Upload app.py

app.py

@@ -0,0 +1,596 @@
+#!/usr/bin/env python3
+"""
+zeroFire - Fire Detection Classification App
+AI-powered fire detection using ConvNeXt transfer learning
+"""
+
+import streamlit as st
+import torch
+import torch.nn.functional as F
+from PIL import Image
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+import pandas as pd
+import sys
+import os
+import time
+from io import BytesIO
+import base64
+
+# Add utils to path
+sys.path.append('utils')
+from model_utils import load_model, FireDetectionClassifier
+from data_utils import get_inference_transform, prepare_image_for_inference, check_data_directory
+
+# Page Configuration
+st.set_page_config(
+    page_title="🔥 zeroFire - Fire Detection System",
+    page_icon="🔥",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for Beautiful UI
+st.markdown("""
+<style>
+    .main-header {
+        background: linear-gradient(135deg, #74b9ff 0%, #0984e3 100%);
+        padding: 2rem;
+        border-radius: 15px;
+        text-align: center;
+        color: white;
+        margin-bottom: 2rem;
+        box-shadow: 0 8px 32px rgba(0,0,0,0.1);
+    }
+    
+    .main-header h1 {
+        font-size: 3rem;
+        margin: 0;
+        font-weight: bold;
+        text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+    }
+    
+    .main-header p {
+        font-size: 1.2rem;
+        margin: 0.5rem 0 0 0;
+        opacity: 0.9;
+    }
+    
+    .upload-section {
+        background: linear-gradient(135deg, #a8e6cf 0%, #74b9ff 100%);
+        color: white;
+        padding: 15px 20px;
+        border-radius: 15px;
+        text-align: center;
+        margin-bottom: 20px;
+        box-shadow: 0 6px 20px rgba(168, 230, 207, 0.3);
+        height: 100px;
+        display: flex;
+        flex-direction: column;
+        justify-content: center;
+    }
+    
+    .upload-section h3 {
+        font-size: 1.3rem;
+        margin: 0 0 5px 0;
+    }
+    
+    .upload-section p {
+        font-size: 0.9rem;
+        margin: 0;
+        opacity: 0.9;
+    }
+    
+    .result-fire {
+        background: linear-gradient(135deg, #dc3545 0%, #c82333 100%);
+        color: white;
+        padding: 15px 20px;
+        border-radius: 15px;
+        text-align: center;
+        margin: 0 0 20px 0;
+        box-shadow: 0 6px 20px rgba(220, 53, 69, 0.3);
+        height: 100px;
+        display: flex;
+        flex-direction: column;
+        justify-content: center;
+    }
+    
+    .result-fire h2 {
+        font-size: 1.2rem;
+        margin: 0 0 5px 0;
+        line-height: 1.2;
+    }
+    
+    .result-fire p {
+        font-size: 0.85rem;
+        margin: 0;
+        font-weight: bold;
+    }
+    
+    .result-no-fire {
+        background: linear-gradient(135deg, #28a745 0%, #20c997 100%);
+        color: white;
+        padding: 15px 20px;
+        border-radius: 15px;
+        text-align: center;
+        margin: 0 0 20px 0;
+        box-shadow: 0 6px 20px rgba(40, 167, 69, 0.3);
+        height: 100px;
+        display: flex;
+        flex-direction: column;
+        justify-content: center;
+    }
+    
+    .result-no-fire h2 {
+        font-size: 1.2rem;
+        margin: 0 0 5px 0;
+        line-height: 1.2;
+    }
+    
+    .result-no-fire p {
+        font-size: 0.85rem;
+        margin: 0;
+        font-weight: bold;
+    }
+    
+    .metric-card {
+        background: linear-gradient(135deg, #ff9ff3 0%, #f368e0 100%);
+        padding: 15px;
+        border-radius: 10px;
+        text-align: center;
+        color: white;
+        margin: 10px 0;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.2);
+    }
+    
+    .info-card {
+        background: linear-gradient(135deg, #ff9ff3 0%, #f368e0 100%);
+        color: white;
+        padding: 20px;
+        border-radius: 15px;
+        margin: 15px 0;
+        box-shadow: 0 8px 32px rgba(0,0,0,0.1);
+    }
+    
+    .info-card-fire {
+        background: linear-gradient(135deg, #ff6b6b 0%, #ee5a52 100%);
+        color: white;
+        padding: 20px;
+        border-radius: 15px;
+        margin: 15px 0;
+        box-shadow: 0 8px 32px rgba(0,0,0,0.1);
+    }
+    
+    .stButton > button {
+        background: linear-gradient(45deg, #74b9ff, #0984e3);
+        color: white;
+        border: none;
+        border-radius: 10px;
+        padding: 12px 24px;
+        font-weight: bold;
+        font-size: 1.1rem;
+        transition: all 0.3s ease;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.2);
+        width: 100%;
+    }
+    
+    .stButton > button:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 6px 20px rgba(0,0,0,0.3);
+    }
+    
+    .sidebar .stSelectbox > div > div {
+        background: linear-gradient(135deg, #74b9ff 0%, #0984e3 100%);
+        color: white;
+    }
+    
+    /* Constrain image height to prevent scrolling */
+    .stImage > img {
+        max-height: 400px;
+        width: auto;
+        object-fit: contain;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+@st.cache_resource
+def load_fire_model():
+    """Load the trained fire detection model"""
+    model_path = 'models/fire_detection_classifier.pth'
+    
+    if not os.path.exists(model_path):
+        return None, "Model not found. Please train the model first."
