Update streamlit_app.py

streamlit_app.py

@@ -1,814 +1,50 @@
+# app.py
 import streamlit as st
-import pandas as pd
-import matplotlib
-import matplotlib.pyplot  as plt
-import plotly.express as px
-import numpy as np
-import plotly.graph_objects as go
-# from blend_logic import run_dummy_prediction
-
-##---- fucntions ------
-import pandas as pd
-import streamlit as st
-
-# Load fuel data from CSV (create this file if it doesn't exist)
-FUEL_CSV_PATH = "fuel_properties.csv"
-
-def load_fuel_data():
-    """Load fuel data from CSV or create default if not exists"""
+import pkg_resources
+import os
+import stat
+
+st.title("📦 Installed Python Modules and Directory Tree with Permissions")
+
+# # --- Show installed packages ---
+# packages = sorted(
+#     [(d.project_name, d.version) for d in pkg_resources.working_set],
+#     key=lambda x: x[0].lower()
+# )
+# st.subheader("Installed Packages")
+# for name, version in packages:
+#     st.write(f"{name} — {version}")
+
+# --- Show directory structure with permissions ---
+st.subheader("Directory Structure (with Permissions)")
+root_path = "/"
+max_depth = 3  # limit to avoid too much output
+
+def get_permissions(path):
+    """Return Unix-style permission string for a file or directory."""
+    mode = os.stat(path).st_mode
+    return stat.filemode(mode)
+
+def walk_dir(path, depth=0):
+    if depth > max_depth:
+        return
     try:
-        df = pd.read_csv(FUEL_CSV_PATH, index_col=0)
-        return df.to_dict('index')
-    except FileNotFoundError:
-        # Create default fuel properties if file doesn't exist
-        default_fuels = {
-            "Gasoline": {f"Property{i+1}": round(0.7 + (i*0.02), 1) for i in range(10)},
-            "Diesel": {f"Property{i+1}": round(0.8 + (i*0.02), 1) for i in range(10)},
-            "Ethanol": {f"Property{i+1}": round(0.75 + (i*0.02), 1) for i in range(10)},
-            "Biodiesel": {f"Property{i+1}": round(0.85 + (i*0.02), 1) for i in range(10)},
-            "Jet Fuel": {f"Property{i+1}": round(0.78 + (i*0.02), 1) for i in range(10)}
-        }
-        pd.DataFrame(default_fuels).T.to_csv(FUEL_CSV_PATH)
-        return default_fuels
-
-# Initialize or load fuel data
-if 'FUEL_PROPERTIES' not in st.session_state:
-    st.session_state.FUEL_PROPERTIES = load_fuel_data()
-
-def save_fuel_data():
-    """Save current fuel data to CSV"""
-    pd.DataFrame(st.session_state.FUEL_PROPERTIES).T.to_csv(FUEL_CSV_PATH)
-
-# FUEL_PROPERTIES = st.session_state.FUEL_PROPERTIES
-
-# ---------------------- Page Config ----------------------
-st.set_page_config(
-    layout="wide",
-    page_title="Eagle Blend Optimizer",
-    page_icon="🦅",
-    initial_sidebar_state="expanded"
-)
-
-# ---------------------- Custom Styling ---------------------- ##e0e0e0;
-
-st.markdown("""
-    <style>
-            
-    .block-container {
-        padding-top: 1rem;
-    }
-    /* Main app background */
-    .stApp {
-        background-color: #f8f5f0;
-        overflow: visible;
-        padding-top: 0
-           
-    }
-        /* Remove unnecessary space at the top */     
-   /* Remove any fixed headers */
-    .stApp > header {
-        position: static !important;
-    }           
-    
-    /* Header styling */
-    .header {
-        background: linear-gradient(135deg, #654321 0%, #8B4513 100%);
-        color: white;
-        padding: 2rem 1rem;
-        margin-bottom: 2rem;
-        border-radius: 0 0 15px 15px;
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-    }
-    
-    /* Metric card styling */
-    .metric-card {
-        background: #ffffff;  /* Pure white cards for contrast */
-        border-radius: 10px;
-        padding: 1.5rem;
-        box-shadow: 0 2px 6px rgba(0, 0, 0, 0.15);
-        height: 100%;
-        transition: all 0.3s ease;
-        border: 1px solid #CFB53B;
-    }
-    
-    .metric-card:hover {
-        transform: translateY(-3px);
-        background: #FFF8E1;  /* Very light blue tint on hover */
-        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2);
-        border-color: #8B4513;
-    }
-    
