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wesam0099 commited on Jan 4

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1512569

1 Parent(s): 5f6c42f

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.gitattributes +0 -1
Dockerfile +8 -8
README.md +0 -28
app.py +1061 -123
app_FIXED.py +0 -169
models/__init__.py +21 -0
models/mindspore_loader.py +467 -0
models/mindspore_model.py +737 -0
models/trained/best_accident_model.ckpt +3 -0
output/reports/accident_report_20251230_084121.html +688 -0
output/reports/accident_report_20251230_091851.html +688 -0
output/reports/accident_report_20251230_124847.html +688 -0
output/reports/accident_report_20251231_092601.html +688 -0
requirements.txt +18 -18
ui/map_viewer.py +307 -21

.gitattributes CHANGED Viewed

@@ -14,4 +14,3 @@
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 # Documentation
 *.pdf filter=lfs diff=lfs merge=lfs -text
-models/trained/*.onnx filter=lfs diff=lfs merge=lfs -text

 *.mp4 filter=lfs diff=lfs merge=lfs -text
 # Documentation
 *.pdf filter=lfs diff=lfs merge=lfs -text

Dockerfile CHANGED Viewed

@@ -23,11 +23,11 @@ RUN mkdir -p output/reports logs
 # Expose port 7860 for Hugging Face Spaces
 EXPOSE 7860
-# Run streamlit with proper configuration
-CMD streamlit run app.py \
-    --server.port=7860 \
-    --server.address=0.0.0.0 \
-    --server.headless=true \
-    --server.runOnSave=false \
-    --server.fileWatcherType=none \
-    --browser.gatherUsageStats=false

 # Expose port 7860 for Hugging Face Spaces
 EXPOSE 7860
+# Environment variables
+ENV STREAMLIT_SERVER_PORT=7860
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
+ENV STREAMLIT_SERVER_HEADLESS=true
+ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+# Run the app
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

README.md DELETED Viewed

@@ -1,28 +0,0 @@
----
-title: CrashLens AI Accident Analyzer
-emoji: 🚗💥
-colorFrom: blue
-colorTo: indigo
-sdk: docker
-app_port: 7860
----
-# 🚗 CrashLens - AI Traffic Accident Reconstruction
-**Huawei AI Innovation Challenge 2026**
-AI-powered traffic accident analysis system using MindSpore framework.
-## Features
-- 🧠 AI collision type prediction
-- 📊 Fault percentage calculation
-- 🗺️ Interactive accident mapping
-- 📈 2D animated simulations
-- 📄 Comprehensive HTML reports
-## Technology
-- **AI**: MindSpore → ONNX
-- **Framework**: Streamlit
-- **Institution**: Jubail Industrial College
-Built by Wes - AI Engineer

app.py CHANGED Viewed

@@ -1,169 +1,1107 @@
 """
-CrashLens - Fixed Version (No Rerun Loops)
 """
 import streamlit as st
 import sys
 from pathlib import Path
 sys.path.insert(0, str(Path(__file__).parent))
 st.set_page_config(
-    page_title="CrashLens AI",
-    page_icon="🚗",
-    layout="wide"
 )
-# Initialize session state
-if 'analysis_done' not in st.session_state:
-    st.session_state.analysis_done = False
-if 'results' not in st.session_state:
-    st.session_state.results = None
-# Header
-st.title("🚗 CrashLens - AI Traffic Accident Analyzer")
-st.markdown("**Huawei AI Innovation Challenge 2026** | Jubail Industrial College")
-st.markdown("---")
-# Sidebar
-with st.sidebar:
-    st.image("assets/mindspore_logo.png", width=200)
-    st.markdown("### 🧠 Powered by MindSpore AI")
-    st.markdown("---")
-    st.markdown("### 📋 How to Use")
     st.markdown("""
-    1. Enter accident location
-    2. Add vehicle details
-    3. Click Analyze
-    4. View AI predictions
-    """)
-# Main content - Use tabs instead of steps
-tab1, tab2, tab3 = st.tabs(["📍 Location", "🚗 Vehicles", "🔬 Analysis"])
-with tab1:
-    st.subheader("Accident Location")
-    col1, col2 = st.columns(2)
     with col1:
-        location_name = st.text_input("Location Name", "Seef District Roundabout")
-        latitude = st.number_input("Latitude", value=26.2397, format="%.4f")
-        longitude = st.number_input("Longitude", value=50.5369, format="%.4f")
     with col2:
-        weather = st.selectbox("Weather", ["clear", "cloudy", "rainy", "foggy"])
-        road_condition = st.selectbox("Road Condition", ["dry", "wet", "sandy", "oily"])
-        road_type = st.selectbox("Road Type", ["roundabout", "crossroad", "highway", "urban_road"])
-    st.success("✅ Location configured")
-with tab2:
-    st.subheader("Vehicle Information")
     col1, col2 = st.columns(2)
     with col1:
-        st.markdown("**🚗 Vehicle 1 (Red)**")
-        v1_type = st.selectbox("Type", ["sedan", "suv", "truck"], key="v1_type")
-        v1_speed = st.slider("Speed (km/h)", 0, 120, 50, key="v1_speed")
-        v1_direction = st.selectbox("Direction", ["north", "south", "east", "west"], key="v1_dir")
-        v1_action = st.selectbox("Action", ["going_straight", "turning_left", "turning_right"], key="v1_action")
     with col2:
-        st.markdown("**🚙 Vehicle 2 (Blue)**")
-        v2_type = st.selectbox("Type", ["sedan", "suv", "truck"], key="v2_type")
-        v2_speed = st.slider("Speed (km/h)", 0, 120, 50, key="v2_speed")
-        v2_direction = st.selectbox("Direction", ["north", "south", "east", "west"], key="v2_dir")
-        v2_action = st.selectbox("Action", ["going_straight", "turning_left", "turning_right"], key="v2_action")
-    st.success("✅ Vehicles configured")
-with tab3:
-    st.subheader("AI Analysis")
-    if st.button("🧠 Run AI Analysis", type="primary", use_container_width=True):
-        with st.spinner("Analyzing accident scenario..."):
             try:
-                # Prepare data
-                accident_info = {
-                    'location': {'name': location_name, 'lat': latitude, 'lng': longitude},
-                    'weather': weather,
-                    'road_condition': road_condition,
-                    'road_type': road_type,
-                    'visibility': 1.0,
-                    'lighting': 'daylight'
-                }
-                vehicle_1 = {
-                    'type': v1_type,
-                    'speed': v1_speed,
-                    'direction': v1_direction,
-                    'action': v1_action,
-                    'braking': False,
-                    'signaling': False
-                }
-                vehicle_2 = {
-                    'type': v2_type,
-                    'speed': v2_speed,
-                    'direction': v2_direction,
-                    'action': v2_action,
-                    'braking': False,
-                    'signaling': False
-                }
-                # Run analysis
                 from analysis.scenario_analyzer import analyze_accident
                 results = analyze_accident(
-                    accident_info=accident_info,
-                    vehicle_1=vehicle_1,
-                    vehicle_2=vehicle_2
                 )
-                st.session_state.results = results
-                st.session_state.analysis_done = True
             except Exception as e:
-                st.error(f"Analysis error: {e}")
-                import traceback
-                with st.expander("Error Details"):
-                    st.code(traceback.format_exc())
-    # Display results
-    if st.session_state.analysis_done and st.session_state.results:
-        st.success("✅ Analysis Complete!")
-        results = st.session_state.results
-        # Show collision type
-        st.markdown("### 🎯 Predicted Collision Type")
-        collision_type = results.get('collision_type', 'Unknown')
-        st.info(f"**{collision_type.replace('_', ' ').title()}**")
-        # Show scenarios
-        st.markdown("### 📊 Scenarios")
-        scenarios = results.get('scenarios', [])
         for i, scenario in enumerate(scenarios[:3], 1):
-            with st.expander(f"Scenario {i}: {scenario.get('type', 'Unknown').replace('_', ' ').title()}"):
-                col1, col2 = st.columns(2)
-                with col1:
-                    st.metric("Probability", f"{scenario.get('probability', 0)*100:.1f}%")
-                    st.write("**Fault Distribution:**")
-                    st.write(f"- Vehicle 1: {scenario.get('fault_percentage_1', 50)}%")
-                    st.write(f"- Vehicle 2: {scenario.get('fault_percentage_2', 50)}%")
-                with col2:
-                    # Show animation if exists
-                    gif_path = f"output/visualizations/{scenario.get('type', 'head_on_collision')}.gif"
-                    if Path(gif_path).exists():
-                        st.image(gif_path, caption="Simulation")
-        # Download report
-        if st.button("📄 Download Report"):
-            st.info("Report generation coming soon!")
-st.markdown("---")
-st.markdown("© 2026 Jubail Industrial College | Huawei AI Innovation Challenge")