+    
+    try:
+        model, model_info = load_model(model_path, device='cpu')
+        return model, model_info
+    except Exception as e:
+        return None, f"Error loading model: {str(e)}"
+
+def get_prediction(image, model, transform):
+    """Get prediction from the model"""
+    try:
+        # Prepare image
+        input_tensor = prepare_image_for_inference(image, transform)
+        
+        # Get prediction
+        with torch.no_grad():
+            model.eval()
+            outputs = model(input_tensor)
+            probabilities = F.softmax(outputs, dim=1)
+            confidence, predicted = torch.max(probabilities, 1)
+            
+            # Convert to numpy
+            predicted_class = predicted.item()
+            confidence_score = confidence.item()
+            all_probs = probabilities.squeeze().cpu().numpy()
+            
+            return predicted_class, confidence_score, all_probs
+    
+    except Exception as e:
+        st.error(f"Error during prediction: {str(e)}")
+        return None, None, None
+
+def create_confidence_chart(probabilities, class_names):
+    """Create confidence chart using Plotly"""
+    fig = go.Figure(data=[
+        go.Bar(
+            x=class_names,
+            y=probabilities,
+            marker_color=['#dc3545', '#28a745'],
+            text=[f'{p:.1%}' for p in probabilities],
+            textposition='auto',
+        )
+    ])
+    
+    fig.update_layout(
+        title="Fire Detection Confidence",
+        xaxis_title="Prediction",
+        yaxis_title="Confidence",
+        yaxis=dict(range=[0, 1]),
+        showlegend=False,
+        height=400,
+        template="plotly_white"
+    )
+    
+    return fig
+
+def create_safety_metrics_chart(predicted_class, confidence):
+    """Create safety metrics visualization"""
+    if predicted_class == 0:  # Fire
+        danger_level = confidence * 100
+        safety_level = (1 - confidence) * 100
+        primary_color = '#dc3545'
+        status = "FIRE DETECTED"
+    else:  # No Fire
+        danger_level = (1 - confidence) * 100
+        safety_level = confidence * 100
+        primary_color = '#28a745'
+        status = "NO FIRE"
+    
+    fig = go.Figure(go.Indicator(
+        mode = "gauge+number+delta",
+        value = danger_level,
+        domain = {'x': [0, 1], 'y': [0, 1]},
+        title = {'text': "Fire Risk Level"},
+        delta = {'reference': 50},
+        gauge = {'axis': {'range': [None, 100]},
+                'bar': {'color': primary_color},
+                'steps' : [
+                    {'range': [0, 25], 'color': "lightgray"},
+                    {'range': [25, 50], 'color': "yellow"},
+                    {'range': [50, 75], 'color': "orange"},
+                    {'range': [75, 100], 'color': "red"}],
+                'threshold' : {'line': {'color': "red", 'width': 4},
+                              'thickness': 0.75, 'value': 90}}))
+    
+    fig.update_layout(height=400)
+    return fig
+
+def analyze_fire_risk(predicted_class, confidence):
+    """Analyze fire risk and provide recommendations"""
+    if predicted_class == 0:  # Fire detected
+        risk_level = confidence * 100
+        
+        if risk_level >= 90:
+            return {
+                'level': 'CRITICAL',
+                'color': '#dc3545',
+                'icon': '🚨',
+                'message': 'IMMEDIATE ACTION REQUIRED',
+                'recommendations': [
+                    'Activate fire suppression system immediately',
+                    'Evacuate the area',
+                    'Call emergency services UAE 997',
+                    'Shut down affected equipment if safe to do so',
+                    'Monitor surrounding areas for spread'
+                ]
+            }
+        elif risk_level >= 75:
+            return {
+                'level': 'HIGH',
+                'color': '#fd7e14',
+                'icon': '⚠️',
+                'message': 'HIGH FIRE RISK DETECTED',
+                'recommendations': [
+                    'Investigate the area immediately',
+                    'Prepare fire suppression systems',
+                    'Alert security personnel',
+                    'Consider equipment shutdown',
+                    'Increase monitoring frequency'
+                ]
+            }
+        else:
+            return {
+                'level': 'MODERATE',
+                'color': '#ffc107',
+                'icon': '🔶',
+                'message': 'POSSIBLE FIRE DETECTED',
+                'recommendations': [
+                    'Verify with additional sensors',
+                    'Send personnel to investigate',
+                    'Check equipment temperatures',
+                    'Review recent maintenance logs',
+                    'Maintain heightened awareness'
+                ]
+            }
+    else:  # No fire
+        safety_level = confidence * 100
+        
+        if safety_level >= 95:
+            return {
+                'level': 'SAFE',
+                'color': '#28a745',