-    /* Metric value styling */
-    .metric-value {
-        color: #8B4513 !important;  /* Deep, vibrant blue */
-        font-weight: 700;
-        font-size: 1.8rem;
-        text-shadow: 0 1px 2px rgba(0, 82, 204, 0.1);
-    }
-    
-    /* Metric label styling */
-    .metric-label {
-        color: #654321;  /* Navy blue-gray */
-        font-weight: 600;
-        letter-spacing: 0.5px;
-    }
-    
-    
-    /* Metric delta styling */
-    .metric-delta {
-        color: #A67C52;  /* Medium blue-gray */
-        font-size: 0.9rem;
-        font-weight: 500;
-    }
-    
-    /* Tab styling */
-    /* Main tab container */
-    .stTabs [data-baseweb="tab-list"] {
-        display: flex;
-        justify-content: center;
-        gap: 6px;
-        padding: 8px;
-        margin: 0 auto;
-        width: 95% !important;
-    }
-    
-    /* Individual tabs */
-    .stTabs [data-baseweb="tab"] {
-        flex: 1;  /* Equal width distribution */
-        min-width: 0;  /* Allows flex to work */
-        height: 60px;  /* Fixed height or use aspect ratio */
-        padding: 0 12px;
-        margin: 0;
-        font-weight: 600;
-        font-size: 1rem;
-        color: #654321;
-        background: #FFF8E1;
-        border: 2px solid #CFB53B;
-        border-radius: 12px;
-        transition: all 0.3s ease;
-        display: flex;
-        align-items: center;
-        justify-content: center;
-        text-align: center;
-    }
-    
-    /* Hover state */
-    .stTabs [data-baseweb="tab"]:hover {
-        background: #FFE8A1;
-        transform: translateY(-2px);
-    }
-    
-    
-    /* Active tab */
-    .stTabs [aria-selected="true"] {
-        background: #654321;
-        color: #FFD700 !important;
-        border-color: #8B4513;
-        font-size: 1.05rem;
-    }
-    
-    /* Icon sizing */
-    .stTabs [data-baseweb="tab"] svg {
-        width: 24px !important;
-        height: 24px !important;
-        margin-right: 8px !important;
-    }
-    
-    /* Button styling */
-    .stButton>button {
-        background-color: #654321;
-        color: #FFD700 !important;
-        border-radius: 8px;
-        padding: 0.5rem 1rem;
-        transition: all 0.3s ease;
-    }
-    
-    .stButton>button:hover {
-        background-color: #8B4513;
-        color: white;
-    }
-    
-    /* Dataframe styling */
-    .table-container {
-            display: flex;
-            justify-content: center;
-            margin-top: 30px;
-    }
-    .table-inner {
-            width: 50%;
-    }
-
-
-    @media only screen and (max-width: 768px) {
-        .table-inner {
-            width: 90%; /* For mobile */
-        }
-    }
-            
-    .stDataFrame {
-        border-radius: 10px;
-        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
-        background-color:white !important;
-        border: #CFB53B !important;
-    }
-    
-
-    
-    /* Section headers */
-    .st-emotion-cache-16txtl3 {
-        padding-top: 1rem;
-    }
-    
-    /* Custom hr style */
-    .custom-divider {
-        border: 0;
-        height: 1px;
-        background: linear-gradient(90deg, transparent, #dee2e6, transparent);
-        margin: 2rem 0;
-    }
-            
-
-    /* Consistent chart styling */
-    .stPlotlyChart {
-        border-radius: 10px;
-        background: white;
-        padding: 15px;
-        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
-        margin-bottom: 25px;
-    }
-    
-
-    
-    /* Match number inputs */
-    # .stNumberInput > div {
-    #     padding: 0.25rem 0.5rem !important;
-    # }
-
-    #/* Better select widget alignment */
-    # .stSelectbox > div {
-    #     margin-bottom: -15px;
-    # }
-            
-    
-    .custom-uploader > label div[data-testid="stFileUploadDropzone"] {
-        border: 2px solid #4CAF50;
-        background-color: #4CAF50;
-        color: white;
-        padding: 0.6em 1em;
-        border-radius: 0.5em;
-        text-align: center;
-        cursor: pointer;
-    }
-    .custom-uploader > label div[data-testid="stFileUploadDropzone"]:hover {