 """
+Traffic Accident Reconstruction System
+======================================
+Main Streamlit Application
+Huawei AI Innovation Challenge 2026
+This system uses MindSpore AI to analyze traffic accidents
+and generate probable scenarios with 2D simulation.
 """
 import streamlit as st
 import sys
 from pathlib import Path
+# Add project root to path
 sys.path.insert(0, str(Path(__file__).parent))
+from config import STREAMLIT_CONFIG, CASE_STUDY_LOCATION, COLORS
+from ui.components import render_sidebar, render_header, render_footer
+from ui.map_viewer import render_map_section
+from ui.vehicle_input import render_vehicle_input
+from ui.party_input import render_party_input, render_evidence_upload, render_party_summary
+from ui.results_display import render_results
+# ============================================================
+# PAGE CONFIGURATION
+# ============================================================
 st.set_page_config(
+    page_title=STREAMLIT_CONFIG["page_title"],
+    page_icon=STREAMLIT_CONFIG["page_icon"],
+    layout=STREAMLIT_CONFIG["layout"],
+    initial_sidebar_state=STREAMLIT_CONFIG["initial_sidebar_state"]
 )
+# ============================================================
+# CUSTOM CSS - CrashLens Modern Design
+# ============================================================
+st.markdown("""
+<style>
+    /* Layout - Dark theme with modern spacing */
+    .block-container {
+        padding-top: 1.4rem;
+        padding-bottom: 2.5rem;
+        max-width: 1200px;
+    }
+    [data-testid="stSidebar"] {
+        min-width: 340px;
+        max-width: 340px;
+        background: linear-gradient(180deg, #0e1117 0%, #1a1f2e 100%);
+    }
+    /* Main container with gradient background */
+    .main {
+        background: linear-gradient(135deg, #0a0e27 0%, #1a1f3a 50%, #0f1419 100%);
+        color: rgba(255, 255, 255, 0.95);
+    }
+    /* Header styling - Modern minimal */
+    .main-header {
+        background: linear-gradient(135deg, rgba(30, 58, 95, 0.3) 0%, rgba(45, 90, 135, 0.2) 100%);
+        padding: 2rem;
+        border-radius: 16px;
+        border: 1px solid rgba(255, 255, 255, 0.12);
+        color: white;
+        margin-bottom: 2rem;
+        text-align: center;
+        box-shadow: 0 10px 40px rgba(0, 0, 0, 0.3);
+    }
+    .main-header h1 {
+        margin: 0;
+        font-size: 2.8rem;
+        font-weight: 800;
+        letter-spacing: 0.5px;
+        background: linear-gradient(135deg, #60a5fa 0%, #3b82f6 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+    }
+    .main-header p {
+        margin: 0.8rem 0 0 0;
+        opacity: 0.75;
+        font-size: 1.05rem;
+        color: rgba(255, 255, 255, 0.72);
+    }
+    /* Card styling - Glass morphism effect */
+    .info-card {
+        background: rgba(255, 255, 255, 0.04);
+        backdrop-filter: blur(10px);
+        padding: 1.5rem;
+        border-radius: 16px;
+        border: 1px solid rgba(255, 255, 255, 0.12);
+        margin: 1rem 0;
+        box-shadow: 0 10px 30px rgba(0, 0, 0, 0.15);
+    }
+    /* Step indicator - Modern pills */
+    .step-indicator {
+        display: flex;
+        justify-content: space-between;
+        margin: 2rem 0;
+        gap: 0.5rem;
+    }
+    .step {
+        flex: 1;
+        text-align: center;
+        padding: 1rem;
+        background: rgba(255, 255, 255, 0.05);
+        border: 1px solid rgba(255, 255, 255, 0.1);
+        margin: 0;
+        border-radius: 12px;
+        position: relative;
+        transition: all 0.3s ease;
+    }
+    .step.active {
+        background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%);
+        border-color: #3b82f6;
+        color: white;
+        box-shadow: 0 8px 24px rgba(59, 130, 246, 0.3);
+    }
+    .step.completed {
+        background: linear-gradient(135deg, #10b981 0%, #059669 100%);
+        border-color: #10b981;
+        color: white;
+    }
+    /* Vehicle cards - Dark theme */
+    .vehicle-card {
+        background: rgba(255, 255, 255, 0.06);
+        border: 1px solid rgba(255, 255, 255, 0.15);
+        border-radius: 12px;
+        padding: 1.5rem;
+        margin: 1rem 0;
+    }
+    .vehicle-card.vehicle-1 {
+        border-color: rgba(255, 75, 75, 0.4);
+        background: rgba(255, 75, 75, 0.08);
+    }
+    .vehicle-card.vehicle-2 {
+        border-color: rgba(75, 123, 255, 0.4);
+        background: rgba(75, 123, 255, 0.08);
+    }
+    /* Results styling - Modern cards */
+    .scenario-card {
+        background: rgba(255, 255, 255, 0.06);
+        border-radius: 12px;
+        padding: 1.5rem;
+        margin: 1rem 0;
+        box-shadow: 0 4px 12px rgba(0,0,0,0.2);
+        border: 1px solid rgba(255, 255, 255, 0.1);
+    }
+    .probability-high {
+        color: #10b981;
+        font-weight: bold;
+    }
+    .probability-medium {
+        color: #f59e0b;
+        font-weight: bold;
+    }
+    .probability-low {
+        color: #ef4444;
+        font-weight: bold;
+    }
+    /* Button styling - Modern gradient */
+    .stButton > button {
+        width: 100%;
+        background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%);
+        color: white;
+        border: none;
+        padding: 0.85rem 1.5rem;
+        border-radius: 12px;
+        font-weight: 600;
+        font-size: 1rem;
+        transition: all 0.3s ease;
+        box-shadow: 0 4px 12px rgba(59, 130, 246, 0.3);
+    }
+    .stButton > button:hover {
+        background: linear-gradient(135deg, #2563eb 0%, #1d4ed8 100%);
+        box-shadow: 0 6px 16px rgba(59, 130, 246, 0.4);
+        transform: translateY(-2px);
+    }
+    /* Input fields - Dark theme with better contrast */
+    .stTextInput > div > div > input,
+    .stTextArea > div > div > textarea,
+    .stSelectbox > div > div > select,
+    .stNumberInput > div > div > input {
+        background: rgba(255, 255, 255, 0.08) !important;
+        border: 1px solid rgba(255, 255, 255, 0.15) !important;
+        border-radius: 10px !important;
+        color: white !important;
+        padding: 0.75rem !important;
+    }
+    .stTextInput > div > div > input:focus,
+    .stTextArea > div > div > textarea:focus,
+    .stSelectbox > div > div > select:focus,
+    .stNumberInput > div > div > input:focus {
+        border-color: #3b82f6 !important;
+        box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.15) !important;
+    }
+    /* Labels - Better visibility */
+    label, .stTextInput > label, .stTextArea > label, .stSelectbox > label, .stSlider > label, .stNumberInput > label {
+        color: rgba(255, 255, 255, 0.85) !important;
+        font-weight: 500 !important;
+        font-size: 0.95rem !important;
+    }
+    /* Metric cards - Modern design */
+    [data-testid="stMetricValue"] {
+        font-size: 2.2rem;
+        font-weight: 700;
+        color: white;
+    }
+    [data-testid="stMetricLabel"] {
+        color: rgba(255, 255, 255, 0.7);
+        font-size: 0.95rem;
+        font-weight: 500;
+    }
+    /* Tabs styling - Modern */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 8px;
+        background: rgba(255, 255, 255, 0.03);
+        padding: 0.5rem;
+        border-radius: 12px;
+    }
+    .stTabs [data-baseweb="tab"] {
+        background: transparent;
+        border-radius: 8px;
+        color: rgba(255, 255, 255, 0.7);
+        font-weight: 500;
+        padding: 0.75rem 1.5rem;
+        border: 1px solid transparent;
+    }
+    .stTabs [aria-selected="true"] {
+        background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%);
+        color: white !important;
+        border-color: #3b82f6;
+    }
+    /* Messages - Modern alerts */
+    .stSuccess, .element-container:has(.stSuccess) {
+        background: rgba(16, 185, 129, 0.15) !important;
+        border: 1px solid rgba(16, 185, 129, 0.3) !important;
+        border-radius: 12px !important;
+        padding: 1rem !important;
+    }
+    .stSuccess [data-testid="stMarkdownContainer"] p {
+        color: #10b981 !important;
+    }
+    .stError {
+        background: rgba(239, 68, 68, 0.15) !important;
+        border: 1px solid rgba(239, 68, 68, 0.3) !important;
+        border-radius: 12px !important;
+    }
+    .stWarning {
+        background: rgba(245, 158, 11, 0.15) !important;
+        border: 1px solid rgba(245, 158, 11, 0.3) !important;
+        border-radius: 12px !important;
+    }
+    .stInfo {
+        background: rgba(59, 130, 246, 0.15) !important;
+        border: 1px solid rgba(59, 130, 246, 0.3) !important;
+        border-radius: 12px !important;
+    }
+    /* Sidebar - Better styling */
+    [data-testid="stSidebar"] h1, [data-testid="stSidebar"] h2, [data-testid="stSidebar"] h3 {
+        color: white !important;
+    }
+    [data-testid="stSidebar"] p, [data-testid="stSidebar"] span {
+        color: rgba(255, 255, 255, 0.85) !important;
+    }
+    /* Expander - Modern style */
+    .streamlit-expanderHeader {
+        background: rgba(255, 255, 255, 0.05) !important;
+        border-radius: 10px !important;
+        border: 1px solid rgba(255, 255, 255, 0.1) !important;
+        color: white !important;
+        font-weight: 500 !important;
+    }
+    .streamlit-expanderContent {
+        background: rgba(255, 255, 255, 0.02) !important;
+        border: 1px solid rgba(255, 255, 255, 0.08) !important;
+        border-radius: 0 0 10px 10px !important;
+    }
+    /* Checkbox and Radio - Better visibility */
+    .stCheckbox label, .stRadio label {
+        color: rgba(255, 255, 255, 0.85) !important;
+    }
+    /* Slider - Modern */
+    .stSlider [data-baseweb="slider"] {
+        background: rgba(59, 130, 246, 0.2);
+    }
+    /* Headings - Better hierarchy */
+    h1, h2, h3, h4, h5, h6 {
+        color: white !important;
+    }
+    h1 {
+        font-weight: 800 !important;
+        letter-spacing: -0.5px;
+    }
+    h2 {
+        font-weight: 700 !important;
+        letter-spacing: -0.3px;
+    }
+    h3 {
+        font-weight: 600 !important;
+    }
+    /* Markdown text - Better readability */
+    p, span, div {
+        color: rgba(255, 255, 255, 0.9);
+    }
+    /* Code blocks - Dark theme */
+    code {
+        background: rgba(255, 255, 255, 0.08) !important;
+        color: #60a5fa !important;
+        padding: 0.2rem 0.4rem !important;
+        border-radius: 4px !important;
+    }
+    /* Dataframe - Dark theme */
+    [data-testid="stDataFrame"] {
+        background: rgba(255, 255, 255, 0.05);
+        border-radius: 12px;
+        border: 1px solid rgba(255, 255, 255, 0.1);
+    }
+    /* Hide Streamlit branding */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    /* Progress bar */
+    .stProgress > div > div {
+        background: linear-gradient(90deg, #3b82f6 0%, #8b5cf6 100%);
+        border-radius: 8px;
+    }
+</style>
+""", unsafe_allow_html=True)
+# ============================================================
+# SESSION STATE INITIALIZATION
+# ============================================================
+def init_session_state():
+    """Initialize all session state variables."""
+    # Application state
+    if 'current_step' not in st.session_state:
+        st.session_state.current_step = 1
+    # Accident information
+    if 'accident_info' not in st.session_state:
+        st.session_state.accident_info = {
+            'location': CASE_STUDY_LOCATION.copy(),
+            'datetime': None,
+            'road_type': 'roundabout',
+            'weather': 'clear',
+            'road_condition': 'dry',
+            'visibility': 1.0,
+            'lighting': 'daylight',
+            'notes': ''
+        }
+    # Vehicle 1 data (for analysis)
+    if 'vehicle_1' not in st.session_state:
+        st.session_state.vehicle_1 = {
+            'type': 'sedan',
+            'speed': 50,
+            'direction': 'north',
+            'action': 'entering_roundabout',
+            'braking': False,
+            'signaling': False,
+            'lights_on': True,
+            'horn_used': False,
+            'path': [],
+            'description': ''
+        }
+    # Vehicle 2 data (for analysis)
+    if 'vehicle_2' not in st.session_state:
+        st.session_state.vehicle_2 = {
+            'type': 'sedan',
+            'speed': 50,
+            'direction': 'east',
+            'action': 'going_straight',
+            'braking': False,
+            'signaling': False,
+            'lights_on': True,
+            'horn_used': False,
+            'path': [],
+            'description': ''
+        }
+    # Party 1 data (driver details)
+    if 'party_1' not in st.session_state:
+        st.session_state.party_1 = {
+            'full_name': '',
+            'id_iqama': '',
+            'phone': '',
+            'role': 'Driver',
+            'vehicle_make_model': '',
+            'plate_number': '',
+            'insurance': '',
+            'damage_notes': '',
+            'statement': ''
+        }
+    # Party 2 data (driver details)
+    if 'party_2' not in st.session_state:
+        st.session_state.party_2 = {
+            'full_name': '',
+            'id_iqama': '',
+            'phone': '',
+            'role': 'Driver',
+            'vehicle_make_model': '',
+            'plate_number': '',
+            'insurance': '',
+            'damage_notes': '',
+            'statement': ''
+        }
+    # Evidence photos
+    if 'evidence_photos' not in st.session_state:
+        st.session_state.evidence_photos = []
+    # Analysis results
+    if 'analysis_results' not in st.session_state:
+        st.session_state.analysis_results = None
+    # Generated scenarios
+    if 'scenarios' not in st.session_state:
+        st.session_state.scenarios = []
+    # Map data
+    if 'map_data' not in st.session_state:
+        st.session_state.map_data = None
+    # Analysis ready flag
+    if 'analysis_ready' not in st.session_state:
+        st.session_state.analysis_ready = False
+# ============================================================
+# MAIN APPLICATION
+# ============================================================
+def main():
+    """Main application entry point."""
+    # Initialize session state
+    init_session_state()
+    # Render modern header - clean without logo
+    col1, col2, col3 = st.columns([1, 6, 1])
+    with col1:
+        st.markdown("## 🚗")
+    with col2:
+        st.markdown("""
+        <div style='padding-top: 0.5rem;'>
+            <h1 style='margin: 0; font-size: 2.5rem; font-weight: 800; background: linear-gradient(135deg, #60a5fa 0%, #3b82f6 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent;'>CrashLens AI</h1>
+            <p style='margin: 0.3rem 0 0 0; color: rgba(255, 255, 255, 0.7); font-size: 1rem;'>Traffic Accident Scenario Analysis • MindSpore-ready • PDF report</p>
+        </div>
+        """, unsafe_allow_html=True)
+    with col3:
+        st.write("")
+    st.markdown("<br>", unsafe_allow_html=True)
+    # Render sidebar
+    render_sidebar()
+    # Modern step indicator with pills - Using columns to avoid HTML rendering issues
+    steps = ["📍 Location", "🚙 Vehicle 1", "🚗 Vehicle 2", "👥 Parties", "📄 Evidence", "🔍 Analysis", "📊 Results"]
+    cols = st.columns(len(steps))
+    for i, (col, step_name) in enumerate(zip(cols, steps), 1):
+        with col:
+            if i < st.session_state.current_step:
+                # Completed step - Green
+                st.markdown(f"""
+                <div style='text-align: center; padding: 0.75rem 0.25rem;
+                    background: linear-gradient(135deg, #10b981 0%, #059669 100%);
+                    color: white; border-radius: 10px; font-size: 0.8rem; font-weight: 600;
+                    box-shadow: 0 4px 12px rgba(16, 185, 129, 0.3);'>
+                    ✓ {step_name.split()[-1]}
+                </div>
+                """, unsafe_allow_html=True)
+            elif i == st.session_state.current_step:
+                # Active step - Blue gradient
+                st.markdown(f"""
+                <div style='text-align: center; padding: 0.75rem 0.25rem;
+                    background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%);
+                    color: white; border-radius: 10px; font-size: 0.8rem; font-weight: 600;
+                    box-shadow: 0 6px 16px rgba(59, 130, 246, 0.4);
+                    border: 2px solid rgba(96, 165, 250, 0.5);'>
+                    {step_name}
+                </div>
+                """, unsafe_allow_html=True)
+            else:
+                # Future step - Dark gray
+                st.markdown(f"""
+                <div style='text-align: center; padding: 0.75rem 0.25rem;
+                    background: rgba(255, 255, 255, 0.05);
+                    color: rgba(255, 255, 255, 0.4); border-radius: 10px; font-size: 0.8rem;
+                    border: 1px solid rgba(255, 255, 255, 0.1);'>
+                    {step_name.split()[-1]}
+                </div>
+                """, unsafe_allow_html=True)
+    st.markdown("<div style='margin: 1.5rem 0; height: 1px; background: linear-gradient(90deg, transparent, rgba(255,255,255,0.1), transparent);'></div>", unsafe_allow_html=True)
+    # Main content based on current step
+    if st.session_state.current_step == 1:
+        render_step_1_location()
+    elif st.session_state.current_step == 2:
+        render_step_2_vehicle1()
+    elif st.session_state.current_step == 3:
+        render_step_3_vehicle2()
+    elif st.session_state.current_step == 4:
+        render_step_4_parties()
+    elif st.session_state.current_step == 5:
+        render_step_5_evidence()
+    elif st.session_state.current_step == 6:
+        render_step_6_analysis()
+    elif st.session_state.current_step == 7:
+        render_step_7_results()
+    # Render footer
+    render_footer()
+# ============================================================
+# STEP 1: LOCATION SELECTION
+# ============================================================
+def render_step_1_location():
+    """Render the location selection step."""
+    st.header("📍 Step 1: Accident Location")
+    st.markdown("""
+    <div class="info-card">
+        <h4>Accident Details</h4>
+        <p>Enter the location and conditions where the accident occurred.</p>
+    </div>
+    """, unsafe_allow_html=True)
+    col1, col2 = st.columns([2, 1])
+    with col1:
+        # Map display
+        render_map_section()
+    with col2:
+        st.subheader("Location Details")
+        # Location name
+        location_name = st.text_input(
+            "Location Name",
+            value=st.session_state.accident_info['location']['name']
+        )
+        # Coordinates
+        lat = st.number_input(
+            "Latitude",
+            value=st.session_state.accident_info['location']['latitude'],
+            format="%.6f",
+            step=0.0001
+        )
+        lon = st.number_input(
+            "Longitude",
+            value=st.session_state.accident_info['location']['longitude'],
+            format="%.6f",
+            step=0.0001
+        )
+        # Road type - expanded options
+        road_types = [
+            'roundabout', 'crossroad', 't_junction', 'highway_merge',
+            'parking', 'highway', 'urban_road', 'other'
+        ]
+        road_type_labels = {
+            'roundabout': 'Roundabout (دوار)',
+            'crossroad': 'Crossroad (تقاطع)',
+            't_junction': 'T-Junction',
+            'highway_merge': 'Highway Merge',
+            'parking': 'Parking / Low Speed',
+            'highway': 'Highway',
+            'urban_road': 'Urban Road',
+            'other': 'Other'
+        }
+        road_type = st.selectbox(
+            "Road Type",
+            options=road_types,
+            format_func=lambda x: road_type_labels.get(x, x.title()),
+            index=road_types.index(st.session_state.accident_info.get('road_type', 'roundabout'))
+        )
+        # Date and time
+        accident_date = st.date_input("Accident Date")
+        accident_time = st.time_input("Accident Time")
+        # Weather conditions
+        weather = st.selectbox(
+            "Weather Conditions",
+            options=['clear', 'cloudy', 'rainy', 'foggy', 'sandstorm'],
+            format_func=lambda x: x.title()
+        )
+        # Road condition
+        road_condition = st.selectbox(
+            "Road Condition",
+            options=['dry', 'wet', 'sandy', 'oily'],
+            format_func=lambda x: x.title()
+        )
+        # Notes
+        notes = st.text_area(
+            "Notes (optional)",
+            value=st.session_state.accident_info.get('notes', ''),
+            placeholder="Any extra context about the accident...",
+            height=80
+        )
+        # Update session state
+        st.session_state.accident_info.update({
+            'location': {
+                'name': location_name,
+                'latitude': lat,
+                'longitude': lon,
+                'radius_meters': 200
+            },
+            'road_type': road_type,
+            'datetime': f"{accident_date} {accident_time}",
+            'weather': weather,
+            'road_condition': road_condition,
+            'notes': notes
+        })
+    # Navigation buttons
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col3:
+        if st.button("Next: Vehicle 1 →", type="primary"):
+            st.session_state.current_step = 2
+            st.rerun()
+# ============================================================
+# STEP 2: VEHICLE 1 INPUT
+# ============================================================
+def render_step_2_vehicle1():
+    """Render Vehicle 1 input step."""
+    st.header("🚙 Step 2: First Vehicle Information")
     st.markdown("""
+    <div class="info-card" style="border-color: #FF4B4B;">
+        <h4>Vehicle 1 (Red)</h4>
+        <p>Enter the details of the first vehicle involved in the accident.</p>
+    </div>
+    """, unsafe_allow_html=True)
+    col1, col2 = st.columns([2, 1])
+    with col1:
+        # Map with path drawing
+        render_map_section(vehicle_id=1)
+    with col2:
+        render_vehicle_input(1)
+    # Navigation buttons
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col1:
+        if st.button("← Back"):
+            st.session_state.current_step = 1
+            st.rerun()
+    with col3:
+        if st.button("Next: Vehicle 2 →", type="primary"):
+            st.session_state.current_step = 3
+            st.rerun()
+# ============================================================
+# STEP 3: VEHICLE 2 INPUT
+# ============================================================
+def render_step_3_vehicle2():
+    """Render Vehicle 2 input step."""
+    st.header("🚗 Step 3: Second Vehicle Information")
+    st.markdown("""
+    <div class="info-card" style="border-color: #4B7BFF;">
+        <h4>Vehicle 2 (Blue)</h4>
+        <p>Enter the details of the second vehicle involved in the accident.</p>
+    </div>
+    """, unsafe_allow_html=True)
+    col1, col2 = st.columns([2, 1])
     with col1:
+        # Map with path drawing (showing both vehicles)
+        render_map_section(vehicle_id=2)
     with col2:
+        render_vehicle_input(2)
+    # Navigation buttons
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col1:
+        if st.button("← Back"):
+            st.session_state.current_step = 2
+            st.rerun()
+    with col3:
+        if st.button("Analyze Accident →", type="primary"):
+            st.session_state.current_step = 4
+            st.rerun()
+# ============================================================
+# STEP 4: PARTIES INFORMATION
+# ============================================================
+def render_step_4_parties():
+    """Render the parties (driver details) input step."""
+    st.header("👥 Step 4: Parties Information")
+    st.markdown("""
+    <div class="info-card">
+        <h4>Driver & Vehicle Details</h4>
+        <p>Enter information about the parties involved in the accident. This information will be included in the official report.</p>
+    </div>
+    """, unsafe_allow_html=True)
+    # Two columns for both parties
     col1, col2 = st.columns(2)
     with col1:
+        render_party_input(1)
     with col2:
+        render_party_input(2)
+    # Navigation buttons
+    st.markdown("---")
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col1:
+        if st.button("← Back to Vehicle 2"):
+            st.session_state.current_step = 3
+            st.rerun()
+    with col3:
+        if st.button("Next: Evidence →", type="primary"):
+            st.session_state.current_step = 5
+            st.rerun()
+# ============================================================
+# STEP 5: EVIDENCE UPLOAD
+# ============================================================
+def render_step_5_evidence():
+    """Render the evidence upload step."""
+    st.header("📷 Step 5: Evidence (Optional)")
+    st.markdown("""
+    <div class="info-card">
+        <h4>Upload Evidence Photos</h4>
+        <p>Upload photos of the accident scene, vehicle damage, or any other relevant evidence. This is optional but recommended.</p>
+    </div>
+    """, unsafe_allow_html=True)
+    # Evidence upload
+    render_evidence_upload()
+    # Quick summary of data entered so far
+    st.markdown("---")
+    st.subheader("📋 Data Summary")
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.markdown("**📍 Location**")
+        st.write(f"{st.session_state.accident_info['location'].get('name', 'Not set')}")
+        st.write(f"Type: {st.session_state.accident_info.get('road_type', 'N/A').replace('_', ' ').title()}")
+    with col2:
+        st.markdown("**🚙 Vehicle 1**")
+        # Defensive check - ensure vehicle_1 is a dict
+        v1 = st.session_state.vehicle_1
+        if isinstance(v1, dict):
+            st.write(f"{v1.get('type', 'sedan').title()} @ {v1.get('speed', 50)} km/h")
+        else:
+            st.write("Vehicle 1 data")
+        party1_name = st.session_state.party_1.get('full_name', '') if isinstance(st.session_state.party_1, dict) else ''
+        if party1_name:
+            st.write(f"Driver: {party1_name}")
+    with col3:
+        st.markdown("**🚗 Vehicle 2**")
+        # Defensive check - ensure vehicle_2 is a dict
+        v2 = st.session_state.vehicle_2
+        if isinstance(v2, dict):
+            st.write(f"{v2.get('type', 'sedan').title()} @ {v2.get('speed', 50)} km/h")
+        else:
+            st.write("Vehicle 2 data")
+        party2_name = st.session_state.party_2.get('full_name', '') if isinstance(st.session_state.party_2, dict) else ''
+        if party2_name:
+            st.write(f"Driver: {party2_name}")
+    # Navigation buttons
+    st.markdown("---")
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col1:
+        if st.button("← Back to Parties"):
+            st.session_state.current_step = 4
+            st.rerun()
+    with col3:
+        if st.button("Run Analysis →", type="primary"):
+            st.session_state.current_step = 6
+            st.rerun()
+# ============================================================
+# STEP 6: AI ANALYSIS
+# ============================================================
+def render_step_6_analysis():
+    """Render the AI analysis step."""
+    st.header("🤖 Step 4: AI Analysis")
+    st.markdown("""
+    <div class="info-card">
+        <h4>MindSpore AI Analysis</h4>
+        <p>Review the accident data below and click "Run AI Analysis" to generate possible scenarios.</p>
+    </div>
+    """, unsafe_allow_html=True)
+    # Data validation
+    validation_passed = True
+    validation_messages = []
+    # Check paths
+    if not st.session_state.vehicle_1.get('path'):
+        validation_messages.append("⚠️ Vehicle 1 path not defined - will use direction-based estimation")
+    if not st.session_state.vehicle_2.get('path'):
+        validation_messages.append("⚠️ Vehicle 2 path not defined - will use direction-based estimation")
+    # Summary of inputs in cards
+    st.subheader("📋 Input Data Summary")
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.markdown("""
+        <div style='background: rgba(59, 130, 246, 0.15); padding: 1rem; border-radius: 12px; border-left: 4px solid #3b82f6; box-shadow: 0 4px 12px rgba(0,0,0,0.2);'>
+        <h4 style='margin:0; color:#60a5fa; font-weight: 700;'>📍 Location</h4>
+        </div>
+        """, unsafe_allow_html=True)
+        st.write(f"**{st.session_state.accident_info['location'].get('name', 'Unknown')}**")
+        st.write(f"🛣️ Type: `{st.session_state.accident_info['road_type']}`")
+        st.write(f"🌤️ Weather: `{st.session_state.accident_info['weather']}`")
+        st.write(f"🛤️ Road: `{st.session_state.accident_info['road_condition']}`")
+        st.write(f"📅 {st.session_state.accident_info.get('datetime', 'Not specified')}")
+    with col2:
+        st.markdown("""
+        <div style='background: rgba(255, 75, 75, 0.15); padding: 1rem; border-radius: 12px; border-left: 4px solid #FF4B4B; box-shadow: 0 4px 12px rgba(0,0,0,0.2);'>
+        <h4 style='margin:0; color:#ff6b6b; font-weight: 700;'>🚙 Vehicle 1 (Red)</h4>
+        </div>
+        """, unsafe_allow_html=True)
+        v1 = st.session_state.vehicle_1 if isinstance(st.session_state.vehicle_1, dict) else {'type': 'sedan', 'speed': 50, 'direction': 'north', 'action': 'going_straight', 'path': []}
+        st.write(f"**Type:** {v1.get('type', 'sedan').title()}")
+        st.write(f"**Speed:** {v1.get('speed', 50)} km/h")
+        st.write(f"**Direction:** {v1.get('direction', 'north').title()}")
+        st.write(f"**Action:** {v1.get('action', 'going_straight').replace('_', ' ').title()}")
+        path_status = "✅ Defined" if v1.get('path') else "⚠️ Not set"
+        st.write(f"**Path:** {path_status}")
+    with col3:
+        st.markdown("""
+        <div style='background: rgba(75, 123, 255, 0.15); padding: 1rem; border-radius: 12px; border-left: 4px solid #4B7BFF; box-shadow: 0 4px 12px rgba(0,0,0,0.2);'>
+        <h4 style='margin:0; color:#6b9bff; font-weight: 700;'>🚗 Vehicle 2 (Blue)</h4>
+        </div>
+        """, unsafe_allow_html=True)
+        v2 = st.session_state.vehicle_2 if isinstance(st.session_state.vehicle_2, dict) else {'type': 'sedan', 'speed': 50, 'direction': 'east', 'action': 'going_straight', 'path': []}
+        st.write(f"**Type:** {v2.get('type', 'sedan').title()}")
+        st.write(f"**Speed:** {v2.get('speed', 50)} km/h")
+        st.write(f"**Direction:** {v2.get('direction', 'east').title()}")
+        st.write(f"**Action:** {v2.get('action', 'going_straight').replace('_', ' ').title()}")
+        path_status = "✅ Defined" if v2.get('path') else "⚠️ Not set"
+        st.write(f"**Path:** {path_status}")
+    # Show validation messages
+    if validation_messages:
+        st.markdown("---")
+        for msg in validation_messages:
+            st.warning(msg)
+    st.markdown("---")
+    # Analysis section
+    st.subheader("🚀 Run Analysis")
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col2:
+        if st.button("🧠 Run MindSpore AI Analysis", type="primary", use_container_width=True):
+            # Progress bar
+            progress_bar = st.progress(0)
+            status_text = st.empty()
             try:
+                # Step 1: Validate data
+                status_text.text("Step 1/4: Validating input data...")
+                progress_bar.progress(10)
+                import time
+                time.sleep(0.5)
+                # Step 2: Extract features
+                status_text.text("Step 2/4: Extracting features...")
+                progress_bar.progress(30)
+                time.sleep(0.5)
+                # Step 3: Run AI model
+                status_text.text("Step 3/4: Running MindSpore AI model...")
+                progress_bar.progress(50)
+                # Import and run analysis
                 from analysis.scenario_analyzer import analyze_accident
                 results = analyze_accident(
+                    accident_info=st.session_state.accident_info,
+                    vehicle_1=st.session_state.vehicle_1,
+                    vehicle_2=st.session_state.vehicle_2
                 )
+                progress_bar.progress(80)
+                time.sleep(0.3)
+                # Step 4: Generate scenarios
+                status_text.text("Step 4/4: Generating scenarios...")
+                progress_bar.progress(100)
+                # Store results
+                st.session_state.analysis_results = results
+                st.session_state.scenarios = results.get('scenarios', [])
+                st.session_state.analysis_ready = True
+                status_text.empty()
+                progress_bar.empty()
+                # Success message
+                st.success(f"""
+                ✅ **Analysis Complete!**
+                - Generated **{len(st.session_state.scenarios)}** possible scenarios
+                - Most likely: **{results.get('most_likely_scenario', {}).get('type', 'Unknown').replace('_', ' ').title()}**
+                - Confidence: **{results.get('overall_collision_probability', 0)*100:.1f}%**
+                """)
             except Exception as e:
+                st.error(f"❌ Analysis failed: {str(e)}")
+                progress_bar.empty()
+                status_text.empty()
+    # Show results preview if available
+    if st.session_state.analysis_results:
+        st.markdown("---")
+        st.subheader("📊 Quick Results Preview")
+        results = st.session_state.analysis_results
+        scenarios = st.session_state.scenarios
+        # Quick metrics
+        metric_cols = st.columns(4)
+        with metric_cols[0]:
+            st.metric(
+                "Scenarios Generated",
+                len(scenarios)
+            )
+        with metric_cols[1]:
+            most_likely = results.get('most_likely_scenario', {})
+            st.metric(
+                "Most Likely",
+                f"#{most_likely.get('id', 1)}"
+            )
+        with metric_cols[2]:
+            prob = results.get('overall_collision_probability', 0) * 100
+            st.metric(
+                "Collision Certainty",
+                f"{prob:.1f}%"
+            )
+        with metric_cols[3]:
+            fault = results.get('preliminary_fault_assessment', {})
+            st.metric(
+                "Primary Factor",
+                fault.get('primary_factor', 'Unknown').replace('_', ' ').title()[:15]
+            )
+        # Top scenarios
+        st.write("**Top Scenarios:**")
         for i, scenario in enumerate(scenarios[:3], 1):
+            prob_pct = scenario['probability'] * 100
+            st.write(f"{i}. **{scenario['accident_type'].replace('_', ' ').title()}** - {prob_pct:.1f}%")
+    # Navigation
+    st.markdown("---")
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col1:
+        if st.button("← Back to Evidence"):
+            st.session_state.current_step = 5
+            st.rerun()
+    with col3:
+        if st.session_state.analysis_results:
+            if st.button("View Full Results →", type="primary"):
+                st.session_state.current_step = 7
+                st.rerun()
+# ============================================================
+# STEP 7: RESULTS
+# ============================================================
+def render_step_7_results():
+    """Render the results step."""
+    st.header("📊 Step 7: Analysis Results")
+    if not st.session_state.analysis_results:
+        st.warning("No analysis results available. Please run the analysis first.")
+        if st.button("← Go to Analysis"):
+            st.session_state.current_step = 6
+            st.rerun()
+        return
+    # Render full results
+    render_results()
+    # Navigation
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col1:
+        if st.button("← Back to Analysis"):
+            st.session_state.current_step = 6
+            st.rerun()
+    with col3:
+        if st.button("🔄 Start New Analysis"):
+            # Reset session state
+            for key in list(st.session_state.keys()):
+                del st.session_state[key]
+            st.rerun()
+# ============================================================
+# RUN APPLICATION
+# ============================================================
+if __name__ == "__main__":
+    main()

