pref),
inputs=[
gr.Checkbox(label="Enable Real-Time Data", value=False),
gr.Slider(minimum=60, maximum=120, step=1, label="Preferred Max Heart Rate (BPM)", value=100),
gr.Slider(minimum=35, maximum=40, step=0.1, label="Preferred Max Body Temperature (°C)", value=37.5),
gr.Radio(choices=["Cooling", "Strong Cooling"], label="Cooling Mechanism Preference", value="Cooling"),
gr.Radio(choices=["Warming", "Strong Warming"], label="Warming Mechanism Preference", value="Warming"),
],
outputs=[
gr.Textbox(label="Heart Rate Feedback"),
gr.Textbox(label="Temperature Feedback"),
gr.Textbox(label="Activity Feedback"),
gr.Textbox(label="Fabric Adjustment")
],
live=True
)
# Launch the Gradio interface
iface.launch()
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