Create visuals.py

modules/visuals.py

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+import streamlit as st
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+import time
+
+# Display the dashboard with metrics and visualizations
+def display_dashboard(df):
+    st.subheader("📊 System Summary")
+
+    col1, col2, col3 = st.columns(3)
+
+    col1.metric("Total Poles", df.shape[0])
+    col2.metric("🚨 Red Alerts", df[df['Alert Level'] == "Red"].shape[0])
+    col3.metric("⚡ Power Issues", df[df['Power Sufficient'] == "No"].shape[0])
+
+    # Heatmap with Faults Visualization
+    st.subheader("📈 Pole Signal Heatmap with Fault Zones")
+    
+    # Create random data for the heatmap (replace with your simulation data)
+    data = np.random.rand(10, 10)
+    fault_x, fault_y = np.random.randint(0, 10), np.random.randint(0, 10)
+    
+    # Show initial heatmap
+    fig, ax = plt.subplots(figsize=(8, 6))
+    sns.heatmap(data, annot=True, cmap="coolwarm", ax=ax)
+    
+    # Show a red dot if there's a fault
+    ax.plot(fault_y, fault_x, 'ro', markersize=10)  # Red dot for fault location
+    
+    st.pyplot(fig)
+
+# Function to simulate blinking red dot and alert
+def display_fault_alert(fault_x, fault_y):
+    st.error(f"Fault detected at Zone ({fault_x}, {fault_y}). Red alert triggered!")
+    
+    # Simulate blinking red dot effect
+    for _ in range(5):
+        fig, ax = plt.subplots(figsize=(8, 6))
+        sns.heatmap(np.random.rand(10, 10), annot=True, cmap="coolwarm", ax=ax)  # Draw heatmap
+        
+        # Plot blinking red dot at fault location
+        ax.plot(fault_y, fault_x, 'ro', markersize=10)
+        
+        # Display updated plot
+        st.pyplot(fig)
+        time.sleep(0.5)  # Wait for 0.5 seconds to simulate blinking
+        ax.clear()  # Clear previous plot