app_FIXED.py DELETED Viewed

@@ -1,169 +0,0 @@
-"""
-CrashLens - Fixed Version (No Rerun Loops)
-"""
-import streamlit as st
-import sys
-from pathlib import Path
-sys.path.insert(0, str(Path(__file__).parent))
-st.set_page_config(
-    page_title="CrashLens AI",
-    page_icon="🚗",
-    layout="wide"
-)
-# Initialize session state
-if 'analysis_done' not in st.session_state:
-    st.session_state.analysis_done = False
-if 'results' not in st.session_state:
-    st.session_state.results = None
-# Header
-st.title("🚗 CrashLens - AI Traffic Accident Analyzer")
-st.markdown("**Huawei AI Innovation Challenge 2026** | Jubail Industrial College")
-st.markdown("---")
-# Sidebar
-with st.sidebar:
-    st.image("assets/mindspore_logo.png", width=200)
-    st.markdown("### 🧠 Powered by MindSpore AI")
-    st.markdown("---")
-    st.markdown("### 📋 How to Use")
-    st.markdown("""
-    1. Enter accident location
-    2. Add vehicle details
-    3. Click Analyze
-    4. View AI predictions
-    """)
-# Main content - Use tabs instead of steps
-tab1, tab2, tab3 = st.tabs(["📍 Location", "🚗 Vehicles", "🔬 Analysis"])
-with tab1:
-    st.subheader("Accident Location")
-    col1, col2 = st.columns(2)
-    with col1:
-        location_name = st.text_input("Location Name", "Seef District Roundabout")
-        latitude = st.number_input("Latitude", value=26.2397, format="%.4f")
-        longitude = st.number_input("Longitude", value=50.5369, format="%.4f")
-    with col2:
-        weather = st.selectbox("Weather", ["clear", "cloudy", "rainy", "foggy"])
-        road_condition = st.selectbox("Road Condition", ["dry", "wet", "sandy", "oily"])
-        road_type = st.selectbox("Road Type", ["roundabout", "crossroad", "highway", "urban_road"])
-    st.success("✅ Location configured")
-with tab2:
-    st.subheader("Vehicle Information")
-    col1, col2 = st.columns(2)
-    with col1:
-        st.markdown("**🚗 Vehicle 1 (Red)**")
-        v1_type = st.selectbox("Type", ["sedan", "suv", "truck"], key="v1_type")
-        v1_speed = st.slider("Speed (km/h)", 0, 120, 50, key="v1_speed")
-        v1_direction = st.selectbox("Direction", ["north", "south", "east", "west"], key="v1_dir")
-        v1_action = st.selectbox("Action", ["going_straight", "turning_left", "turning_right"], key="v1_action")
-    with col2:
-        st.markdown("**🚙 Vehicle 2 (Blue)**")
-        v2_type = st.selectbox("Type", ["sedan", "suv", "truck"], key="v2_type")
-        v2_speed = st.slider("Speed (km/h)", 0, 120, 50, key="v2_speed")
-        v2_direction = st.selectbox("Direction", ["north", "south", "east", "west"], key="v2_dir")
-        v2_action = st.selectbox("Action", ["going_straight", "turning_left", "turning_right"], key="v2_action")
-    st.success("✅ Vehicles configured")
-with tab3:
-    st.subheader("AI Analysis")
-    if st.button("🧠 Run AI Analysis", type="primary", use_container_width=True):
-        with st.spinner("Analyzing accident scenario..."):
-            try:
-                # Prepare data
-                accident_info = {
-                    'location': {'name': location_name, 'lat': latitude, 'lng': longitude},
-                    'weather': weather,
-                    'road_condition': road_condition,
-                    'road_type': road_type,
-                    'visibility': 1.0,
-                    'lighting': 'daylight'
-                }
-                vehicle_1 = {
-                    'type': v1_type,
-                    'speed': v1_speed,
-                    'direction': v1_direction,
-                    'action': v1_action,
-                    'braking': False,
-                    'signaling': False
-                }
-                vehicle_2 = {
-                    'type': v2_type,
-                    'speed': v2_speed,
-                    'direction': v2_direction,
-                    'action': v2_action,
-                    'braking': False,
-                    'signaling': False
-                }
-                # Run analysis
-                from analysis.scenario_analyzer import analyze_accident
-                results = analyze_accident(
-                    accident_info=accident_info,
-                    vehicle_1=vehicle_1,
-                    vehicle_2=vehicle_2
-                )
-                st.session_state.results = results
-                st.session_state.analysis_done = True
-            except Exception as e:
-                st.error(f"Analysis error: {e}")
-                import traceback
-                with st.expander("Error Details"):
-                    st.code(traceback.format_exc())
-    # Display results
-    if st.session_state.analysis_done and st.session_state.results:
-        st.success("✅ Analysis Complete!")
-        results = st.session_state.results
-        # Show collision type
-        st.markdown("### 🎯 Predicted Collision Type")
-        collision_type = results.get('collision_type', 'Unknown')
-        st.info(f"**{collision_type.replace('_', ' ').title()}**")
-        # Show scenarios
-        st.markdown("### 📊 Scenarios")
-        scenarios = results.get('scenarios', [])
-        for i, scenario in enumerate(scenarios[:3], 1):
-            with st.expander(f"Scenario {i}: {scenario.get('type', 'Unknown').replace('_', ' ').title()}"):
-                col1, col2 = st.columns(2)
-                with col1:
-                    st.metric("Probability", f"{scenario.get('probability', 0)*100:.1f}%")
-                    st.write("**Fault Distribution:**")
-                    st.write(f"- Vehicle 1: {scenario.get('fault_percentage_1', 50)}%")
-                    st.write(f"- Vehicle 2: {scenario.get('fault_percentage_2', 50)}%")
-                with col2:
-                    # Show animation if exists
-                    gif_path = f"output/visualizations/{scenario.get('type', 'head_on_collision')}.gif"
-                    if Path(gif_path).exists():
-                        st.image(gif_path, caption="Simulation")
-        # Download report
-        if st.button("📄 Download Report"):
-            st.info("Report generation coming soon!")
-st.markdown("---")
-st.markdown("© 2026 Jubail Industrial College | Huawei AI Innovation Challenge")

models/__init__.py CHANGED Viewed

	@@ -0,0 +1,21 @@

+"""
+Models Package
+==============
+MindSpore neural network models for accident prediction.
+"""
+from .mindspore_model import (
+    AccidentModelTrainer,
+    NumpyAccidentModel,
+    get_accident_type_name,
+    prepare_features,
+    MINDSPORE_AVAILABLE
+)
+__all__ = [
+    'AccidentModelTrainer',
+    'NumpyAccidentModel',
+    'get_accident_type_name',
+    'prepare_features',
+    'MINDSPORE_AVAILABLE'
+]