+                'icon': '✅',
+                'message': 'NORMAL OPERATION',
+                'recommendations': [
+                    'Continue normal operations',
+                    'Maintain regular monitoring',
+                    'Keep fire suppression systems ready',
+                    'Perform scheduled maintenance',
+                    'Review safety protocols periodically'
+                ]
+            }
+        else:
+            return {
+                'level': 'CAUTION',
+                'color': '#17a2b8',
+                'icon': '⚠️',
+                'message': 'MONITOR CLOSELY',
+                'recommendations': [
+                    'Increase monitoring frequency',
+                    'Check for unusual conditions',
+                    'Verify sensor functionality',
+                    'Review environmental factors',
+                    'Maintain readiness for action'
+                ]
+            }
+
+# display_fire_safety_checklist function removed - content moved to right column
+
+def main():
+    """Main application function"""
+    # Header
+    st.markdown("""
+    <div class="main-header">
+        <h1>🔥 zeroFire</h1>
+        <p>AI-Powered Fire Detection System for Data Centers</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Sidebar
+    with st.sidebar:
+        st.markdown("### 🔥 Fire Detection System")
+        st.markdown("---")
+        
+        # Model status
+        model, model_info = load_fire_model()
+        
+        if model is None:
+            st.error("❌ Model not available")
+            st.info("Train the model first using: `python train_fire_detection.py`")
+            return
+        else:
+            st.success("✅ Model loaded successfully")
+            if isinstance(model_info, dict):
+                accuracy = model_info.get('best_acc', 'Unknown')
+                if accuracy != 'Unknown':
+                    # Format accuracy to 2 decimal places
+                    accuracy_formatted = f"{float(accuracy):.2f}%"
+                    st.info(f"📊 Model: ConvNeXt Large")
+                    st.info(f"🎯 Accuracy: {accuracy_formatted}")
+                    st.info(f"🔄 Transfer Learning: Food→Fire")
+                    st.info(f"⚡ Precision: High-recall optimized")
+                else:
+                    st.info("📊 Model Accuracy: Unknown")
+        
+        st.markdown("---")
+        
+        # Settings
+        st.markdown("### ⚙️ Settings")
+        confidence_threshold = st.slider(
+            "Confidence Threshold",
+            min_value=0.0,
+            max_value=1.0,
+            value=0.5,
+            step=0.05,
+            help="Minimum confidence required for fire detection"
+        )
+        
+        show_details = st.checkbox("Show detailed analysis", value=True)
+        
+        st.markdown("---")
+        st.markdown("### 📊 Data Center Status")
+        check_data_directory('data')
+    
+    # Main content
+    col1, col2 = st.columns([2, 1])
+    
+    with col1:
+        # Upload section
+        st.markdown("""
+        <div class="upload-section">
+            <h3>📸 Upload Data Center Image</h3>
+            <p>Upload an image to detect fire or smoke in your data center</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        uploaded_file = st.file_uploader(
+            "Choose an image...",
+            type=['jpg', 'jpeg', 'png', 'bmp', 'tiff'],
+            help="Upload an image from your data center for fire detection"
+        )
+        
+        if uploaded_file is not None:
+            # Display uploaded image
+            image = Image.open(uploaded_file)
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+            
+            # Get prediction
+            transform = get_inference_transform()
+            predicted_class, confidence_score, all_probs = get_prediction(image, model, transform)
+            
+            if predicted_class is not None:
+                class_names = ['Fire', 'No Fire']
+                predicted_label = class_names[predicted_class]
+                
+                # Fire detection result moved to right column
+                
+                # Technical details (keep only this in left column)
+                with st.expander("🔬 Technical Details"):
+                    st.markdown(f"""
+                    **Prediction Details:**
+                    - Predicted Class: {predicted_label}
+                    - Confidence Score: {confidence_score:.4f}
+                    - Fire Probability: {all_probs[0]:.4f}
+                    - No-Fire Probability: {all_probs[1]:.4f}
+                    - Threshold: {confidence_threshold:.2f}
+                    """)
+    
+    with col2:
+        # Display fire detection result first
+        if 'predicted_class' in locals():
+            # Display fire detection result box
+            if predicted_class == 0:  # Fire
+                st.markdown(f"""
+                <div class="result-fire">
+                    <h2>🚨 FIRE DETECTED - Confidence: {confidence_score:.1%}</h2>