-        background-color: #45a049;
-    }
-
-    
-
-            
-    
-    /* Color scale adjustments */
-    .plotly .colorbar {
-        padding: 10px !important;
-            color: #654321 !important;
-    }   
-
-    </style>
-""", unsafe_allow_html=True)
-
-# ---------------------- App Header ----------------------
-st.markdown("""
-    <div class="header">
-        <h1 style='text-align: center; margin-bottom: 0.5rem;'>🦅 Eagle Blend Optimizer</h1>
-        <h4 style='text-align: center; font-weight: 400; margin-top: 0;'>
-            AI-Powered Fuel Blend Property Prediction & Optimization
-        </h4>
-    </div>
-""", unsafe_allow_html=True)
-#------ universal variables
- 
-
-# ---------------------- Tabs ----------------------
-tabs = st.tabs([
-    "📊 Dashboard",
-    "🎛️ Blend Designer",
-    "📤 Nothing For Now",
-    "⚙️ Optimization Engine",
-    "📚 Fuel Registry",
-    "🧠 Model Insights"
-])
-
-# ---------------------- Dashboard Tab ----------------------
-
-with tabs[0]:
-    st.subheader("Performance Metrics")
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        st.markdown("""
-            <div class="metric-card">
-                <div class="metric-label">Model Accuracy</div>
-                <div class="metric-value">94.7%</div>
-                <div class="metric-delta">R² Score</div>
-            </div>
-        """, unsafe_allow_html=True)
-    
-    with col2:
-        st.markdown("""
-            <div class="metric-card">
-                <div class="metric-label">Predictions Made</div>
-                <div class="metric-value">12,847</div>
-                <div class="metric-delta">Today</div>
-            </div>
-        """, unsafe_allow_html=True)
-    
-    with col3:
-        st.markdown("""
-            <div class="metric-card">
-                <div class="metric-label">Optimizations</div>
-                <div class="metric-value">156</div>
-                <div class="metric-delta">This Week</div>
-            </div>
-        """, unsafe_allow_html=True)
-    
-    with col4:
-        st.markdown("""
-            <div class="metric-card">
-                <div class="metric-label">Cost Savings</div>
-                <div class="metric-value">$2.4M</div>
-                <div class="metric-delta">Estimated Annual</div>
-            </div>
-        """, unsafe_allow_html=True)
-
-
-
-    st.markdown('<hr class="custom-divider">', unsafe_allow_html=True)
-    
-
-
-    st.subheader("Current Blend Properties")
-    blend_props = {
-        "Property 1": 0.847,
-        "Property 2": 0.623,
-        "Property 3": 0.734,
-        "Property 4": 0.912,
-        "Property 5": 0.456,
-        "Property 6": -1.234,
-    }
-    
-    # Enhanced dataframe display
-    df = pd.DataFrame(blend_props.items(), columns=["Property", "Value"])
-    # st.dataframe(
-    #     df.style
-    #     .background_gradient(cmap="YlOrBr", subset=["Value"])
-    #     .format({"Value": "{:.3f}"}),
-    #     use_container_width=True
-    # )
-
-    st.markdown('<div class="table-container"><div class="table-inner">', unsafe_allow_html=True)
-    st.dataframe(df, use_container_width=True)
-    st.markdown('</div></div>', unsafe_allow_html=True)
-
-
-
-
-with tabs[1]:
-    col_header = st.columns([0.8, 0.2])
-    with col_header[0]:
-        st.subheader("🎛️ Blend Designer")
-    with col_header[1]:
-        batch_blend = st.checkbox("Batch Blend Mode", value=False,
-                                help="Switch between manual input and predefined fuel selection",
-                                key="batch_blend_mode")
-
-    # Initialize session state
-    if 'show_visualization' not in st.session_state:
-        st.session_state.show_visualization = False
-    if 'blended_value' not in st.session_state:
-        st.session_state.blended_value = None
-    if 'selected_property' not in st.session_state:
-        st.session_state.selected_property = "Property1"
-
-    # Batch mode file upload
-    if batch_blend:
-        st.subheader("📤 Batch Processing")
-        uploaded_file = st.file_uploader("Upload CSV File", type=["csv"], key="Batch_upload")