models/mindspore_loader.py ADDED Viewed

	@@ -0,0 +1,467 @@

+"""
+MindSpore Model Loader
+======================
+Loads the trained MindSpore model for accident prediction.
+Supports both MindSpore (.ckpt) and NumPy fallback (.npz).
+"""
+import numpy as np
+import json
+from pathlib import Path
+from typing import Dict, Tuple, Optional
+# Try to import MindSpore
+MINDSPORE_AVAILABLE = False
+try:
+    import mindspore as ms
+    import mindspore.nn as nn
+    from mindspore import Tensor, context, load_checkpoint, load_param_into_net
+    import mindspore.ops as ops
+    MINDSPORE_AVAILABLE = True
+    print(f"✅ MindSpore {ms.__version__} available")
+except ImportError:
+    print("⚠️ MindSpore not available, using NumPy fallback")
+# ============================================================
+# ENCODING MAPS (must match training)
+# ============================================================
+DIRECTION_MAP = {
+    'north': 0, 'northeast': 1, 'east': 2, 'southeast': 3,
+    'south': 4, 'southwest': 5, 'west': 6, 'northwest': 7
+}
+ACTION_MAP = {
+    'going_straight': 0, 'turning_left': 1, 'turning_right': 2,
+    'entering_roundabout': 3, 'exiting_roundabout': 4,
+    'changing_lane_left': 5, 'changing_lane_right': 6,
+    'slowing_down': 7, 'accelerating': 8, 'stopped': 9
+}
+VEHICLE_MAP = {'sedan': 0, 'suv': 1, 'truck': 2, 'motorcycle': 3, 'bus': 4}
+WEATHER_MAP = {'clear': 0, 'cloudy': 1, 'rainy': 2, 'foggy': 3, 'sandstorm': 4}
+ROAD_COND_MAP = {'dry': 0, 'wet': 1, 'sandy': 2, 'oily': 3}
+ROAD_TYPE_MAP = {
+    'roundabout': 0, 'crossroad': 1, 't_junction': 2, 'highway_merge': 3,
+    'parking': 4, 'highway': 5, 'urban_road': 6, 'other': 7
+}
+LIGHTING_MAP = {'daylight': 0, 'dusk': 1, 'dawn': 2, 'night_lit': 3, 'night_dark': 4}
+ACCIDENT_MAP = {
+    'rear_end_collision': 0,
+    'side_impact': 1,
+    'head_on_collision': 2,
+    'sideswipe': 3,
+    'roundabout_entry_collision': 4,
+    'lane_change_collision': 5,
+    'intersection_collision': 6
+}
+ACCIDENT_NAMES = {v: k for k, v in ACCIDENT_MAP.items()}
+# ============================================================
+# MINDSPORE MODEL DEFINITION
+# ============================================================
+if MINDSPORE_AVAILABLE:
+    class AccidentClassifier(nn.Cell):
+        """
+        MindSpore Neural Network for Traffic Accident Classification.
+        Architecture: 31 → 128 → 64 → 32 → 7
+        """
+        def __init__(self, input_dim=31, num_classes=7):
+            super(AccidentClassifier, self).__init__()
+            # Layer 1: Input → 128
+            self.fc1 = nn.Dense(input_dim, 128)
+            self.bn1 = nn.BatchNorm1d(128)
+            self.relu1 = nn.ReLU()
+            self.dropout1 = nn.Dropout(p=0.3)
+            # Layer 2: 128 → 64
+            self.fc2 = nn.Dense(128, 64)
+            self.bn2 = nn.BatchNorm1d(64)
+            self.relu2 = nn.ReLU()
+            self.dropout2 = nn.Dropout(p=0.3)
+            # Layer 3: 64 → 32
+            self.fc3 = nn.Dense(64, 32)
+            self.bn3 = nn.BatchNorm1d(32)
+            self.relu3 = nn.ReLU()
+            self.dropout3 = nn.Dropout(p=0.2)
+            # Output: 32 → 7
+            self.fc4 = nn.Dense(32, num_classes)
+        def construct(self, x):
+            x = self.fc1(x)
+            x = self.bn1(x)
+            x = self.relu1(x)
+            x = self.dropout1(x)
+            x = self.fc2(x)
+            x = self.bn2(x)
+            x = self.relu2(x)
+            x = self.dropout2(x)
+            x = self.fc3(x)
+            x = self.bn3(x)
+            x = self.relu3(x)
+            x = self.dropout3(x)
+            x = self.fc4(x)
+            return x
+# ============================================================
+# NUMPY FALLBACK MODEL
+# ============================================================
+class NumpyAccidentModel:
+    """NumPy fallback when MindSpore is not available."""
+    def __init__(self, input_dim=31, num_classes=7):
+        self.input_dim = input_dim
+        self.num_classes = num_classes
+        self.trained = False
+        # Initialize weights
+        np.random.seed(42)
+        self.W1 = np.random.randn(input_dim, 128) * np.sqrt(2.0 / input_dim)
+        self.b1 = np.zeros(128)
+        self.W2 = np.random.randn(128, 64) * np.sqrt(2.0 / 128)
+        self.b2 = np.zeros(64)
+        self.W3 = np.random.randn(64, 32) * np.sqrt(2.0 / 64)
+        self.b3 = np.zeros(32)
+        self.W4 = np.random.randn(32, num_classes) * np.sqrt(2.0 / 32)
+        self.b4 = np.zeros(num_classes)
+    def relu(self, x):
+        return np.maximum(0, x)
+    def softmax(self, x):
+        exp_x = np.exp(x - np.max(x, axis=-1, keepdims=True))
+        return exp_x / np.sum(exp_x, axis=-1, keepdims=True)
+    def forward(self, x):
+        x = self.relu(x @ self.W1 + self.b1)
+        x = self.relu(x @ self.W2 + self.b2)
+        x = self.relu(x @ self.W3 + self.b3)
+        x = x @ self.W4 + self.b4
+        return self.softmax(x)
+    def predict(self, x):
+        if x.ndim == 1:
+            x = x.reshape(1, -1)
+        probs = self.forward(x)
+        classes = np.argmax(probs, axis=1)
+        return classes, probs
+    def load(self, filepath):
+        data = np.load(filepath)
+        self.W1 = data['W1']
+        self.b1 = data['b1']
+        self.W2 = data['W2']
+        self.b2 = data['b2']
+        self.W3 = data['W3']
+        self.b3 = data['b3']
+        self.W4 = data.get('W4', data.get('weight_4', np.random.randn(32, 7) * 0.1))
+        self.b4 = data.get('b4', data.get('bias_4', np.zeros(7)))
+        self.trained = True
+        print(f"✅ NumPy model loaded from {filepath}")
+# ============================================================
+# FEATURE EXTRACTION
+# ============================================================
+def extract_features(accident_info: Dict, vehicle_1: Dict, vehicle_2: Dict) -> np.ndarray:
+    """
+    Extract 31 features from accident data.
+    Must match the training feature extraction exactly!
+    """
+    # Vehicle 1 features (7)
+    v1_type = VEHICLE_MAP.get(vehicle_1.get('type', 'sedan'), 0) / 5
+    v1_speed = vehicle_1.get('speed', 50) / 200
+    v1_dir = DIRECTION_MAP.get(vehicle_1.get('direction', 'north'), 0) / 8
+    v1_angle = v1_dir * 45 / 360
+    v1_action = ACTION_MAP.get(vehicle_1.get('action', 'going_straight'), 0) / 10
+    v1_braking = 1.0 if vehicle_1.get('braking', False) else 0.0
+    v1_signaling = 1.0 if vehicle_1.get('signaling', False) else 0.0
+    # Vehicle 2 features (7)
+    v2_type = VEHICLE_MAP.get(vehicle_2.get('type', 'sedan'), 0) / 5
+    v2_speed = vehicle_2.get('speed', 50) / 200
+    v2_dir = DIRECTION_MAP.get(vehicle_2.get('direction', 'east'), 2) / 8
+    v2_angle = v2_dir * 45 / 360
+    v2_action = ACTION_MAP.get(vehicle_2.get('action', 'going_straight'), 0) / 10
+    v2_braking = 1.0 if vehicle_2.get('braking', False) else 0.0
+    v2_signaling = 1.0 if vehicle_2.get('signaling', False) else 0.0
+    # Environment features (5)
+    weather = WEATHER_MAP.get(accident_info.get('weather', 'clear'), 0) / 5
+    road_cond = ROAD_COND_MAP.get(accident_info.get('road_condition', 'dry'), 0) / 4
+    visibility = accident_info.get('visibility', 1.0)
+    lighting = LIGHTING_MAP.get(accident_info.get('lighting', 'daylight'), 0) / 5
+    road_type = ROAD_TYPE_MAP.get(accident_info.get('road_type', 'roundabout'), 0) / 8
+    # Derived features (12)
+    angle1 = DIRECTION_MAP.get(vehicle_1.get('direction', 'north'), 0) * 45
+    angle2 = DIRECTION_MAP.get(vehicle_2.get('direction', 'east'), 2) * 45
+    collision_angle = abs(angle1 - angle2)
+    if collision_angle > 180:
+        collision_angle = 360 - collision_angle
+    collision_angle_norm = collision_angle / 180
+    speed1 = vehicle_1.get('speed', 50)
+    speed2 = vehicle_2.get('speed', 50)
+    speed_diff = abs(speed1 - speed2) / 200
+    combined_speed = (speed1 + speed2) / 400
+    same_direction = 1.0 if vehicle_1.get('direction') == vehicle_2.get('direction') else 0.0
+    speed_product = (speed1 * speed2) / 40000
+    weather_risk = [0.1, 0.2, 0.5, 0.7, 0.8][WEATHER_MAP.get(accident_info.get('weather', 'clear'), 0)]
+    road_risk = [0.1, 0.5, 0.6, 0.8][ROAD_COND_MAP.get(accident_info.get('road_condition', 'dry'), 0)]
+    base_risk = (weather_risk + road_risk) / 2
+    action_risk = {
+        'going_straight': 0.3, 'turning_left': 0.5, 'turning_right': 0.4,
+        'entering_roundabout': 0.6, 'exiting_roundabout': 0.5,
+        'changing_lane_left': 0.7, 'changing_lane_right': 0.7,
+        'slowing_down': 0.4, 'accelerating': 0.6, 'stopped': 0.2
+    }
+    v1_action_risk = action_risk.get(vehicle_1.get('action', 'going_straight'), 0.5)
+    v2_action_risk = action_risk.get(vehicle_2.get('action', 'going_straight'), 0.5)
+    relative_speed = (speed1 + speed2) / 400 if collision_angle > 90 else abs(speed1 - speed2) / 200
+    approach_rate = min(relative_speed * (1 + base_risk), 1.0)
+    time_factor = 0.5  # Default noon
+    # Build feature vector (31 features)
+    features = np.array([
+        # Vehicle 1 (7)
+        v1_type, v1_speed, v1_dir, v1_angle, v1_action, v1_braking, v1_signaling,
+        # Vehicle 2 (7)
+        v2_type, v2_speed, v2_dir, v2_angle, v2_action, v2_braking, v2_signaling,
+        # Environment (5)
+        weather, road_cond, visibility, lighting, road_type,
+        # Derived (12)
+        collision_angle_norm, speed_diff, combined_speed, same_direction,
+        speed_product, collision_angle_norm, time_factor, base_risk,
+        v1_action_risk, v2_action_risk, relative_speed, approach_rate
+    ], dtype=np.float32)
+    return features
+# ============================================================
+# MODEL MANAGER CLASS
+# ============================================================
+class AccidentModelManager:
+    """
+    Manages loading and inference for accident prediction model.
+    Automatically uses MindSpore if available, otherwise NumPy fallback.
+    """
+    def __init__(self, model_dir: str = None):
+        self.model_dir = Path(model_dir) if model_dir else Path(__file__).parent / "trained"
+        self.model = None
+        self.metadata = None
+        self.backend = None
+        self._loaded = False
+    def load(self, ckpt_path: str = None, npz_path: str = None):
+        """Load the model from checkpoint."""
+        # Try MindSpore first
+        if MINDSPORE_AVAILABLE and ckpt_path:
+            ckpt_file = Path(ckpt_path)
+            if ckpt_file.exists():
+                try:
+                    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+                    self.model = AccidentClassifier(input_dim=31, num_classes=7)
+                    param_dict = load_checkpoint(str(ckpt_file))
+                    load_param_into_net(self.model, param_dict)
+                    self.model.set_train(False)
+                    self.backend = "MindSpore"
+                    self._loaded = True
+                    print(f"✅ MindSpore model loaded from {ckpt_file}")
+                    return True
+                except Exception as e:
+                    print(f"⚠️ Failed to load MindSpore model: {e}")
+        # Fallback to NumPy
+        if npz_path:
+            npz_file = Path(npz_path)
+            if npz_file.exists():
+                try:
+                    self.model = NumpyAccidentModel(input_dim=31, num_classes=7)
+                    self.model.load(str(npz_file))
+                    self.backend = "NumPy"
+                    self._loaded = True
+                    return True
+                except Exception as e:
+                    print(f"⚠️ Failed to load NumPy model: {e}")
+        # Try default paths
+        default_ckpt = self.model_dir / "best_accident_model.ckpt"
+        default_npz = self.model_dir / "accident_model.npz"
+        if MINDSPORE_AVAILABLE and default_ckpt.exists():
+            return self.load(ckpt_path=str(default_ckpt))
+        elif default_npz.exists():
+            return self.load(npz_path=str(default_npz))
+        print("⚠️ No model found. Using untrained model.")
+        self.model = NumpyAccidentModel(input_dim=31, num_classes=7)
+        self.backend = "NumPy (untrained)"
+        self._loaded = True
+        return False
+    def load_metadata(self, metadata_path: str = None):
+        """Load model metadata."""
+        if metadata_path:
+            meta_file = Path(metadata_path)
+        else:
+            meta_file = self.model_dir / "model_metadata.json"
+        if meta_file.exists():
+            with open(meta_file, 'r') as f:
+                self.metadata = json.load(f)
+            print(f"✅ Metadata loaded from {meta_file}")
+            return self.metadata
+        return None
+    def predict(self, accident_info: Dict, vehicle_1: Dict, vehicle_2: Dict) -> Dict:
+        """
+        Predict accident type from input data.
+        Returns:
+            Dict with predicted_class, class_name, probabilities, confidence
+        """
+        if not self._loaded:
+            self.load()
+        # Extract features
+        features = extract_features(accident_info, vehicle_1, vehicle_2)
+        # Get prediction
+        if self.backend == "MindSpore" and MINDSPORE_AVAILABLE:
+            self.model.set_train(False)
+            x = Tensor(features.reshape(1, -1), ms.float32)
+            logits = self.model(x)
+            softmax = ops.Softmax(axis=1)
+            probs = softmax(logits)[0].asnumpy()
+            pred_class = int(np.argmax(probs))
+        else:
+            pred_class, probs = self.model.predict(features)
+            pred_class = int(pred_class[0]) if isinstance(pred_class, np.ndarray) else int(pred_class)
+            probs = probs[0] if probs.ndim > 1 else probs
+        # Build result
+        result = {
+            'predicted_class': pred_class,
+            'class_name': ACCIDENT_NAMES[pred_class],
+            'confidence': float(probs[pred_class]),
+            'probabilities': {
+                ACCIDENT_NAMES[i]: float(probs[i])
+                for i in range(len(probs))
+            },
+            'backend': self.backend
+        }
+        return result
+    def get_all_predictions(self, accident_info: Dict, vehicle_1: Dict, vehicle_2: Dict) -> list:
+        """Get all accident types sorted by probability."""
+        result = self.predict(accident_info, vehicle_1, vehicle_2)
+        sorted_predictions = sorted(
+            result['probabilities'].items(),
+            key=lambda x: x[1],
+            reverse=True
+        )
+        return [
+            {'type': name, 'probability': prob}
+            for name, prob in sorted_predictions
+        ]
+# ============================================================
+# GLOBAL MODEL INSTANCE
+# ============================================================
+_model_manager = None
+def get_model_manager(model_dir: str = None) -> AccidentModelManager:
+    """Get or create global model manager instance."""
+    global _model_manager
+    if _model_manager is None:
+        _model_manager = AccidentModelManager(model_dir)
+    return _model_manager
+def predict_accident(accident_info: Dict, vehicle_1: Dict, vehicle_2: Dict) -> Dict:
+    """Convenience function for prediction."""
+    manager = get_model_manager()
+    if not manager._loaded:
+        manager.load()
+    return manager.predict(accident_info, vehicle_1, vehicle_2)
+# ============================================================
+# TEST
+# ============================================================
+if __name__ == "__main__":
+    print("\n" + "="*60)
+    print("🧪 Testing MindSpore Model Loader")
+    print("="*60)
+    # Create manager
+    manager = AccidentModelManager()
+    manager.load()
+    # Test prediction
+    test_accident = {
+        'weather': 'clear',
+        'road_condition': 'dry',
+        'visibility': 1.0,
+        'lighting': 'daylight',
+        'road_type': 'roundabout'
+    }
+    test_v1 = {
+        'type': 'sedan',
+        'speed': 45,
+        'direction': 'north',
+        'action': 'going_straight',
+        'braking': False,
+        'signaling': False
+    }
+    test_v2 = {
+        'type': 'suv',
+        'speed': 55,
+        'direction': 'east',
+        'action': 'entering_roundabout',
+        'braking': False,
+        'signaling': True
+    }
+    result = manager.predict(test_accident, test_v1, test_v2)
+    print(f"\n🔮 Prediction Result:")
+    print(f"   Backend: {result['backend']}")
+    print(f"   Predicted: {result['class_name']}")
+    print(f"   Confidence: {result['confidence']*100:.1f}%")
+    print(f"\n📊 All Probabilities:")
+    for name, prob in sorted(result['probabilities'].items(), key=lambda x: -x[1]):
+        bar = '█' * int(prob * 20)
+        print(f"   {name:30}: {prob*100:5.1f}% {bar}")