+                    <p>IMMEDIATE ACTION REQUIRED</p>
+                </div>
+                """, unsafe_allow_html=True)
+            else:  # No Fire
+                st.markdown(f"""
+                <div class="result-no-fire">
+                    <h2>✅ NO FIRE DETECTED - Confidence: {confidence_score:.1%}</h2>
+                    <p>Normal Operation</p>
+                </div>
+                """, unsafe_allow_html=True)
+        
+        # Quick metrics
+        st.markdown("### 📊 Quick Metrics")
+        
+        if 'predicted_class' in locals():
+            # Create three columns for metrics in a row
+            metric_col1, metric_col2, metric_col3 = st.columns(3)
+            
+            with metric_col1:
+                # Fire risk gauge
+                risk_percentage = all_probs[0] * 100
+                st.metric(
+                    label="Fire Risk",
+                    value=f"{risk_percentage:.1f}%"
+                )
+            
+            with metric_col2:
+                # Safety score
+                safety_score = all_probs[1] * 100
+                st.metric(
+                    label="Safety Score",
+                    value=f"{safety_score:.1f}%"
+                )
+            
+            with metric_col3:
+                # Model status instead of redundant confidence
+                status = "FIRE ALERT" if predicted_class == 0 else "NORMAL"
+                st.metric(
+                    label="Status",
+                    value=status
+                )
+            
+                         # Charts removed to eliminate confusion and redundancy
+            
+            # Detailed analysis moved from left column
+            if show_details:
+                st.markdown("### 📋 Detailed Analysis")
+                
+                analysis = analyze_fire_risk(predicted_class, confidence_score)
+                
+                # Use red styling only for fire detection
+                card_class = "info-card-fire" if predicted_class == 0 else "info-card"
+                
+                st.markdown(f"""
+                <div class="{card_class}">
+                    <h3>{analysis['icon']} Risk Level: {analysis['level']}</h3>
+                    <p><strong>{analysis['message']}</strong></p>
+                </div>
+                """, unsafe_allow_html=True)
+                
+                st.markdown("#### 🎯 Recommended Actions:")
+                for rec in analysis['recommendations']:
+                    st.markdown(f"- {rec}")
+                
+                # Fire Safety Checklist moved here
+                st.markdown("---")
+                st.markdown("""
+                <div class="info-card">
+                    <h3>🔥 Fire Safety Checklist</h3>
+                    <p>Essential fire safety measures for data centers:</p>
+                </div>
+                """, unsafe_allow_html=True)
+                
+                safety_items = [
+                    "🔥 **Fire Detection Systems** - Smoke, heat, and flame detectors",
+                    "💨 **Suppression Systems** - Clean agent, water mist, or CO2",
+                    "🌡️ **Temperature Monitoring** - Continuous thermal monitoring",
+                    "⚡ **Electrical Safety** - Arc fault and ground fault protection",
+                    "🚪 **Emergency Exits** - Clear and well-marked escape routes",
+                    "📋 **Emergency Procedures** - Staff training and evacuation plans",
+                    "🔧 **Equipment Maintenance** - Regular inspection and testing",
+                    "📞 **Emergency Contacts** - Quick access to fire department",
+                    "🎯 **Response Plans** - Pre-defined actions for different scenarios",
+                    "📊 **Documentation** - Incident logging and safety records"
+                ]
+                
+                for item in safety_items:
+                    st.markdown(f"- {item}")
+        
+        # Additional info moved to bottom
+    
+    # Footer
+    st.markdown("---")
+    
+    # Emergency Contacts only (Fire Safety Checklist moved to right column)
+    st.markdown("""
+    <div class="info-card" style="padding: 15px;">
+        <h3 style="margin: 0 0 10px 0; text-align: center;">🚨 Emergency Contacts</h3>
+        <div style="display: flex; justify-content: space-around; flex-wrap: wrap; gap: 15px;">
+            <span><strong>Fire Dept:</strong> UAE 997</span>
+            <span><strong>Security:</strong> [Your Number]</span>
+            <span><strong>Facilities:</strong> [Your Number]</span>
+            <span><strong>IT Ops:</strong> [Your Number]</span>
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    st.markdown("---")
+    st.markdown("""
+    <div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #a8e6cf 0%, #74b9ff 100%); border-radius: 15px; color: white;">
+        <p>🔥 zeroFire - AI-Powered Fire Detection System</p>
+        <p>Protecting your data center with advanced machine learning</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()