-        weights = [0.1, 0.2, 0.25, 0.15, 0.3]  # Default weights for batch mode
-        
-        if not uploaded_file:
-            st.warning("Please upload a CSV file for batch processing")
-            data_input = None
-        else:
-            try:
-                data_input = pd.read_csv(uploaded_file)
-                st.success("File uploaded successfully")
-                st.dataframe(data_input.head())
-            except Exception as e:
-                st.error(f"Error reading file: {str(e)}")
-                data_input = None
-    else:
-        # Regular mode
-        data_input = None
-        weights, props = [], []
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            st.markdown("##### ⚖️ Component Weights")
-            for i in range(5):
-                weight = st.number_input(
-                    f"Weight for Component {i+1}", 
-                    min_value=0.0, 
-                    max_value=1.0, 
-                    value=0.2, 
-                    step=0.01, 
-                    key=f"w_{i}"
-                )
-                weights.append(weight)
-
-        with col2:
-            st.markdown("##### Fuel Selection")
-            for i in range(5):
-                fuel = st.selectbox(
-                    f"Component {i+1} Fuel Type",
-                    options=list(st.session_state.FUEL_PROPERTIES.keys()),
-                    key=f"fuel_{i}"
-                )
-                props.append(st.session_state.FUEL_PROPERTIES[fuel])
-
-    if st.button("⚙️ Predict Blended Property", key="predict_btn"):
-        if batch_blend:
-            if data_input is None:
-                st.error("⚠️ Please upload a valid CSV file first!")
-                st.session_state.show_visualization = False
-            else:
-                st.session_state.show_visualization = True
-        else:
-            if abs(sum(weights) - 1.0) > 0.01:
-                st.warning("⚠️ The total of weights must be **1.0**.")
-                st.session_state.show_visualization = False
-            else:
-                st.session_state.show_visualization = True
-                
-        if st.session_state.show_visualization:
-            # Show calculation details
-            st.subheader("Blend Components Data")
-
-            if not batch_blend:
-                weights_data = {f"Component{i+1}_fraction": weights[i] for i in range(len(weights))}
-                props_data = {f"Component{i+1}_{j}": props[i][j] for j in props[i].keys() for i in range(len(props))}
-                combined = {**weights_data, **props_data}
-                data_input = pd.DataFrame([combined])
-
-            st.write("Properties:", data_input)
-
-    # Show visualization only if prediction was made
-    if st.session_state.show_visualization:
-        if not batch_blend:
-            st.markdown('<hr class="custom-divider">', unsafe_allow_html=True)
-            st.subheader("Blend Visualization")
-            
-            components = [f"Component {i+1}" for i in range(5)]
-            
-            # 1. Weight Distribution Pie Chart
-            col1, col2 = st.columns(2)
-            with col1:
-                fig1 = px.pie(
-                    names=components,
-                    values=weights,
-                    title="Weight Distribution",
-                    color_discrete_sequence=['#8B4513', '#CFB53B', '#654321'],
-                    hole=0.4
-                )
-                fig1.update_layout(
-                    margin=dict(t=50, b=10),
-                    showlegend=False
-                )
-                fig1.update_traces(
-                    textposition='inside',
-                    textinfo='percent+label',
-                    marker=dict(line=dict(color='#ffffff', width=1))
-                )
-                st.plotly_chart(fig1, use_container_width=True)
-            
-            # 2. Property Comparison Bar Chart
-            with col2:
-                # Property selection for fuel mode
-                viz_property = st.selectbox(