models/mindspore_model.py ADDED Viewed

	@@ -0,0 +1,737 @@

+"""
+MindSpore Accident Prediction Model
+====================================
+Neural network model for predicting accident types and probabilities
+using Huawei MindSpore framework.
+Architecture:
+- Input: 31 features (vehicle data, environment, derived features)
+- Hidden: [256, 128, 64, 32] with BatchNorm and Dropout
+- Output: 7 classes (accident types) + 1 probability
+Huawei AI Innovation Challenge 2026
+"""
+import numpy as np
+from pathlib import Path
+from typing import Tuple, Dict, Any, Optional
+import json
+# Try to import MindSpore (may not be available in all environments)
+try:
+    import mindspore as ms
+    import mindspore.nn as nn
+    import mindspore.ops as ops
+    from mindspore import Tensor, Parameter
+    from mindspore.train import Model
+    from mindspore.train.callback import LossMonitor, ModelCheckpoint, CheckpointConfig
+    from mindspore.nn import SoftmaxCrossEntropyWithLogits, Adam, WithLossCell, TrainOneStepCell
+    from mindspore import context, save_checkpoint, load_checkpoint, load_param_into_net
+    MINDSPORE_AVAILABLE = True
+except ImportError:
+    MINDSPORE_AVAILABLE = False
+    print("Warning: MindSpore not available. Using NumPy fallback.")
+import sys
+sys.path.insert(0, str(Path(__file__).parent.parent))
+from config import MINDSPORE_CONFIG, ACCIDENT_TYPES
+# Define MODEL_DIR locally if not in config
+MODEL_DIR = Path(__file__).parent
+# ============================================================
+# MODEL ARCHITECTURE
+# ============================================================
+if MINDSPORE_AVAILABLE:
+    class AccidentClassifier(nn.Cell):
+        """
+        MindSpore Neural Network for Accident Type Classification.
+        Architecture:
+        - Input Layer: 31 features
+        - Hidden Layers: [256, 128, 64, 32] with BatchNorm, ReLU, Dropout
+        - Output Layer: 7 classes (accident types)
+        """
+        def __init__(self,
+                     input_dim: int = 31,
+                     hidden_dims: list = [256, 128, 64, 32],
+                     num_classes: int = 7,
+                     dropout_rate: float = 0.3):
+            super(AccidentClassifier, self).__init__()
+            self.input_dim = input_dim
+            self.num_classes = num_classes
+            # Build layers
+            layers = []
+            prev_dim = input_dim
+            for hidden_dim in hidden_dims:
+                layers.extend([
+                    nn.Dense(prev_dim, hidden_dim),
+                    nn.BatchNorm1d(hidden_dim),
+                    nn.ReLU(),
+                    nn.Dropout(p=dropout_rate)
+                ])
+                prev_dim = hidden_dim
+            # Output layer
+            layers.append(nn.Dense(prev_dim, num_classes))
+            self.network = nn.SequentialCell(layers)
+        def construct(self, x):
+            """Forward pass."""
+            return self.network(x)
+    class AccidentProbabilityRegressor(nn.Cell):
+        """
+        MindSpore Neural Network for Accident Probability Regression.
+        Predicts collision probability (0-1) based on input features.
+        """
+        def __init__(self,
+                     input_dim: int = 31,
+                     hidden_dims: list = [128, 64, 32],
+                     dropout_rate: float = 0.2):
+            super(AccidentProbabilityRegressor, self).__init__()
+            layers = []
+            prev_dim = input_dim
+            for hidden_dim in hidden_dims:
+                layers.extend([
+                    nn.Dense(prev_dim, hidden_dim),
+                    nn.BatchNorm1d(hidden_dim),
+                    nn.ReLU(),
+                    nn.Dropout(p=dropout_rate)
+                ])
+                prev_dim = hidden_dim
+            # Output layer (sigmoid for probability)
+            layers.append(nn.Dense(prev_dim, 1))
+            layers.append(nn.Sigmoid())
+            self.network = nn.SequentialCell(layers)
+        def construct(self, x):
+            """Forward pass."""
+            return self.network(x)
+    class CombinedAccidentModel(nn.Cell):
+        """
+        Combined model for both classification and probability regression.
+        Two-head architecture:
+        - Shared backbone for feature extraction
+        - Classification head for accident type
+        - Regression head for collision probability
+        """
+        def __init__(self,
+                     input_dim: int = 31,
+                     backbone_dims: list = [256, 128],
+                     classifier_dims: list = [64, 32],
+                     regressor_dims: list = [32],
+                     num_classes: int = 7,
+                     dropout_rate: float = 0.3):
+            super(CombinedAccidentModel, self).__init__()
+            # Shared backbone
+            backbone_layers = []
+            prev_dim = input_dim
+            for hidden_dim in backbone_dims:
+                backbone_layers.extend([
+                    nn.Dense(prev_dim, hidden_dim),
+                    nn.BatchNorm1d(hidden_dim),
+                    nn.ReLU(),
+                    nn.Dropout(p=dropout_rate)
+                ])
+                prev_dim = hidden_dim
+            self.backbone = nn.SequentialCell(backbone_layers)
+            backbone_output_dim = prev_dim
+            # Classification head
+            classifier_layers = []
+            prev_dim = backbone_output_dim
+            for hidden_dim in classifier_dims:
+                classifier_layers.extend([
+                    nn.Dense(prev_dim, hidden_dim),
+                    nn.ReLU(),
+                    nn.Dropout(p=dropout_rate * 0.5)
+                ])
+                prev_dim = hidden_dim
+            classifier_layers.append(nn.Dense(prev_dim, num_classes))
+            self.classifier = nn.SequentialCell(classifier_layers)
+            # Regression head
+            regressor_layers = []
+            prev_dim = backbone_output_dim
+            for hidden_dim in regressor_dims:
+                regressor_layers.extend([
+                    nn.Dense(prev_dim, hidden_dim),
+                    nn.ReLU()
+                ])
+                prev_dim = hidden_dim
+            regressor_layers.append(nn.Dense(prev_dim, 1))
+            regressor_layers.append(nn.Sigmoid())
+            self.regressor = nn.SequentialCell(regressor_layers)
+        def construct(self, x):
+            """Forward pass returning both outputs."""
+            features = self.backbone(x)
+            class_logits = self.classifier(features)
+            probability = self.regressor(features)
+            return class_logits, probability
+# ============================================================
+# NUMPY FALLBACK MODEL (when MindSpore is not available)
+# ============================================================
+class NumpyAccidentModel:
+    """
+    NumPy-based neural network model for accident classification.
+    Uses proper mini-batch gradient descent with Adam optimizer approximation.
+    """
+    def __init__(self, input_dim: int = 31, num_classes: int = 7):
+        self.input_dim = input_dim
+        self.num_classes = num_classes
+        self.trained = False
+        # Network architecture: input -> 64 -> 32 -> output
+        np.random.seed(42)
+        # He initialization for ReLU
+        self.W1 = np.random.randn(input_dim, 64) * np.sqrt(2.0 / input_dim)
+        self.b1 = np.zeros(64)
+        self.W2 = np.random.randn(64, 32) * np.sqrt(2.0 / 64)
+        self.b2 = np.zeros(32)
+        self.W3 = np.random.randn(32, num_classes) * np.sqrt(2.0 / 32)
+        self.b3 = np.zeros(num_classes)
+        # Adam optimizer state
+        self.m = {}
+        self.v = {}
+        self.t = 0
+    def relu(self, x):
+        return np.maximum(0, x)
+    def relu_derivative(self, x):
+        return (x > 0).astype(float)
+    def softmax(self, x):
+        exp_x = np.exp(x - np.max(x, axis=-1, keepdims=True))
+        return exp_x / np.sum(exp_x, axis=-1, keepdims=True)
+    def forward(self, X):
+        """Forward pass."""
+        self.z1 = X @ self.W1 + self.b1
+        self.a1 = self.relu(self.z1)
+        self.z2 = self.a1 @ self.W2 + self.b2
+        self.a2 = self.relu(self.z2)
+        self.z3 = self.a2 @ self.W3 + self.b3
+        self.a3 = self.softmax(self.z3)
+        return self.a3
+    def compute_loss(self, y_pred, y_true):
+        """Cross-entropy loss."""
+        n = len(y_true)
+        y_onehot = np.zeros((n, self.num_classes))
+        y_onehot[np.arange(n), y_true] = 1
+        return -np.mean(np.sum(y_onehot * np.log(y_pred + 1e-10), axis=1))
+    def backward(self, X, y_true, learning_rate=0.001):
+        """Backward pass with gradient descent."""
+        n = len(y_true)
+        # One-hot encode
+        y_onehot = np.zeros((n, self.num_classes))
+        y_onehot[np.arange(n), y_true] = 1
+        # Output layer gradient
+        dz3 = self.a3 - y_onehot
+        dW3 = self.a2.T @ dz3 / n
+        db3 = np.mean(dz3, axis=0)
+        # Hidden layer 2
+        da2 = dz3 @ self.W3.T
+        dz2 = da2 * self.relu_derivative(self.z2)
+        dW2 = self.a1.T @ dz2 / n
+        db2 = np.mean(dz2, axis=0)
+        # Hidden layer 1
+        da1 = dz2 @ self.W2.T
+        dz1 = da1 * self.relu_derivative(self.z1)
+        dW1 = X.T @ dz1 / n
+        db1 = np.mean(dz1, axis=0)
+        # Gradient clipping
+        max_grad = 5.0
+        dW1 = np.clip(dW1, -max_grad, max_grad)
+        dW2 = np.clip(dW2, -max_grad, max_grad)
+        dW3 = np.clip(dW3, -max_grad, max_grad)
+        # Update weights
+        self.W1 -= learning_rate * dW1
+        self.b1 -= learning_rate * db1
+        self.W2 -= learning_rate * dW2
+        self.b2 -= learning_rate * db2
+        self.W3 -= learning_rate * dW3
+        self.b3 -= learning_rate * db3
+    def predict(self, X):
+        """Predict classes and probabilities."""
+        if X.ndim == 1:
+            X = X.reshape(1, -1)
+        probs = self.forward(X)
+        classes = np.argmax(probs, axis=1)
+        return classes, probs
+    def train(self, X, y, epochs=100, lr=0.01, batch_size=32):
+        """Train the model."""
+        n_samples = X.shape[0]
+        best_loss = float('inf')
+        patience = 20
+        patience_counter = 0
+        for epoch in range(epochs):
+            # Shuffle
+            indices = np.random.permutation(n_samples)
+            X_shuffled = X[indices]
+            y_shuffled = y[indices]
+            total_loss = 0
+            n_batches = 0
+            # Learning rate schedule
+            current_lr = lr * (0.98 ** (epoch // 10))
+            for i in range(0, n_samples, batch_size):
+                X_batch = X_shuffled[i:i+batch_size]
+                y_batch = y_shuffled[i:i+batch_size]
+                # Forward
+                probs = self.forward(X_batch)
+                loss = self.compute_loss(probs, y_batch)
+                total_loss += loss
+                n_batches += 1
+                # Backward
+                self.backward(X_batch, y_batch, current_lr)
+            avg_loss = total_loss / n_batches
+            # Calculate accuracy
+            preds, _ = self.predict(X)
+            accuracy = np.mean(preds == y)
+            # Early stopping
+            if avg_loss < best_loss:
+                best_loss = avg_loss
+                patience_counter = 0
+            else:
+                patience_counter += 1
+            if (epoch + 1) % 10 == 0:
+                print(f"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}, Accuracy: {accuracy*100:.1f}%")
+            if patience_counter >= patience:
+                print(f"Early stopping at epoch {epoch+1}")
+                break
+        self.trained = True
+    def save(self, filepath: str):
+        """Save model weights."""
+        np.savez(filepath,
+                 W1=self.W1, b1=self.b1,
+                 W2=self.W2, b2=self.b2,
+                 W3=self.W3, b3=self.b3,
+                 trained=self.trained)
+        print(f"Model saved to {filepath}")
+    def load(self, filepath: str):
+        """Load model weights."""
+        data = np.load(filepath)
+        self.W1 = data['W1']
+        self.b1 = data['b1']
+        self.W2 = data['W2']
+        self.b2 = data['b2']
+        self.W3 = data['W3']
+        self.b3 = data['b3']
+        self.trained = bool(data['trained'])
+        print(f"Model loaded from {filepath}")
+# ============================================================
+# MODEL TRAINER
+# ============================================================
+class AccidentModelTrainer:
+    """
+    Trainer class for accident prediction models.
+    Handles both MindSpore and NumPy fallback.
+    """
+    def __init__(self,
+                 input_dim: int = 31,
+                 num_classes: int = 7,
+                 use_mindspore: bool = True):
+        self.input_dim = input_dim
+        self.num_classes = num_classes
+        self.use_mindspore = use_mindspore and MINDSPORE_AVAILABLE
+        if self.use_mindspore:
+            # Set MindSpore context
+            context.set_context(
+                mode=context.GRAPH_MODE,
+                device_target="CPU"
+            )
+            self.model = AccidentClassifier(
+                input_dim=input_dim,
+                num_classes=num_classes
+            )
+            print("Using MindSpore model")
+        else:
+            self.model = NumpyAccidentModel(
+                input_dim=input_dim,
+                num_classes=num_classes
+            )
+            print("Using NumPy fallback model")
+        self.trained = False
+        self.history = {'loss': [], 'accuracy': []}
+    def train(self,
+              X_train: np.ndarray,
+              y_train: np.ndarray,
+              X_val: np.ndarray = None,
+              y_val: np.ndarray = None,
+              epochs: int = 100,
+              batch_size: int = 64,
+              learning_rate: float = 0.001):
+        """
+        Train the model.
+        Args:
+            X_train: Training features
+            y_train: Training labels
+            X_val: Validation features (optional)
+            y_val: Validation labels (optional)
+            epochs: Number of training epochs
+            batch_size: Batch size
+            learning_rate: Learning rate
+        """
+        print(f"\n{'='*60}")
+        print("TRAINING ACCIDENT PREDICTION MODEL")
+        print(f"{'='*60}")
+        print(f"Training samples: {X_train.shape[0]}")
+        print(f"Features: {X_train.shape[1]}")
+        print(f"Classes: {self.num_classes}")
+        print(f"Epochs: {epochs}")
+        print(f"Batch size: {batch_size}")
+        print(f"Learning rate: {learning_rate}")
+        print(f"{'='*60}\n")
+        if self.use_mindspore:
+            self._train_mindspore(X_train, y_train, X_val, y_val,
+                                  epochs, batch_size, learning_rate)
+        else:
+            self._train_numpy(X_train, y_train, epochs, learning_rate, batch_size)
+        self.trained = True
+        print("\n✅ Training complete!")
+    def _train_mindspore(self, X_train, y_train, X_val, y_val,
+                         epochs, batch_size, learning_rate):
+        """Train using MindSpore."""
+        # Create dataset
+        dataset = ms.dataset.NumpySlicesDataset(
+            {"data": X_train, "label": y_train},
+            shuffle=True
+        )
+        dataset = dataset.batch(batch_size)
+        # Loss and optimizer
+        loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+        optimizer = nn.Adam(self.model.trainable_params(), learning_rate=learning_rate)
+        # Training loop
+        self.model.set_train()
+        for epoch in range(epochs):
+            total_loss = 0
+            num_batches = 0
+            for batch in dataset.create_dict_iterator():
+                data = batch["data"]
+                label = batch["label"]
+                # Forward pass
+                logits = self.model(data)
+                loss = loss_fn(logits, label)
+                # Backward pass
+                grads = ops.GradOperation(get_all=True)(loss_fn)(logits, label)
+                optimizer(grads)
+                total_loss += loss.asnumpy()
+                num_batches += 1
+            avg_loss = total_loss / num_batches
+            self.history['loss'].append(avg_loss)
+            if (epoch + 1) % 10 == 0:
+                # Calculate accuracy on validation set
+                if X_val is not None:
+                    acc = self._evaluate_mindspore(X_val, y_val)
+                    self.history['accuracy'].append(acc)
+                    print(f"Epoch {epoch+1}/{epochs} - Loss: {avg_loss:.4f} - Val Acc: {acc:.4f}")
+                else:
+                    print(f"Epoch {epoch+1}/{epochs} - Loss: {avg_loss:.4f}")
+    def _train_numpy(self, X_train, y_train, epochs, learning_rate, batch_size):
+        """Train using NumPy fallback."""
+        self.model.train(X_train, y_train, epochs=epochs,
+                        lr=learning_rate, batch_size=batch_size)
+    def _evaluate_mindspore(self, X, y):
+        """Evaluate accuracy using MindSpore."""
+        self.model.set_train(False)
+        logits = self.model(Tensor(X, ms.float32))
+        predictions = logits.asnumpy().argmax(axis=1)
+        accuracy = (predictions == y).mean()
+        self.model.set_train(True)
+        return accuracy
+    def predict(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Predict accident type and probabilities.
+        Returns:
+            class_ids: Predicted class indices
+            probabilities: Probability for each class
+        """
+        if not self.trained:
+            print("Warning: Model not trained, using random initialization")
+        if self.use_mindspore:
+            self.model.set_train(False)
+            logits = self.model(Tensor(X, ms.float32))
+            probs = ops.Softmax(axis=1)(logits).asnumpy()
+            class_ids = probs.argmax(axis=1)
+        else:
+            class_ids, probs = self.model.predict(X)
+        return class_ids, probs
+    def save(self, filepath: str):
+        """Save the trained model."""
+        model_dir = Path(filepath).parent
+        model_dir.mkdir(parents=True, exist_ok=True)
+        if self.use_mindspore:
+            save_checkpoint(self.model, filepath + ".ckpt")
+            print(f"MindSpore model saved to {filepath}.ckpt")
+        else:
+            self.model.save(filepath + ".npz")
+        # Save metadata
+        metadata = {
+            'input_dim': self.input_dim,
+            'num_classes': self.num_classes,
+            'use_mindspore': self.use_mindspore,
+            'trained': self.trained,
+            'history': self.history
+        }
+        with open(filepath + "_metadata.json", 'w') as f:
+            json.dump(metadata, f, indent=2)
+    def load(self, filepath: str):
+        """Load a trained model."""
+        if self.use_mindspore:
+            param_dict = load_checkpoint(filepath + ".ckpt")
+            load_param_into_net(self.model, param_dict)
+            print(f"MindSpore model loaded from {filepath}.ckpt")
+        else:
+            self.model.load(filepath + ".npz")
+        # Load metadata
+        metadata_path = filepath + "_metadata.json"
+        if Path(metadata_path).exists():
+            with open(metadata_path, 'r') as f:
+                metadata = json.load(f)
+            self.trained = metadata.get('trained', True)
+            self.history = metadata.get('history', {})
+        else:
+            self.trained = True
+# ============================================================
+# HELPER FUNCTIONS
+# ============================================================
+def get_accident_type_name(class_id: int) -> str:
+    """Convert class ID to accident type name."""
+    if 0 <= class_id < len(ACCIDENT_TYPES):
+        return ACCIDENT_TYPES[class_id]
+    return "unknown"
+def prepare_features(accident_data: Dict) -> np.ndarray:
+    """
+    Prepare feature vector from accident data dictionary.
+    Args:
+        accident_data: Dictionary containing accident information
+    Returns:
+        Feature vector (31 dimensions)
+    """
+    # Encodings
+    direction_encoding = {
+        'north': 0, 'northeast': 1, 'east': 2, 'southeast': 3,
+        'south': 4, 'southwest': 5, 'west': 6, 'northwest': 7
+    }
+    action_encoding = {
+        'going_straight': 0, 'turning_left': 1, 'turning_right': 2,
+        'entering_roundabout': 3, 'exiting_roundabout': 4,
+        'changing_lane_left': 5, 'changing_lane_right': 6,
+        'slowing_down': 7, 'accelerating': 8, 'stopped': 9
+    }
+    vehicle_encoding = {'sedan': 0, 'suv': 1, 'truck': 2, 'motorcycle': 3, 'bus': 4}
+    weather_encoding = {'clear': 0, 'cloudy': 1, 'rainy': 2, 'foggy': 3, 'sandstorm': 4}
+    road_encoding = {'dry': 0, 'wet': 1, 'sandy': 2, 'oily': 3}
+    road_type_encoding = {
+        'roundabout': 0, 'crossroad': 1, 't_junction': 2, 'highway_merge': 3,
+        'parking': 4, 'highway': 5, 'urban_road': 6, 'other': 7
+    }
+    lighting_encoding = {'daylight': 0, 'dusk': 1, 'dawn': 2, 'night_lit': 3, 'night_dark': 4}
+    # Extract data
+    v1 = accident_data.get('vehicle_1', {})
+    v2 = accident_data.get('vehicle_2', {})
+    conditions = accident_data.get('accident_info', {})
+    # Build feature vector
+    v1_speed = v1.get('speed', 50)
+    v2_speed = v2.get('speed', 50)
+    v1_dir = direction_encoding.get(v1.get('direction', 'north'), 0)
+    v2_dir = direction_encoding.get(v2.get('direction', 'east'), 2)
+    v1_angle = v1_dir * 45
+    v2_angle = v2_dir * 45
+    collision_angle = abs(v1_angle - v2_angle)
+    if collision_angle > 180:
+        collision_angle = 360 - collision_angle
+    weather = weather_encoding.get(conditions.get('weather', 'clear'), 0)
+    road_cond = road_encoding.get(conditions.get('road_condition', 'dry'), 0)
+    visibility = conditions.get('visibility', 1.0)
+    lighting = lighting_encoding.get(conditions.get('lighting', 'daylight'), 0)
+    road_type = road_type_encoding.get(conditions.get('road_type', 'roundabout'), 0)
+    features = [
+        # Vehicle 1 (7)
+        vehicle_encoding.get(v1.get('type', 'sedan'), 0) / 5,
+        v1_speed / 200,
+        v1_dir / 8,
+        v1_angle / 360,
+        action_encoding.get(v1.get('action', 'going_straight'), 0) / 10,
+        1 if v1.get('braking', False) else 0,
+        1 if v1.get('signaling', False) else 0,
+        # Vehicle 2 (7)
+        vehicle_encoding.get(v2.get('type', 'sedan'), 0) / 5,
+        v2_speed / 200,
+        v2_dir / 8,
+        v2_angle / 360,
+        action_encoding.get(v2.get('action', 'going_straight'), 0) / 10,
+        1 if v2.get('braking', False) else 0,
+        1 if v2.get('signaling', False) else 0,
+        # Environment (5)
+        weather / 5,
+        road_cond / 4,
+        visibility,
+        lighting / 5,
+        road_type / 8,
+        # Derived (12)
+        collision_angle / 180,
+        abs(v1_speed - v2_speed) / 200,
+        (v1_speed + v2_speed) / 400,
+        1 if v1.get('direction') == v2.get('direction') else 0,
+        (v1_speed * v2_speed) / 40000,
+        collision_angle / 180,
+        12 / 24,  # Default noon
+        0.3,  # Default risk
+        0.5,  # V1 action risk
+        0.5,  # V2 action risk
+        (v1_speed + v2_speed) / 400,  # Relative speed
+        0.3   # Approach rate
+    ]
+    return np.array(features, dtype=np.float32)
+# ============================================================
+# TEST
+# ============================================================
+if __name__ == "__main__":
+    print("Testing Accident Prediction Model")
+    print("="*60)
+    # Create dummy data
+    np.random.seed(42)
+    X_test = np.random.randn(100, 31).astype(np.float32)
+    y_test = np.random.randint(0, 7, 100).astype(np.int32)
+    # Create and test model
+    trainer = AccidentModelTrainer(
+        input_dim=31,
+        num_classes=7,
+        use_mindspore=MINDSPORE_AVAILABLE
+    )
+    # Train
+    trainer.train(X_test, y_test, epochs=20, batch_size=16)
+    # Predict
+    class_ids, probs = trainer.predict(X_test[:5])
+    print("\nPredictions:")
+    for i in range(5):
+        print(f"  Sample {i+1}: {get_accident_type_name(class_ids[i])} "
+              f"(prob: {probs[i].max():.3f})")
+    print("\n✅ Model test complete!")

models/trained/best_accident_model.ckpt ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:2f579d411b52fcf754113dd5b81e1c9aba5f319dd493910c4210e95b03974976
+size 62916

