Create app.py

app.py

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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()