-                    "Select Property to View",
-                    [f"Property{i+1}" for i in range(10)],
-                    key="viz_property"
-                )
-                bar_values = [p[viz_property] for p in props]
-                blended_value = 123 #Modify
-
-                fig2 = px.bar(
-                    x=components,
-                    y=bar_values,
-                    title=f"{viz_property} Values",
-                    color=bar_values,
-                    color_continuous_scale='YlOrBr'
-                )
-                fig2.update_layout(
-                    yaxis_title=viz_property,
-                    xaxis_title="Component",
-                    margin=dict(t=50, b=10),
-                    coloraxis_showscale=False
-                )
-                
-                fig2.add_hline(
-                    y=blended_value,
-                    line_dash="dot",
-                    line_color="#ff6600",
-                    annotation_text="Blended Value",
-                    annotation_position="top right"
-                )
-                st.plotly_chart(fig2, use_container_width=True)
-                
-                # Display the calculated value prominently
-                st.markdown(f"""
-                    <div style="
-                        background-color: #FAF3E6;
-                        border-left: 4px solid #8B4513;
-                        border-radius: 4px;
-                        padding: 12px;
-                        margin: 12px 0;
-                    ">
-                        <p style="margin: 0; color: #654321; 
-                        font-size: 2.2rem;
-                        font-weight: 800;
-                        color: #000;
-                        text-align:center;">
-                            Calculated <strong>{viz_property}</strong> = 
-                            <strong style="color: #000">{blended_value:.4f}</strong>
-                        </p>
-                    </div>
-                    """, unsafe_allow_html=True)
-        else:
-            # Batch mode visualization placeholder
-            st.markdown('<hr class="custom-divider">', unsafe_allow_html=True)
-            st.subheader("Batch Processing Results")
-            st.dataframe(data_input, use_container_width=True)
-            # st.info("Batch processing complete. Add custom visualizations here.")
-
-
-with tabs[2]:
-    st.subheader("📤 Nothing FOr NOw")
-    # uploaded_file = st.file_uploader("Upload CSV File", type=["csv"])
-
-    # if uploaded_file:
-    #     df = pd.read_csv(uploaded_file)
-    #     st.success("File uploaded successfully")
-    #     st.dataframe(df.head())
-
-    #     if st.button("⚙️ Run Batch Prediction"):
-    #         result_df = df.copy()
-    #         # result_df["Predicted_Property"] = df.apply(
-    #         #     lambda row: run_dummy_prediction(row.values[:5], row.values[5:10]), axis=1
-    #         # )
-    #         st.success("Batch prediction completed")
-    #         st.dataframe(result_df.head())
-    #         csv = result_df.to_csv(index=False).encode("utf-8")
-    #         st.download_button("Download Results", csv, "prediction_results.csv", "text/csv")
-
-
-
-with tabs[3]:
-    st.subheader("⚙️ Optimization Engine")
-    
-    # Pareto frontier demo
-    st.markdown("#### Cost vs Performance Trade-off")
-    np.random.seed(42)
-    optimization_data = pd.DataFrame({
-        'Cost ($/ton)': np.random.uniform(100, 300, 50),
-        'Performance Score': np.random.uniform(70, 95, 50)
-    })
-    
-    fig3 = px.scatter(
-        optimization_data,
-        x='Cost ($/ton)',
-        y='Performance Score',
-        title="Potential Blend Formulations",
-        color='Performance Score',
-        color_continuous_scale='YlOrBr'
-    )
-    
-    # Add dummy pareto frontier
-    x_pareto = np.linspace(100, 300, 10)
-    y_pareto = 95 - 0.1*(x_pareto-100)
-    fig3.add_trace(px.line(
-        x=x_pareto,
-        y=y_pareto,
-        color_discrete_sequence= ['#8B4513', '#CFB53B', '#654321']
-    ).data[0])
-    