output/reports/accident_report_20251230_084121.html ADDED Viewed

	@@ -0,0 +1,688 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Traffic Accident Analysis Report</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            line-height: 1.6;
+            color: #333;
+            background: #f5f5f5;
+        }
+        .container {
+            max-width: 1000px;
+            margin: 0 auto;
+            background: white;
+            box-shadow: 0 0 20px rgba(0,0,0,0.1);
+        }
+        .header {
+            background: linear-gradient(135deg, #1e3a5f 0%, #2d5a87 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }
+        .header h1 {
+            font-size: 2.5rem;
+            margin-bottom: 10px;
+        }
+        .header .subtitle {
+            opacity: 0.9;
+            font-size: 1.1rem;
+        }
+        .section {
+            padding: 30px 40px;
+            border-bottom: 1px solid #eee;
+        }
+        .section h2 {
+            color: #1e3a5f;
+            margin-bottom: 20px;
+            padding-bottom: 10px;
+            border-bottom: 2px solid #2d5a87;
+        }
+        .summary-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+            margin: 20px 0;
+        }
+        .summary-card {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+            text-align: center;
+            border-top: 4px solid #2d5a87;
+        }
+        .summary-card .label {
+            font-size: 0.9rem;
+            color: #666;
+            margin-bottom: 5px;
+        }
+        .summary-card .value {
+            font-size: 1.8rem;
+            font-weight: bold;
+            color: #1e3a5f;
+        }
+        .summary-card .delta {
+            font-size: 0.85rem;
+            margin-top: 5px;
+        }
+        .delta.high { color: #28a745; }
+        .delta.medium { color: #ffc107; }
+        .delta.low { color: #dc3545; }
+        .info-grid {
+            display: grid;
+            grid-template-columns: repeat(2, 1fr);
+            gap: 30px;
+        }
+        .info-box {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+        }
+        .info-box h3 {
+            color: #1e3a5f;
+            margin-bottom: 15px;
+        }
+        .info-row {
+            display: flex;
+            justify-content: space-between;
+            padding: 8px 0;
+            border-bottom: 1px solid #eee;
+        }
+        .info-row:last-child {
+            border-bottom: none;
+        }
+        .vehicle-card {
+            background: white;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 15px 0;
+        }
+        .vehicle-card.v1 {
+            border-left: 4px solid #FF4B4B;
+        }
+        .vehicle-card.v2 {
+            border-left: 4px solid #4B7BFF;
+        }
+        .scenario-card {
+            background: #f8f9fa;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 15px 0;
+            border-left: 4px solid #2d5a87;
+        }
+        .scenario-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 15px;
+        }
+        .probability {
+            font-size: 1.5rem;
+            font-weight: bold;
+        }
+        .probability.high { color: #28a745; }
+        .probability.medium { color: #ffc107; }
+        .probability.low { color: #dc3545; }
+        .progress-bar {
+            background: #e9ecef;
+            border-radius: 5px;
+            height: 10px;
+            margin: 10px 0;
+            overflow: hidden;
+        }
+        .progress-fill {
+            background: #2d5a87;
+            height: 100%;
+            border-radius: 5px;
+        }
+        .factors-list {
+            list-style: none;
+            padding: 0;
+        }
+        .factors-list li {
+            padding: 5px 0;
+            padding-left: 20px;
+            position: relative;
+        }
+        .factors-list li::before {
+            content: "•";
+            color: #2d5a87;
+            position: absolute;
+            left: 0;
+        }
+        .timeline {
+            margin: 20px 0;
+        }
+        .timeline-item {
+            display: flex;
+            margin: 10px 0;
+        }
+        .timeline-time {
+            min-width: 80px;
+            padding: 8px 15px;
+            background: #ffc107;
+            color: white;
+            font-weight: bold;
+            text-align: center;
+            border-radius: 5px;
+        }
+        .timeline-time.impact {
+            background: #dc3545;
+        }
+        .timeline-time.after {
+            background: #28a745;
+        }
+        .timeline-event {
+            flex: 1;
+            padding: 8px 15px;
+            background: #f8f9fa;
+            margin-left: 10px;
+            border-radius: 5px;
+        }
+        .fault-assessment {
+            background: #fff3cd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 4px solid #ffc107;
+            margin: 20px 0;
+        }
+        .footer {
+            background: #1e3a5f;
+            color: white;
+            padding: 30px 40px;
+            text-align: center;
+        }
+        .footer p {
+            opacity: 0.8;
+            font-size: 0.9rem;
+        }
+        @media print {
+            .container {
+                box-shadow: none;
+            }
+            .section {
+                page-break-inside: avoid;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Header -->
+        <div class="header">
+            <h1>🚗 Traffic Accident Analysis Report</h1>
+            <p class="subtitle">AI-Powered Analysis using Huawei MindSpore</p>
+            <p style="margin-top: 15px; opacity: 0.7;">Generated: December 30, 2025 at 08:41</p>
+        </div>
+        <!-- Executive Summary -->
+        <div class="section">
+            <h2>📊 Executive Summary</h2>
+            <div class="summary-grid">
+                <div class="summary-card">
+                    <div class="label">Most Likely Scenario</div>
+                    <div class="value">#1</div>
+                    <div class="delta high">27.9% probability</div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Scenarios Generated</div>
+                    <div class="value">5</div>
+                    <div class="delta">AI-generated</div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Collision Certainty</div>
+                    <div class="value">86.0%</div>
+                    <div class="delta high">
+                        High
+                    </div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Primary Factor</div>
+                    <div class="value" style="font-size: 1.2rem;">Failure To Yield</div>
+                    <div class="delta">Vehicle 2</div>
+                </div>
+            </div>
+        </div>
+        <!-- Accident Details -->
+        <div class="section">
+            <h2>📍 Accident Details</h2>
+            <div class="info-grid">
+                <div class="info-box">
+                    <h3>Location Information</h3>
+                    <div class="info-row">
+                        <span>Location:</span>
+                        <strong>دوار السيف - Seef District Roundabout</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Coordinates:</span>
+                        <strong>26.2397, 50.5369</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Road Type:</span>
+                        <strong>Roundabout</strong>
+                    </div>
+                </div>
+                <div class="info-box">
+                    <h3>Conditions</h3>
+                    <div class="info-row">
+                        <span>Date/Time:</span>
+                        <strong>2025-12-30 10:30</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Weather:</span>
+                        <strong>Clear</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Road Condition:</span>
+                        <strong>Dry</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Vehicle Information -->
+        <div class="section">
+            <h2>🚙 Vehicle Information</h2>
+            <div class="info-grid">
+                <div class="vehicle-card v1">
+                    <h3 style="color: #FF4B4B;">Vehicle 1 (Red)</h3>
+                    <div class="info-row">
+                        <span>Type:</span>
+                        <strong>Sedan</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Speed:</span>
+                        <strong>45 km/h</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Direction:</span>
+                        <strong>North</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Action:</span>
+                        <strong>Entering Roundabout</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Braking:</span>
+                        <strong>No</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Signaling:</span>
+                        <strong>Yes</strong>
+                    </div>
+                </div>
+                <div class="vehicle-card v2">
+                    <h3 style="color: #4B7BFF;">Vehicle 2 (Blue)</h3>
+                    <div class="info-row">
+                        <span>Type:</span>
+                        <strong>SUV</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Speed:</span>
+                        <strong>55 km/h</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Direction:</span>
+                        <strong>East</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Action:</span>
+                        <strong>Going Straight</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Braking:</span>
+                        <strong>Yes</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Signaling:</span>
+                        <strong>No</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Generated Scenarios -->
+        <div class="section">
+            <h2>🎯 AI-Generated Scenarios</h2>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 1: Side Impact</h3>
+                    </div>
+                    <div class="probability medium">
+                        27.9%
+                    </div>
+                </div>
+                <p>A Sedan was struck on the side by a SUV at an intersection. The impact angle was approximately 90 degrees.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li><li>Running Red Light</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 2: Roundabout Entry Collision</h3>
+                    </div>
+                    <div class="probability medium">
+                        23.0%
+                    </div>
+                </div>
+                <p>A Sedan entering the roundabout collided with a SUV already circulating within the roundabout.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li><li>Running Red Light</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 3: Intersection Collision</h3>
+                    </div>
+                    <div class="probability medium">
+                        23.0%
+                    </div>
+                </div>
+                <p>Both vehicles entered the intersection simultaneously, resulting in a collision at the crossing point.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 4: Lane Change Collision</h3>
+                    </div>
+                    <div class="probability low">
+                        19.7%
+                    </div>
+                </div>
+                <p>A collision occurred when one vehicle changed lanes without properly checking for the other vehicle.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Improper Lane Change</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 5: Sideswipe</h3>
+                    </div>
+                    <div class="probability low">
+                        6.6%
+                    </div>
+                </div>
+                <p>Both vehicles were traveling in similar directions when a Sedan sideswiped a SUV during a lane change or merge.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Improper Lane Change</li><li>Running Red Light</li>
+                    </ul>
+                </div>
+            </div>
+        </div>
+        <!-- Fault Assessment -->
+        <div class="section">
+            <h2>⚖️ Preliminary Fault Assessment</h2>
+            <div class="fault-assessment">
+                <h3>⚠️ Disclaimer</h3>
+                <p>This is a preliminary AI-generated assessment for reference purposes only. Final fault determination should be made by qualified traffic authorities based on comprehensive investigation.</p>
+            </div>
+            <div class="info-grid" style="margin-top: 20px;">
+                <div class="info-box">
+                    <h3>Contribution Analysis</h3>
+                    <div style="margin: 15px 0;">
+                        <span style="color: #FF4B4B;">Vehicle 1: 36.4%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 36.44859813084113%; background: #FF4B4B;"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span style="color: #4B7BFF;">Vehicle 2: 63.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 63.55140186915889%; background: #4B7BFF;"></div>
+                        </div>
+                    </div>
+                </div>
+                <div class="info-box">
+                    <h3>Assessment Summary</h3>
+                    <div class="info-row">
+                        <span>Higher Contribution:</span>
+                        <strong>Vehicle 2</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Primary Factor:</span>
+                        <strong>Failure To Yield</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Assessment Confidence:</span>
+                        <strong>63.6%</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Timeline -->
+        <div class="section">
+            <h2>⏱️ Estimated Accident Timeline</h2>
+            <div class="timeline">
+                <div class="timeline-item">
+                    <div class="timeline-time ">-1.2s</div>
+                    <div class="timeline-event">Vehicles approaching conflict zone</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.7s</div>
+                    <div class="timeline-event">Vehicle paths begin to converge</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.3s</div>
+                    <div class="timeline-event">Collision becomes imminent</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.1s</div>
+                    <div class="timeline-event">Point of no return - evasive action no longer possible</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time impact">+0.0s</div>
+                    <div class="timeline-event">Impact - Collision occurs</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time after">+0.5s</div>
+                    <div class="timeline-event">Vehicles come to rest after impact</div>
+                </div>
+            </div>
+        </div>
+        <!-- Footer -->
+        <div class="footer">
+            <p><strong>Traffic Accident Reconstruction System</strong></p>
+            <p>Huawei AI Innovation Challenge 2026 | Jubail Industrial College</p>
+            <p style="margin-top: 10px;">Powered by Huawei MindSpore AI Framework</p>
+            <p style="margin-top: 15px; font-size: 0.8rem;">Report ID: 20251230_084121</p>
+        </div>
+    </div>
+</body>
+</html>

output/reports/accident_report_20251230_091851.html ADDED Viewed

	@@ -0,0 +1,688 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Traffic Accident Analysis Report</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            line-height: 1.6;
+            color: #333;
+            background: #f5f5f5;
+        }
+        .container {
+            max-width: 1000px;
+            margin: 0 auto;
+            background: white;
+            box-shadow: 0 0 20px rgba(0,0,0,0.1);
+        }
+        .header {
+            background: linear-gradient(135deg, #1e3a5f 0%, #2d5a87 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }
+        .header h1 {
+            font-size: 2.5rem;
+            margin-bottom: 10px;
+        }
+        .header .subtitle {
+            opacity: 0.9;
+            font-size: 1.1rem;
+        }
+        .section {
+            padding: 30px 40px;
+            border-bottom: 1px solid #eee;
+        }
+        .section h2 {
+            color: #1e3a5f;
+            margin-bottom: 20px;
+            padding-bottom: 10px;
+            border-bottom: 2px solid #2d5a87;
+        }
+        .summary-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+            margin: 20px 0;
+        }
+        .summary-card {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+            text-align: center;
+            border-top: 4px solid #2d5a87;
+        }
+        .summary-card .label {
+            font-size: 0.9rem;
+            color: #666;
+            margin-bottom: 5px;
+        }
+        .summary-card .value {
+            font-size: 1.8rem;
+            font-weight: bold;
+            color: #1e3a5f;
+        }
+        .summary-card .delta {
+            font-size: 0.85rem;
+            margin-top: 5px;
+        }
+        .delta.high { color: #28a745; }
+        .delta.medium { color: #ffc107; }
+        .delta.low { color: #dc3545; }
+        .info-grid {
+            display: grid;
+            grid-template-columns: repeat(2, 1fr);
+            gap: 30px;
+        }
+        .info-box {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+        }
+        .info-box h3 {
+            color: #1e3a5f;
+            margin-bottom: 15px;
+        }
+        .info-row {
+            display: flex;
+            justify-content: space-between;
+            padding: 8px 0;
+            border-bottom: 1px solid #eee;
+        }
+        .info-row:last-child {
+            border-bottom: none;
+        }
+        .vehicle-card {
+            background: white;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 15px 0;
+        }
+        .vehicle-card.v1 {
+            border-left: 4px solid #FF4B4B;
+        }
+        .vehicle-card.v2 {
+            border-left: 4px solid #4B7BFF;
+        }
+        .scenario-card {
+            background: #f8f9fa;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 15px 0;
+            border-left: 4px solid #2d5a87;
+        }
+        .scenario-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 15px;
+        }
+        .probability {
+            font-size: 1.5rem;
+            font-weight: bold;
+        }
+        .probability.high { color: #28a745; }
+        .probability.medium { color: #ffc107; }
+        .probability.low { color: #dc3545; }
+        .progress-bar {
+            background: #e9ecef;
+            border-radius: 5px;
+            height: 10px;
+            margin: 10px 0;
+            overflow: hidden;
+        }
+        .progress-fill {
+            background: #2d5a87;
+            height: 100%;
+            border-radius: 5px;
+        }
+        .factors-list {
+            list-style: none;
+            padding: 0;
+        }
+        .factors-list li {
+            padding: 5px 0;
+            padding-left: 20px;
+            position: relative;
+        }
+        .factors-list li::before {
+            content: "•";
+            color: #2d5a87;
+            position: absolute;
+            left: 0;
+        }
+        .timeline {
+            margin: 20px 0;
+        }
+        .timeline-item {
+            display: flex;
+            margin: 10px 0;
+        }
+        .timeline-time {
+            min-width: 80px;
+            padding: 8px 15px;
+            background: #ffc107;
+            color: white;
+            font-weight: bold;
+            text-align: center;
+            border-radius: 5px;
+        }
+        .timeline-time.impact {
+            background: #dc3545;
+        }
+        .timeline-time.after {
+            background: #28a745;
+        }
+        .timeline-event {
+            flex: 1;
+            padding: 8px 15px;
+            background: #f8f9fa;
+            margin-left: 10px;
+            border-radius: 5px;
+        }
+        .fault-assessment {
+            background: #fff3cd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 4px solid #ffc107;
+            margin: 20px 0;
+        }
+        .footer {
+            background: #1e3a5f;
+            color: white;
+            padding: 30px 40px;
+            text-align: center;
+        }
+        .footer p {
+            opacity: 0.8;
+            font-size: 0.9rem;
+        }
+        @media print {
+            .container {
+                box-shadow: none;
+            }
+            .section {
+                page-break-inside: avoid;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Header -->
+        <div class="header">
+            <h1>🚗 Traffic Accident Analysis Report</h1>
+            <p class="subtitle">AI-Powered Analysis using Huawei MindSpore</p>
+            <p style="margin-top: 15px; opacity: 0.7;">Generated: December 30, 2025 at 09:18</p>
+        </div>
+        <!-- Executive Summary -->
+        <div class="section">
+            <h2>📊 Executive Summary</h2>
+            <div class="summary-grid">
+                <div class="summary-card">
+                    <div class="label">Most Likely Scenario</div>
+                    <div class="value">#1</div>
+                    <div class="delta high">95.3% probability</div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Scenarios Generated</div>
+                    <div class="value">5</div>
+                    <div class="delta">AI-generated</div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Collision Certainty</div>
+                    <div class="value">86.0%</div>
+                    <div class="delta high">
+                        High
+                    </div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Primary Factor</div>
+                    <div class="value" style="font-size: 1.2rem;">Failure To Yield</div>
+                    <div class="delta">Vehicle 2</div>
+                </div>
+            </div>
+        </div>
+        <!-- Accident Details -->
+        <div class="section">
+            <h2>📍 Accident Details</h2>
+            <div class="info-grid">
+                <div class="info-box">
+                    <h3>Location Information</h3>
+                    <div class="info-row">
+                        <span>Location:</span>
+                        <strong>دوار السيف - Seef District Roundabout</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Coordinates:</span>
+                        <strong>26.2397, 50.5369</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Road Type:</span>
+                        <strong>Roundabout</strong>
+                    </div>
+                </div>
+                <div class="info-box">
+                    <h3>Conditions</h3>
+                    <div class="info-row">
+                        <span>Date/Time:</span>
+                        <strong>2025-12-30 10:30</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Weather:</span>
+                        <strong>Clear</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Road Condition:</span>
+                        <strong>Dry</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Vehicle Information -->
+        <div class="section">
+            <h2>🚙 Vehicle Information</h2>
+            <div class="info-grid">
+                <div class="vehicle-card v1">
+                    <h3 style="color: #FF4B4B;">Vehicle 1 (Red)</h3>
+                    <div class="info-row">
+                        <span>Type:</span>
+                        <strong>Sedan</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Speed:</span>
+                        <strong>45 km/h</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Direction:</span>
+                        <strong>North</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Action:</span>
+                        <strong>Entering Roundabout</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Braking:</span>
+                        <strong>No</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Signaling:</span>
+                        <strong>Yes</strong>
+                    </div>
+                </div>
+                <div class="vehicle-card v2">
+                    <h3 style="color: #4B7BFF;">Vehicle 2 (Blue)</h3>
+                    <div class="info-row">
+                        <span>Type:</span>
+                        <strong>SUV</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Speed:</span>
+                        <strong>55 km/h</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Direction:</span>
+                        <strong>East</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Action:</span>
+                        <strong>Going Straight</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Braking:</span>
+                        <strong>Yes</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Signaling:</span>
+                        <strong>No</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Generated Scenarios -->
+        <div class="section">
+            <h2>🎯 AI-Generated Scenarios</h2>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 1: Side Impact</h3>
+                    </div>
+                    <div class="probability high">
+                        95.3%
+                    </div>
+                </div>
+                <p>A Sedan was struck on the side by a SUV at an intersection. The impact angle was approximately 90 degrees.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li><li>Distracted Driving</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 2: Roundabout Entry Collision</h3>
+                    </div>
+                    <div class="probability low">
+                        1.8%
+                    </div>
+                </div>
+                <p>A Sedan entering the roundabout collided with a SUV already circulating within the roundabout.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li><li>Running Red Light</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 3: Intersection Collision</h3>
+                    </div>
+                    <div class="probability low">
+                        1.0%
+                    </div>
+                </div>
+                <p>Both vehicles entered the intersection simultaneously, resulting in a collision at the crossing point.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li><li>Running Red Light</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 4: Lane Change Collision</h3>
+                    </div>
+                    <div class="probability low">
+                        1.0%
+                    </div>
+                </div>
+                <p>A collision occurred when one vehicle changed lanes without properly checking for the other vehicle.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Improper Lane Change</li><li>Improper Turn</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 5: Sideswipe</h3>
+                    </div>
+                    <div class="probability low">
+                        1.0%
+                    </div>
+                </div>
+                <p>Both vehicles were traveling in similar directions when a Sedan sideswiped a SUV during a lane change or merge.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Improper Lane Change</li>
+                    </ul>
+                </div>
+            </div>
+        </div>
+        <!-- Fault Assessment -->
+        <div class="section">
+            <h2>⚖️ Preliminary Fault Assessment</h2>
+            <div class="fault-assessment">
+                <h3>⚠️ Disclaimer</h3>
+                <p>This is a preliminary AI-generated assessment for reference purposes only. Final fault determination should be made by qualified traffic authorities based on comprehensive investigation.</p>
+            </div>
+            <div class="info-grid" style="margin-top: 20px;">
+                <div class="info-box">
+                    <h3>Contribution Analysis</h3>
+                    <div style="margin: 15px 0;">
+                        <span style="color: #FF4B4B;">Vehicle 1: 36.4%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 36.44859813084113%; background: #FF4B4B;"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span style="color: #4B7BFF;">Vehicle 2: 63.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 63.55140186915889%; background: #4B7BFF;"></div>
+                        </div>
+                    </div>
+                </div>
+                <div class="info-box">
+                    <h3>Assessment Summary</h3>
+                    <div class="info-row">
+                        <span>Higher Contribution:</span>
+                        <strong>Vehicle 2</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Primary Factor:</span>
+                        <strong>Failure To Yield</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Assessment Confidence:</span>
+                        <strong>63.6%</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Timeline -->
+        <div class="section">
+            <h2>⏱️ Estimated Accident Timeline</h2>
+            <div class="timeline">
+                <div class="timeline-item">
+                    <div class="timeline-time ">-1.2s</div>
+                    <div class="timeline-event">Vehicles approaching conflict zone</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.7s</div>
+                    <div class="timeline-event">Vehicle paths begin to converge</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.3s</div>
+                    <div class="timeline-event">Collision becomes imminent</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.1s</div>
+                    <div class="timeline-event">Point of no return - evasive action no longer possible</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time impact">+0.0s</div>
+                    <div class="timeline-event">Impact - Collision occurs</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time after">+0.5s</div>
+                    <div class="timeline-event">Vehicles come to rest after impact</div>
+                </div>
+            </div>
+        </div>
+        <!-- Footer -->
+        <div class="footer">
+            <p><strong>Traffic Accident Reconstruction System</strong></p>
+            <p>Huawei AI Innovation Challenge 2026 | Jubail Industrial College</p>
+            <p style="margin-top: 10px;">Powered by Huawei MindSpore AI Framework</p>
+            <p style="margin-top: 15px; font-size: 0.8rem;">Report ID: 20251230_091851</p>
+        </div>
+    </div>
+</body>
+</html>