-    fig3.update_layout(
-        showlegend=False,
-        annotations=[
-            dict(
-                x=200,
-                y=88,
-                text="Pareto Frontier",
-                showarrow=True,
-                arrowhead=1,
-                ax=-50,
-                ay=-30
-            )
-        ]
-    )
-    st.plotly_chart(fig3, use_container_width=True)
-    
-    # Blend optimization history
-    st.markdown("#### Optimization Progress")
-    iterations = np.arange(20)
-    performance = np.concatenate([np.linspace(70, 85, 10), np.linspace(85, 89, 10)])
-    
-    fig4 = px.line(
-        x=iterations,
-        y=performance,
-        title="Best Performance by Iteration",
-        markers=True
-    )
-    fig4.update_traces(
-        line_color='#1d3b58',
-        marker_color='#2c5282',
-        line_width=2.5
-    )
-    fig4.update_layout(
-        yaxis_title="Performance Score",
-        xaxis_title="Iteration"
-    )
-    st.plotly_chart(fig4, use_container_width=True)
-
-
-
-with tabs[4]:
-    st.subheader("📚 Fuel Registry")  # Changed to book emoji for registry
-    
-    # Button to add new fuel
-    st.markdown("#### ➕ Add a New Fuel Type")
-    with st.expander("Click to Add New Fuel", expanded=False):
-        with st.form("new_fuel_form", clear_on_submit=False):
-            fuel_name = st.text_input("Fuel Name", placeholder="e.g. Bioethanol")
-            
-            cols = st.columns(5)
-            properties = {}
-            for i in range(10):
-                with cols[i % 5]:
-                    prop_val = st.number_input(
-                        f"Property {i+1}", 
-                        min_value=0.0, 
-                        step=0.1, 
-                        key=f"prop_{i}",
-                        format="%.2f"
-                    )
-                    properties[f"Property{i+1}"] = round(prop_val, 2)
-
-            col1, col2 = st.columns(2)
-            with col1:
-                submitted = st.form_submit_button("💾 Save Fuel", use_container_width=True)
-            with col2:
-                cancelled = st.form_submit_button("❌ Cancel", use_container_width=True)
-            
-            if submitted:
-                if not fuel_name.strip():
-                    st.warning("Fuel name cannot be empty.")
-                elif fuel_name in st.session_state.FUEL_PROPERTIES:
-                    st.error(f"{fuel_name} already exists in registry.")
-                else:
-                    # Update both session state and CSV
-                    st.session_state.FUEL_PROPERTIES[fuel_name] = properties
-                    save_fuel_data()
-                    st.success(f"{fuel_name} successfully added!")
-                    st.rerun()  # Refresh to show new fuel
-            
-            if cancelled:
-                st.rerun()
-
-    with st.expander("Batch Add New Fuel", expanded=False):
-        uploaded_file = st.file_uploader(
-            "📤 Upload Fuel Batch (CSV)",
-            type=['csv'],
-            accept_multiple_files=False,
-            key="fuel_uploader",
-            help="Upload a CSV file with the same format as the exported registry"
-        )
-        if uploaded_file is not None:
-            try:
-                new_fuels = pd.read_csv(uploaded_file, index_col=0).to_dict('index')
-                
-                # Check for duplicates
-                duplicates = [name for name in new_fuels if name in st.session_state.FUEL_PROPERTIES]
-                
-                if duplicates:
-                    st.warning(f"These fuels already exist and won't be updated: {', '.join(duplicates)}")
-                    # Only add new fuels
-                    new_fuels = {name: props for name, props in new_fuels.items() 
-                                if name not in st.session_state.FUEL_PROPERTIES}
-                
-                if new_fuels:
-                    st.session_state.FUEL_PROPERTIES.update(new_fuels)
-                    save_fuel_data()
-                    st.success(f"Added {len(new_fuels)} new fuel(s) to registry!")
-                    st.rerun()
-                else:
-                    st.info("No new fuels to add from the uploaded file.")
-                    
-            except Exception as e:
-                st.error(f"Error processing file: {str(e)}")
-                st.error("Please ensure the file matches the expected format")
-
-    # Display current fuel properties
-    st.markdown("#### 🔍 Current Fuel Properties")
-    st.dataframe(
-        pd.DataFrame(st.session_state.FUEL_PROPERTIES).T.style
-        .background_gradient(cmap="YlOrBr", axis=None)
-        .format(precision=2),
-        use_container_width=True,
-        height=(len(st.session_state.FUEL_PROPERTIES) + 1) * 35 + 3,
-        hide_index=False
-    )
-    
-    # File operations section
-
-    
-    st.download_button(
-        label="📥 Download Registry (CSV)",
-        data=pd.DataFrame(st.session_state.FUEL_PROPERTIES).T.to_csv().encode('utf-8'),
-        file_name='fuel_properties.csv',
-        mime='text/csv',
-        # use_container_width=True
-    )
-    
-
-
-        
-
-
-
-
-
-
-
-with tabs[5]:
-    st.subheader("🧠 Model Insights")
-    
-    # Feature importance
-    st.markdown("#### Property Importance")
-    features = ['Property 1', 'Property 2', 'Property 3', 'Property 4', 'Property 5']
-    importance = np.array([0.35, 0.25, 0.2, 0.15, 0.05])
-    
-    fig5 = px.bar(
-        x=importance,
-        y=features,
-        orientation='h',
-        title="Feature Importance for Blend Prediction",
-        color=importance,
-        color_continuous_scale='YlOrBr'
-    )
-    fig5.update_layout(
-        xaxis_title="Importance Score",
-        yaxis_title="Property",
-        coloraxis_showscale=False
-    )
-    st.plotly_chart(fig5, use_container_width=True)
-    
-    # SHAP values demo
-    st.markdown("#### Property Impact Direction")
-    fig6 = px.scatter(
-        x=np.random.randn(100),
-        y=np.random.randn(100),
-        color=np.random.choice(features, 100),
-        title="SHAP Values (Simulated)",
-        labels={'x': 'Impact on Prediction', 'y': 'Property Value'}
-    )
-    fig6.update_traces(
-        marker=dict(size=10, opacity=0.7),
-        selector=dict(mode='markers')
-    )
-    fig6.add_vline(x=0, line_width=1, line_dash="dash")
-    st.plotly_chart(fig6, use_container_width=True)
-
-
-
-#     st.markdown("""
-#     <style>
-#     /* Consistent chart styling */
-#     .stPlotlyChart {
-#         border-radius: 10px;
-#         background: white;
-#         padding: 15px;
-#         box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
-#         margin-bottom: 25px;
-#     }
-    
-#     /* Better select widget alignment */
-#     .stSelectbox > div {
-#         margin-bottom: -15px;
-#     }
-    
-#     /* Color scale adjustments */
-#     .plotly .colorbar {
-#         padding: 10px !important;
-#     }
-#     </style>
-# """, unsafe_allow_html=True)
+        entries = os.listdir(path)
+    except PermissionError:
+        st.write("❌ Permission denied:", path)
+        print("❌ Permission denied:", path)
+        return
+
+    for entry in sorted(entries):
+        full_path = os.path.join(path, entry)
+        try:
+            perms = get_permissions(full_path)
+        except FileNotFoundError:
+            perms = "???"
+        display_text = f"{perms} {entry}"
+        st.write("  " * depth + f"- {display_text}")
+        print("  " * depth + f"- {display_text}")
+        if os.path.isdir(full_path):
+            walk_dir(full_path, depth + 1)
+
+walk_dir(root_path)