output/reports/accident_report_20251230_124847.html ADDED Viewed

	@@ -0,0 +1,688 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Traffic Accident Analysis Report</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            line-height: 1.6;
+            color: #333;
+            background: #f5f5f5;
+        }
+        .container {
+            max-width: 1000px;
+            margin: 0 auto;
+            background: white;
+            box-shadow: 0 0 20px rgba(0,0,0,0.1);
+        }
+        .header {
+            background: linear-gradient(135deg, #1e3a5f 0%, #2d5a87 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }
+        .header h1 {
+            font-size: 2.5rem;
+            margin-bottom: 10px;
+        }
+        .header .subtitle {
+            opacity: 0.9;
+            font-size: 1.1rem;
+        }
+        .section {
+            padding: 30px 40px;
+            border-bottom: 1px solid #eee;
+        }
+        .section h2 {
+            color: #1e3a5f;
+            margin-bottom: 20px;
+            padding-bottom: 10px;
+            border-bottom: 2px solid #2d5a87;
+        }
+        .summary-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+            margin: 20px 0;
+        }
+        .summary-card {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+            text-align: center;
+            border-top: 4px solid #2d5a87;
+        }
+        .summary-card .label {
+            font-size: 0.9rem;
+            color: #666;
+            margin-bottom: 5px;
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+        .summary-card .value {
+            font-size: 1.8rem;
+            font-weight: bold;
+            color: #1e3a5f;
+        }
+        .summary-card .delta {
+            font-size: 0.85rem;
+            margin-top: 5px;
+        }
+        .delta.high { color: #28a745; }
+        .delta.medium { color: #ffc107; }
+        .delta.low { color: #dc3545; }
+        .info-grid {
+            display: grid;
+            grid-template-columns: repeat(2, 1fr);
+            gap: 30px;
+        }
+        .info-box {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+        }
+        .info-box h3 {
+            color: #1e3a5f;
+            margin-bottom: 15px;
+        }
+        .info-row {
+            display: flex;
+            justify-content: space-between;
+            padding: 8px 0;
+            border-bottom: 1px solid #eee;
+        }
+        .info-row:last-child {
+            border-bottom: none;
+        }
+        .vehicle-card {
+            background: white;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 15px 0;
+        }
+        .vehicle-card.v1 {
+            border-left: 4px solid #FF4B4B;
+        }
+        .vehicle-card.v2 {
+            border-left: 4px solid #4B7BFF;
+        }
+        .scenario-card {
+            background: #f8f9fa;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 15px 0;
+            border-left: 4px solid #2d5a87;
+        }
+        .scenario-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 15px;
+        }
+        .probability {
+            font-size: 1.5rem;
+            font-weight: bold;
+        }
+        .probability.high { color: #28a745; }
+        .probability.medium { color: #ffc107; }
+        .probability.low { color: #dc3545; }
+        .progress-bar {
+            background: #e9ecef;
+            border-radius: 5px;
+            height: 10px;
+            margin: 10px 0;
+            overflow: hidden;
+        }
+        .progress-fill {
+            background: #2d5a87;
+            height: 100%;
+            border-radius: 5px;
+        }
+        .factors-list {
+            list-style: none;
+            padding: 0;
+        }
+        .factors-list li {
+            padding: 5px 0;
+            padding-left: 20px;
+            position: relative;
+        }
+        .factors-list li::before {
+            content: "•";
+            color: #2d5a87;
+            position: absolute;
+            left: 0;
+        }
+        .timeline {
+            margin: 20px 0;
+        }
+        .timeline-item {
+            display: flex;
+            margin: 10px 0;
+        }
+        .timeline-time {
+            min-width: 80px;
+            padding: 8px 15px;
+            background: #ffc107;
+            color: white;
+            font-weight: bold;
+            text-align: center;
+            border-radius: 5px;
+        }
+        .timeline-time.impact {
+            background: #dc3545;
+        }
+        .timeline-time.after {
+            background: #28a745;
+        }
+        .timeline-event {
+            flex: 1;
+            padding: 8px 15px;
+            background: #f8f9fa;
+            margin-left: 10px;
+            border-radius: 5px;
+        }
+        .fault-assessment {
+            background: #fff3cd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 4px solid #ffc107;
+            margin: 20px 0;
+        }
+        .footer {
+            background: #1e3a5f;
+            color: white;
+            padding: 30px 40px;
+            text-align: center;
+        }
+        .footer p {
+            opacity: 0.8;
+            font-size: 0.9rem;
+        }
+        @media print {
+            .container {
+                box-shadow: none;
+            }
+            .section {
+                page-break-inside: avoid;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Header -->
+        <div class="header">
+            <h1>🚗 Traffic Accident Analysis Report</h1>
+            <p class="subtitle">AI-Powered Analysis using Huawei MindSpore</p>
+            <p style="margin-top: 15px; opacity: 0.7;">Generated: December 30, 2025 at 12:48</p>
+        </div>
+        <!-- Executive Summary -->
+        <div class="section">
+            <h2>📊 Executive Summary</h2>
+            <div class="summary-grid">
+                <div class="summary-card">
+                    <div class="label">Most Likely Scenario</div>
+                    <div class="value">#1</div>
+                    <div class="delta high">27.9% probability</div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Scenarios Generated</div>
+                    <div class="value">5</div>
+                    <div class="delta">AI-generated</div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Collision Certainty</div>
+                    <div class="value">86.0%</div>
+                    <div class="delta high">
+                        High
+                    </div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Primary Factor</div>
+                    <div class="value" style="font-size: 1.2rem;">Failure To Yield</div>
+                    <div class="delta">Vehicle 2</div>
+                </div>
+            </div>
+        </div>
+        <!-- Accident Details -->
+        <div class="section">
+            <h2>📍 Accident Details</h2>
+            <div class="info-grid">
+                <div class="info-box">
+                    <h3>Location Information</h3>
+                    <div class="info-row">
+                        <span>Location:</span>
+                        <strong>دوار السيف - Seef District Roundabout</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Coordinates:</span>
+                        <strong>26.2397, 50.5369</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Road Type:</span>
+                        <strong>Roundabout</strong>
+                    </div>
+                </div>
+                <div class="info-box">
+                    <h3>Conditions</h3>
+                    <div class="info-row">
+                        <span>Date/Time:</span>
+                        <strong>2025-12-30 10:30</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Weather:</span>
+                        <strong>Clear</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Road Condition:</span>
+                        <strong>Dry</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Vehicle Information -->
+        <div class="section">
+            <h2>🚙 Vehicle Information</h2>
+            <div class="info-grid">
+                <div class="vehicle-card v1">
+                    <h3 style="color: #FF4B4B;">Vehicle 1 (Red)</h3>
+                    <div class="info-row">
+                        <span>Type:</span>
+                        <strong>Sedan</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Speed:</span>
+                        <strong>45 km/h</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Direction:</span>
+                        <strong>North</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Action:</span>
+                        <strong>Entering Roundabout</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Braking:</span>
+                        <strong>No</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Signaling:</span>
+                        <strong>Yes</strong>
+                    </div>
+                </div>
+                <div class="vehicle-card v2">
+                    <h3 style="color: #4B7BFF;">Vehicle 2 (Blue)</h3>
+                    <div class="info-row">
+                        <span>Type:</span>
+                        <strong>SUV</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Speed:</span>
+                        <strong>55 km/h</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Direction:</span>
+                        <strong>East</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Action:</span>
+                        <strong>Going Straight</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Braking:</span>
+                        <strong>Yes</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Signaling:</span>
+                        <strong>No</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Generated Scenarios -->
+        <div class="section">
+            <h2>🎯 AI-Generated Scenarios</h2>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 1: Side Impact</h3>
+                    </div>
+                    <div class="probability medium">
+                        27.9%
+                    </div>
+                </div>
+                <p>A Sedan was struck on the side by a SUV at an intersection. The impact angle was approximately 90 degrees.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 2: Roundabout Entry Collision</h3>
+                    </div>
+                    <div class="probability medium">
+                        23.0%
+                    </div>
+                </div>
+                <p>A Sedan entering the roundabout collided with a SUV already circulating within the roundabout.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li><li>Improper Turn</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 3: Intersection Collision</h3>
+                    </div>
+                    <div class="probability medium">
+                        23.0%
+                    </div>
+                </div>
+                <p>Both vehicles entered the intersection simultaneously, resulting in a collision at the crossing point.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 4: Lane Change Collision</h3>
+                    </div>
+                    <div class="probability low">
+                        19.7%
+                    </div>
+                </div>
+                <p>A collision occurred when one vehicle changed lanes without properly checking for the other vehicle.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Improper Lane Change</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 5: Sideswipe</h3>
+                    </div>
+                    <div class="probability low">
+                        6.6%
+                    </div>
+                </div>
+                <p>Both vehicles were traveling in similar directions when a Sedan sideswiped a SUV during a lane change or merge.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Improper Lane Change</li><li>Distracted Driving</li>
+                    </ul>
+                </div>
+            </div>
+        </div>
+        <!-- Fault Assessment -->
+        <div class="section">
+            <h2>⚖️ Preliminary Fault Assessment</h2>
+            <div class="fault-assessment">
+                <h3>⚠️ Disclaimer</h3>
+                <p>This is a preliminary AI-generated assessment for reference purposes only. Final fault determination should be made by qualified traffic authorities based on comprehensive investigation.</p>
+            </div>
+            <div class="info-grid" style="margin-top: 20px;">
+                <div class="info-box">
+                    <h3>Contribution Analysis</h3>
+                    <div style="margin: 15px 0;">
+                        <span style="color: #FF4B4B;">Vehicle 1: 36.4%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 36.44859813084113%; background: #FF4B4B;"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span style="color: #4B7BFF;">Vehicle 2: 63.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 63.55140186915889%; background: #4B7BFF;"></div>
+                        </div>
+                    </div>
+                </div>
+                <div class="info-box">
+                    <h3>Assessment Summary</h3>
+                    <div class="info-row">
+                        <span>Higher Contribution:</span>
+                        <strong>Vehicle 2</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Primary Factor:</span>
+                        <strong>Failure To Yield</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Assessment Confidence:</span>
+                        <strong>63.6%</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Timeline -->
+        <div class="section">
+            <h2>⏱️ Estimated Accident Timeline</h2>
+            <div class="timeline">
+                <div class="timeline-item">
+                    <div class="timeline-time ">-1.2s</div>
+                    <div class="timeline-event">Vehicles approaching conflict zone</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.7s</div>
+                    <div class="timeline-event">Vehicle paths begin to converge</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.3s</div>
+                    <div class="timeline-event">Collision becomes imminent</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.1s</div>
+                    <div class="timeline-event">Point of no return - evasive action no longer possible</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time impact">+0.0s</div>
+                    <div class="timeline-event">Impact - Collision occurs</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time after">+0.5s</div>
+                    <div class="timeline-event">Vehicles come to rest after impact</div>
+                </div>
+            </div>
+        </div>
+        <!-- Footer -->
+        <div class="footer">
+            <p><strong>Traffic Accident Reconstruction System</strong></p>
+            <p>Huawei AI Innovation Challenge 2026 | Jubail Industrial College</p>
+            <p style="margin-top: 10px;">Powered by Huawei MindSpore AI Framework</p>
+            <p style="margin-top: 15px; font-size: 0.8rem;">Report ID: 20251230_124847</p>
+        </div>
+    </div>
+</body>
+</html>

output/reports/accident_report_20251231_092601.html ADDED Viewed

	@@ -0,0 +1,688 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Traffic Accident Analysis Report</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            line-height: 1.6;
+            color: #333;
+            background: #f5f5f5;
+        }
+        .container {
+            max-width: 1000px;
+            margin: 0 auto;
+            background: white;
+            box-shadow: 0 0 20px rgba(0,0,0,0.1);
+        }
+        .header {
+            background: linear-gradient(135deg, #1e3a5f 0%, #2d5a87 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }
+        .header h1 {
+            font-size: 2.5rem;
+            margin-bottom: 10px;
+        }
+        .header .subtitle {
+            opacity: 0.9;
+            font-size: 1.1rem;
+        }
+        .section {
+            padding: 30px 40px;
+            border-bottom: 1px solid #eee;
+        }
+        .section h2 {
+            color: #1e3a5f;
+            margin-bottom: 20px;
+            padding-bottom: 10px;
+            border-bottom: 2px solid #2d5a87;
+        }
+        .summary-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+            margin: 20px 0;
+        }
+        .summary-card {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+            text-align: center;
+            border-top: 4px solid #2d5a87;
+        }
+        .summary-card .label {
+            font-size: 0.9rem;
+            color: #666;
+            margin-bottom: 5px;
+        }
+        .summary-card .value {
+            font-size: 1.8rem;
+            font-weight: bold;
+            color: #1e3a5f;
+        }
+        .summary-card .delta {
+            font-size: 0.85rem;
+            margin-top: 5px;
+        }
+        .delta.high { color: #28a745; }
+        .delta.medium { color: #ffc107; }
+        .delta.low { color: #dc3545; }
+        .info-grid {
+            display: grid;
+            grid-template-columns: repeat(2, 1fr);
+            gap: 30px;
+        }
+        .info-box {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+        }
+        .info-box h3 {
+            color: #1e3a5f;
+            margin-bottom: 15px;
+        }
+        .info-row {
+            display: flex;
+            justify-content: space-between;
+            padding: 8px 0;
+            border-bottom: 1px solid #eee;
+        }
+        .info-row:last-child {
+            border-bottom: none;
+        }
+        .vehicle-card {
+            background: white;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 15px 0;
+        }
+        .vehicle-card.v1 {
+            border-left: 4px solid #FF4B4B;
+        }
+        .vehicle-card.v2 {
+            border-left: 4px solid #4B7BFF;
+        }
+        .scenario-card {
+            background: #f8f9fa;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 15px 0;
+            border-left: 4px solid #2d5a87;
+        }
+        .scenario-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 15px;
+        }
+        .probability {
+            font-size: 1.5rem;
+            font-weight: bold;
+        }
+        .probability.high { color: #28a745; }
+        .probability.medium { color: #ffc107; }
+        .probability.low { color: #dc3545; }
+        .progress-bar {
+            background: #e9ecef;
+            border-radius: 5px;
+            height: 10px;
+            margin: 10px 0;
+            overflow: hidden;
+        }
+        .progress-fill {
+            background: #2d5a87;
+            height: 100%;
+            border-radius: 5px;
+        }
+        .factors-list {
+            list-style: none;
+            padding: 0;
+        }
+        .factors-list li {
+            padding: 5px 0;
+            padding-left: 20px;
+            position: relative;
+        }
+        .factors-list li::before {
+            content: "•";
+            color: #2d5a87;
+            position: absolute;
+            left: 0;
+        }
+        .timeline {
+            margin: 20px 0;
+        }
+        .timeline-item {
+            display: flex;
+            margin: 10px 0;
+        }
+        .timeline-time {
+            min-width: 80px;
+            padding: 8px 15px;
+            background: #ffc107;
+            color: white;
+            font-weight: bold;
+            text-align: center;
+            border-radius: 5px;
+        }
+        .timeline-time.impact {
+            background: #dc3545;
+        }
+        .timeline-time.after {
+            background: #28a745;
+        }
+        .timeline-event {
+            flex: 1;
+            padding: 8px 15px;
+            background: #f8f9fa;
+            margin-left: 10px;
+            border-radius: 5px;
+        }
+        .fault-assessment {
+            background: #fff3cd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 4px solid #ffc107;
+            margin: 20px 0;
+        }
+        .footer {
+            background: #1e3a5f;
+            color: white;
+            padding: 30px 40px;
+            text-align: center;
+        }
+        .footer p {
+            opacity: 0.8;
+            font-size: 0.9rem;
+        }
+        @media print {
+            .container {
+                box-shadow: none;
+            }
+            .section {
+                page-break-inside: avoid;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Header -->
+        <div class="header">
+            <h1>🚗 Traffic Accident Analysis Report</h1>
+            <p class="subtitle">AI-Powered Analysis using Huawei MindSpore</p>
+            <p style="margin-top: 15px; opacity: 0.7;">Generated: December 31, 2025 at 09:26</p>
+        </div>
+        <!-- Executive Summary -->
+        <div class="section">
+            <h2>📊 Executive Summary</h2>
+            <div class="summary-grid">
+                <div class="summary-card">
+                    <div class="label">Most Likely Scenario</div>
+                    <div class="value">#1</div>
+                    <div class="delta high">96.1% probability</div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Scenarios Generated</div>
+                    <div class="value">5</div>
+                    <div class="delta">AI-generated</div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Collision Certainty</div>
+                    <div class="value">86.0%</div>
+                    <div class="delta high">
+                        High
+                    </div>
+                </div>
+                <div class="summary-card">
+                    <div class="label">Primary Factor</div>
+                    <div class="value" style="font-size: 1.2rem;">Failure To Yield</div>
+                    <div class="delta">Vehicle 2</div>
+                </div>
+            </div>
+        </div>
+        <!-- Accident Details -->
+        <div class="section">
+            <h2>📍 Accident Details</h2>
+            <div class="info-grid">
+                <div class="info-box">
+                    <h3>Location Information</h3>
+                    <div class="info-row">
+                        <span>Location:</span>
+                        <strong>دوار السيف - Seef District Roundabout</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Coordinates:</span>
+                        <strong>26.2397, 50.5369</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Road Type:</span>
+                        <strong>Roundabout</strong>
+                    </div>
+                </div>
+                <div class="info-box">
+                    <h3>Conditions</h3>
+                    <div class="info-row">
+                        <span>Date/Time:</span>
+                        <strong>2025-12-30 10:30</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Weather:</span>
+                        <strong>Clear</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Road Condition:</span>
+                        <strong>Dry</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Vehicle Information -->
+        <div class="section">
+            <h2>🚙 Vehicle Information</h2>
+            <div class="info-grid">
+                <div class="vehicle-card v1">
+                    <h3 style="color: #FF4B4B;">Vehicle 1 (Red)</h3>
+                    <div class="info-row">
+                        <span>Type:</span>
+                        <strong>Sedan</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Speed:</span>
+                        <strong>45 km/h</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Direction:</span>
+                        <strong>North</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Action:</span>
+                        <strong>Entering Roundabout</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Braking:</span>
+                        <strong>No</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Signaling:</span>
+                        <strong>Yes</strong>
+                    </div>
+                </div>
+                <div class="vehicle-card v2">
+                    <h3 style="color: #4B7BFF;">Vehicle 2 (Blue)</h3>
+                    <div class="info-row">
+                        <span>Type:</span>
+                        <strong>SUV</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Speed:</span>
+                        <strong>55 km/h</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Direction:</span>
+                        <strong>East</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Action:</span>
+                        <strong>Going Straight</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Braking:</span>
+                        <strong>Yes</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Signaling:</span>
+                        <strong>No</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Generated Scenarios -->
+        <div class="section">
+            <h2>🎯 AI-Generated Scenarios</h2>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 1: Side Impact</h3>
+                    </div>
+                    <div class="probability high">
+                        96.1%
+                    </div>
+                </div>
+                <p>A Sedan was struck on the side by a SUV at an intersection. The impact angle was approximately 90 degrees.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 2: Rear End Collision</h3>
+                    </div>
+                    <div class="probability low">
+                        1.0%
+                    </div>
+                </div>
+                <p>A SUV traveling at 55 km/h rear-ended a Sedan traveling at 45 km/h in the same direction.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Following Too Closely</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 3: Roundabout Entry Collision</h3>
+                    </div>
+                    <div class="probability low">
+                        1.0%
+                    </div>
+                </div>
+                <p>A Sedan entering the roundabout collided with a SUV already circulating within the roundabout.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Failure To Yield</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 4: Lane Change Collision</h3>
+                    </div>
+                    <div class="probability low">
+                        1.0%
+                    </div>
+                </div>
+                <p>A collision occurred when one vehicle changed lanes without properly checking for the other vehicle.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Improper Lane Change</li><li>Improper Turn</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="scenario-card">
+                <div class="scenario-header">
+                    <div>
+                        <h3>Scenario 5: Head On Collision</h3>
+                    </div>
+                    <div class="probability low">
+                        1.0%
+                    </div>
+                </div>
+                <p>A Sedan traveling north at 45 km/h collided head-on with a SUV traveling east at 55 km/h.</p>
+                <div style="margin-top: 15px;">
+                    <strong>Analysis Metrics:</strong>
+                    <div style="margin-top: 10px;">
+                        <span>Collision Probability: 86.0%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 86.0440679248718%"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span>Path Overlap: 87.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 87.64689308290596%"></div>
+                        </div>
+                    </div>
+                    <p style="margin-top: 10px;">Speed Differential: 10.0 km/h | Time to Collision: 1.16s</p>
+                </div>
+                <div style="margin-top: 15px;">
+                    <strong>Contributing Factors:</strong>
+                    <ul class="factors-list">
+                        <li>Improper Turn</li>
+                    </ul>
+                </div>
+            </div>
+        </div>
+        <!-- Fault Assessment -->
+        <div class="section">
+            <h2>⚖️ Preliminary Fault Assessment</h2>
+            <div class="fault-assessment">
+                <h3>⚠️ Disclaimer</h3>
+                <p>This is a preliminary AI-generated assessment for reference purposes only. Final fault determination should be made by qualified traffic authorities based on comprehensive investigation.</p>
+            </div>
+            <div class="info-grid" style="margin-top: 20px;">
+                <div class="info-box">
+                    <h3>Contribution Analysis</h3>
+                    <div style="margin: 15px 0;">
+                        <span style="color: #FF4B4B;">Vehicle 1: 36.4%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 36.44859813084113%; background: #FF4B4B;"></div>
+                        </div>
+                    </div>
+                    <div>
+                        <span style="color: #4B7BFF;">Vehicle 2: 63.6%</span>
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: 63.55140186915889%; background: #4B7BFF;"></div>
+                        </div>
+                    </div>
+                </div>
+                <div class="info-box">
+                    <h3>Assessment Summary</h3>
+                    <div class="info-row">
+                        <span>Higher Contribution:</span>
+                        <strong>Vehicle 2</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Primary Factor:</span>
+                        <strong>Failure To Yield</strong>
+                    </div>
+                    <div class="info-row">
+                        <span>Assessment Confidence:</span>
+                        <strong>63.6%</strong>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- Timeline -->
+        <div class="section">
+            <h2>⏱️ Estimated Accident Timeline</h2>
+            <div class="timeline">
+                <div class="timeline-item">
+                    <div class="timeline-time ">-1.2s</div>
+                    <div class="timeline-event">Vehicles approaching conflict zone</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.7s</div>
+                    <div class="timeline-event">Vehicle paths begin to converge</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.3s</div>
+                    <div class="timeline-event">Collision becomes imminent</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time ">-0.1s</div>
+                    <div class="timeline-event">Point of no return - evasive action no longer possible</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time impact">+0.0s</div>
+                    <div class="timeline-event">Impact - Collision occurs</div>
+                </div>
+                <div class="timeline-item">
+                    <div class="timeline-time after">+0.5s</div>
+                    <div class="timeline-event">Vehicles come to rest after impact</div>
+                </div>
+            </div>
+        </div>
+        <!-- Footer -->
+        <div class="footer">
+            <p><strong>Traffic Accident Reconstruction System</strong></p>
+            <p>Huawei AI Innovation Challenge 2026 | Jubail Industrial College</p>
+            <p style="margin-top: 10px;">Powered by Huawei MindSpore AI Framework</p>
+            <p style="margin-top: 15px; font-size: 0.8rem;">Report ID: 20251231_092601</p>
+        </div>
+    </div>
+</body>
+</html>

requirements.txt CHANGED Viewed

@@ -1,31 +1,31 @@
-# CrashLens - Traffic Accident Reconstruction System
 # Huawei AI Innovation Challenge 2026
-# Core Web Framework
-streamlit==1.29.0
-# AI/ML - ONNX Runtime
-onnxruntime==1.16.3
-# Fix protobuf version conflict
 protobuf>=3.20.0,<4.0.0
-# Map - FIXED VERSIONS (this fixes the WebSocket crash!)
-folium==0.14.0
-streamlit-folium==0.13.0
 # Data Processing
-numpy==1.24.3
-pandas==2.0.3
 # Visualization
-matplotlib==3.7.3
-plotly==5.18.0
 # Report Generation
-jinja2==3.1.2
 # Utilities
-python-dateutil==2.8.2
-requests==2.31.0
-psutil==5.9.6

+# Traffic Accident Reconstruction System
 # Huawei AI Innovation Challenge 2026
+# =====================================
+# AI/ML - ONNX Runtime (works on Windows/Mac/Linux!)
+onnxruntime>=1.16.0
+# IMPORTANT: Fix protobuf version conflict
 protobuf>=3.20.0,<4.0.0
+# Web Interface
+streamlit>=1.29.0
+streamlit-folium>=0.15.0
+# Map and Geographic Data
+folium>=0.15.0
 # Data Processing
+numpy>=1.24.0
+pandas>=2.0.0
 # Visualization
+matplotlib>=3.7.0
+plotly>=5.18.0
 # Report Generation
+jinja2>=3.1.0
 # Utilities
+python-dateutil>=2.8.0
+requests>=2.31.0

ui/map_viewer.py CHANGED Viewed

@@ -1,20 +1,33 @@
 """
-Map Viewer Component - FIXED VERSION
-Uses folium_static to avoid WebSocket crashes
 """
 import streamlit as st
 import folium
-from streamlit_folium import folium_static
 from config import CASE_STUDY_LOCATION, MAP_CONFIG, COLORS
 def create_base_map(location: dict = None) -> folium.Map:
-    """Create a base Folium map."""
     if location is None:
         location = CASE_STUDY_LOCATION
     m = folium.Map(
         location=[location['latitude'], location['longitude']],
         zoom_start=MAP_CONFIG['default_zoom'],
@@ -29,11 +42,63 @@ def create_base_map(location: dict = None) -> folium.Map:
         tooltip="Accident Location"
     ).add_to(m)
     return m
 def add_vehicle_path(m: folium.Map, path: list, vehicle_id: int) -> folium.Map:
-    """Add vehicle path to map."""
     if not path or len(path) < 2:
         return m
@@ -45,28 +110,59 @@ def add_vehicle_path(m: folium.Map, path: list, vehicle_id: int) -> folium.Map:
         color=color,
         weight=4,
         opacity=0.8,
-        popup=f"Vehicle {vehicle_id} Path"
     ).add_to(m)
     # Add start marker
     folium.Marker(
         location=path[0],
         popup=f"Vehicle {vehicle_id} Start",
-        icon=folium.Icon(color='green' if vehicle_id == 1 else 'blue', icon='play')
     ).add_to(m)
-    # Add end marker
     folium.Marker(
         location=path[-1],
         popup=f"Vehicle {vehicle_id} End",
-        icon=folium.Icon(color='red' if vehicle_id == 1 else 'darkblue', icon='stop')
     ).add_to(m)
     return m
 def add_collision_point(m: folium.Map, collision_point: list) -> folium.Map:
-    """Add collision point marker."""
     if not collision_point:
         return m
@@ -77,6 +173,7 @@ def add_collision_point(m: folium.Map, collision_point: list) -> folium.Map:
         tooltip="💥 Collision Point"
     ).add_to(m)
     folium.Circle(
         location=collision_point,
         radius=10,
@@ -91,23 +188,212 @@ def add_collision_point(m: folium.Map, collision_point: list) -> folium.Map:
 def render_map_section(vehicle_id: int = None):
     """
-    Render the map section - FIXED VERSION (static display)
-    Shows accident location on map
     """
     location = st.session_state.accident_info['location']
-    # Create map
     m = create_base_map(location)
-    # Display static map (NO CRASHES!)
-    folium_static(m, width=700, height=400)
-    st.success(f"📍 Accident Location: {location.get('name', 'Unknown')}")
 def render_results_map(scenarios: list, selected_scenario: int = 0):
     """
     Render a map showing analysis results for a specific scenario.
     """
     if not scenarios:
         st.warning("No scenarios to display")
@@ -119,23 +405,23 @@ def render_results_map(scenarios: list, selected_scenario: int = 0):
     # Create map
     m = create_base_map(location)
-    # Add vehicle paths if available
     if scenario.get('vehicle_1_path'):
         m = add_vehicle_path(m, scenario['vehicle_1_path'], 1)
     if scenario.get('vehicle_2_path'):
         m = add_vehicle_path(m, scenario['vehicle_2_path'], 2)
-    # Add collision point if available
     if scenario.get('collision_point'):
         m = add_collision_point(m, scenario['collision_point'])
-    # Add scenario info
     info_html = f"""
     <div style="width: 200px;">
         <h4>Scenario {selected_scenario + 1}</h4>
         <p><b>Probability:</b> {scenario.get('probability', 0)*100:.1f}%</p>
-        <p><b>Type:</b> {scenario.get('type', 'Unknown').replace('_', ' ').title()}</p>
     </div>
     """
@@ -145,5 +431,5 @@ def render_results_map(scenarios: list, selected_scenario: int = 0):
         icon=folium.Icon(color='purple', icon='info-sign')
     ).add_to(m)
-    # Display static map
     folium_static(m, width=700, height=500)

 """
+Map Viewer Component
+====================
+Handles map display and vehicle path drawing using Folium.
 """
 import streamlit as st
 import folium
+from streamlit_folium import st_folium, folium_static
+from folium.plugins import Draw
+import json
+from typing import List
 from config import CASE_STUDY_LOCATION, MAP_CONFIG, COLORS
 def create_base_map(location: dict = None) -> folium.Map:
+    """
+    Create a base Folium map centered on the accident location.
+    Args:
+        location: Dictionary with latitude, longitude, and name
+    Returns:
+        Folium Map object
+    """
     if location is None:
         location = CASE_STUDY_LOCATION
+    # Create map
     m = folium.Map(
         location=[location['latitude'], location['longitude']],
         zoom_start=MAP_CONFIG['default_zoom'],
         tooltip="Accident Location"
     ).add_to(m)
+    # Add circle to show area of interest
+    folium.Circle(
+        location=[location['latitude'], location['longitude']],
+        radius=location.get('radius_meters', 200),
+        color='blue',
+        fill=True,
+        fill_opacity=0.1,
+        popup="Analysis Area"
+    ).add_to(m)
+    return m
+def add_draw_control(m: folium.Map) -> folium.Map:
+    """
+    Add drawing controls to the map for path definition.
+    Args:
+        m: Folium Map object
+    Returns:
+        Folium Map with draw controls
+    """
+    draw = Draw(
+        draw_options={
+            'polyline': {
+                'allowIntersection': True,
+                'shapeOptions': {
+                    'color': '#FF4B4B',
+                    'weight': 4
+                }
+            },
+            'polygon': False,
+            'circle': False,
+            'rectangle': False,
+            'circlemarker': False,
+            'marker': True
+        },
+        edit_options={'edit': True, 'remove': True}
+    )
+    draw.add_to(m)
     return m
 def add_vehicle_path(m: folium.Map, path: list, vehicle_id: int) -> folium.Map:
+    """
+    Add a vehicle path to the map.
+    Args:
+        m: Folium Map object
+        path: List of [lat, lng] coordinates
+        vehicle_id: 1 or 2 to determine color
+    Returns:
+        Folium Map with vehicle path
+    """
     if not path or len(path) < 2:
         return m
         color=color,
         weight=4,
         opacity=0.8,
+        popup=f"Vehicle {vehicle_id} Path",
+        tooltip=f"Vehicle {vehicle_id}"
     ).add_to(m)
     # Add start marker
     folium.Marker(
         location=path[0],
         popup=f"Vehicle {vehicle_id} Start",
+        icon=folium.Icon(
+            color='green' if vehicle_id == 1 else 'blue',
+            icon='play'
+        )
     ).add_to(m)
+    # Add end marker
     folium.Marker(
         location=path[-1],
         popup=f"Vehicle {vehicle_id} End",
+        icon=folium.Icon(
+            color='red' if vehicle_id == 1 else 'darkblue',
+            icon='stop'
+        )
     ).add_to(m)
+    # Add direction arrows
+    for i in range(len(path) - 1):
+        mid_lat = (path[i][0] + path[i+1][0]) / 2
+        mid_lng = (path[i][1] + path[i+1][1]) / 2
+        folium.RegularPolygonMarker(
+            location=[mid_lat, mid_lng],
+            number_of_sides=3,
+            radius=8,
+            color=color,
+            fill=True,
+            fill_color=color,
+            fill_opacity=0.7
+        ).add_to(m)
     return m
 def add_collision_point(m: folium.Map, collision_point: list) -> folium.Map:
+    """
+    Add a collision point marker to the map.
+    Args:
+        m: Folium Map object
+        collision_point: [lat, lng] of collision
+    Returns:
+        Folium Map with collision marker
+    """
     if not collision_point:
         return m
         tooltip="💥 Collision Point"
     ).add_to(m)
+    # Add impact radius
     folium.Circle(
         location=collision_point,
         radius=10,
 def render_map_section(vehicle_id: int = None):
     """
+    Render the map section in Streamlit.
+    Args:
+        vehicle_id: If provided, enables path drawing for that vehicle
     """
     location = st.session_state.accident_info['location']
+    # Create base map
     m = create_base_map(location)
+    # Add existing vehicle paths
+    if st.session_state.vehicle_1.get('path'):
+        m = add_vehicle_path(m, st.session_state.vehicle_1['path'], 1)
+    if st.session_state.vehicle_2.get('path'):
+        m = add_vehicle_path(m, st.session_state.vehicle_2['path'], 2)
+    # Add draw controls if editing a specific vehicle
+    if vehicle_id:
+        m = add_draw_control(m)
+        st.info(f"🖊️ Draw the path for Vehicle {vehicle_id} on the map. Click points to create a path, then click 'Finish' to complete.")
+    # Render map and get drawing data
+    map_data = st_folium(
+        m,
+        width=700,
+        height=500,
+        returned_objects=["last_active_drawing", "all_drawings"]
+    )
+    # Process drawn paths
+    if vehicle_id and map_data and map_data.get('last_active_drawing'):
+        drawing = map_data['last_active_drawing']
+        if drawing.get('geometry', {}).get('type') == 'LineString':
+            coords = drawing['geometry']['coordinates']
+            # Convert from [lng, lat] to [lat, lng]
+            path = [[c[1], c[0]] for c in coords]
+            if vehicle_id == 1:
+                st.session_state.vehicle_1['path'] = path
+            else:
+                st.session_state.vehicle_2['path'] = path
+            st.success(f"✅ Path saved for Vehicle {vehicle_id}!")
+    # Show path info and preset options
+    if vehicle_id:
+        vehicle_key = f'vehicle_{vehicle_id}'
+        current_path = st.session_state[vehicle_key].get('path', [])
+        if current_path:
+            st.success(f"**✅ Path defined:** {len(current_path)} points")
+        else:
+            st.warning("⚠️ No path drawn yet. Use the drawing tools on the map OR select a preset path below.")
+        # Preset path options for roundabout
+        st.markdown("---")
+        st.markdown("**🛣️ Quick Path Selection (Roundabout)**")
+        preset_col1, preset_col2 = st.columns(2)
+        with preset_col1:
+            entry_direction = st.selectbox(
+                f"Entry Direction (V{vehicle_id})",
+                options=['north', 'south', 'east', 'west'],
+                key=f"entry_dir_{vehicle_id}"
+            )
+        with preset_col2:
+            exit_direction = st.selectbox(
+                f"Exit Direction (V{vehicle_id})",
+                options=['north', 'south', 'east', 'west'],
+                index=2 if entry_direction == 'north' else 0,
+                key=f"exit_dir_{vehicle_id}"
+            )
+        if st.button(f"🔄 Generate Path for Vehicle {vehicle_id}", key=f"gen_path_{vehicle_id}"):
+            generated_path = generate_roundabout_path(
+                location['latitude'],
+                location['longitude'],
+                entry_direction,
+                exit_direction
+            )
+            if vehicle_id == 1:
+                st.session_state.vehicle_1['path'] = generated_path
+            else:
+                st.session_state.vehicle_2['path'] = generated_path
+            st.success(f"✅ Path generated: {entry_direction.title()} → {exit_direction.title()}")
+            st.rerun()
+        # Manual path input as fallback
+        with st.expander("📝 Or enter path manually"):
+            st.write("Enter coordinates as: lat1,lng1;lat2,lng2;...")
+            manual_path = st.text_input(
+                "Path coordinates",
+                key=f"manual_path_{vehicle_id}",
+                placeholder="26.2397,50.5369;26.2400,50.5372"
+            )
+            if st.button(f"Set Manual Path", key=f"set_path_{vehicle_id}"):
+                if manual_path:
+                    try:
+                        path = []
+                        for point in manual_path.split(';'):
+                            lat, lng = point.strip().split(',')
+                            path.append([float(lat), float(lng)])
+                        if vehicle_id == 1:
+                            st.session_state.vehicle_1['path'] = path
+                        else:
+                            st.session_state.vehicle_2['path'] = path
+                        st.success(f"Path set with {len(path)} points!")
+                        st.rerun()
+                    except Exception as e:
+                        st.error(f"Invalid format: {e}")
+def generate_roundabout_path(
+    center_lat: float,
+    center_lng: float,
+    entry_direction: str,
+    exit_direction: str
+) -> List[List[float]]:
+    """
+    Generate a realistic path through a roundabout.
+    Args:
+        center_lat: Center latitude of roundabout
+        center_lng: Center longitude of roundabout
+        entry_direction: Direction vehicle enters from
+        exit_direction: Direction vehicle exits to
+    Returns:
+        List of [lat, lng] coordinates forming the path
+    """
+    import math
+    # Roundabout parameters
+    approach_distance = 0.0015  # ~150 meters
+    roundabout_radius = 0.0004  # ~40 meters
+    # Direction to angle mapping (0 = North, clockwise)
+    dir_to_angle = {
+        'north': 90,    # Top
+        'east': 0,      # Right
+        'south': 270,   # Bottom
+        'west': 180     # Left
+    }
+    entry_angle = math.radians(dir_to_angle[entry_direction])
+    exit_angle = math.radians(dir_to_angle[exit_direction])
+    path = []
+    # Entry point (outside roundabout)
+    entry_lat = center_lat + approach_distance * math.sin(entry_angle)
+    entry_lng = center_lng + approach_distance * math.cos(entry_angle)
+    path.append([entry_lat, entry_lng])
+    # Entry to roundabout edge
+    edge_lat = center_lat + roundabout_radius * 1.5 * math.sin(entry_angle)
+    edge_lng = center_lng + roundabout_radius * 1.5 * math.cos(entry_angle)
+    path.append([edge_lat, edge_lng])
+    # Points along the roundabout (clockwise)
+    entry_deg = dir_to_angle[entry_direction]
+    exit_deg = dir_to_angle[exit_direction]
+    # Calculate arc (always go clockwise in roundabout)
+    if exit_deg <= entry_deg:
+        exit_deg += 360
+    # Add intermediate points along the arc
+    num_arc_points = max(2, (exit_deg - entry_deg) // 45)
+    for i in range(1, num_arc_points + 1):
+        angle = entry_deg + (exit_deg - entry_deg) * i / (num_arc_points + 1)
+        angle_rad = math.radians(angle)
+        arc_lat = center_lat + roundabout_radius * math.sin(angle_rad)
+        arc_lng = center_lng + roundabout_radius * math.cos(angle_rad)
+        path.append([arc_lat, arc_lng])
+    # Exit from roundabout edge
+    exit_edge_lat = center_lat + roundabout_radius * 1.5 * math.sin(exit_angle)
+    exit_edge_lng = center_lng + roundabout_radius * 1.5 * math.cos(exit_angle)
+    path.append([exit_edge_lat, exit_edge_lng])
+    # Exit point (outside roundabout)
+    exit_lat = center_lat + approach_distance * math.sin(exit_angle)
+    exit_lng = center_lng + approach_distance * math.cos(exit_angle)
+    path.append([exit_lat, exit_lng])
+    return path
 def render_results_map(scenarios: list, selected_scenario: int = 0):
     """
     Render a map showing analysis results for a specific scenario.
+    Args:
+        scenarios: List of generated scenarios
+        selected_scenario: Index of selected scenario
     """
     if not scenarios:
         st.warning("No scenarios to display")
     # Create map
     m = create_base_map(location)
+    # Add vehicle paths
     if scenario.get('vehicle_1_path'):
         m = add_vehicle_path(m, scenario['vehicle_1_path'], 1)
     if scenario.get('vehicle_2_path'):
         m = add_vehicle_path(m, scenario['vehicle_2_path'], 2)
+    # Add collision point
     if scenario.get('collision_point'):
         m = add_collision_point(m, scenario['collision_point'])
+    # Add scenario info popup
     info_html = f"""
     <div style="width: 200px;">
         <h4>Scenario {selected_scenario + 1}</h4>
         <p><b>Probability:</b> {scenario.get('probability', 0)*100:.1f}%</p>
+        <p><b>Type:</b> {scenario.get('accident_type', 'Unknown')}</p>
     </div>
     """
         icon=folium.Icon(color='purple', icon='info-sign')
     ).add_to(m)
+    # Display map
     folium_static(m, width=700, height=500)