Update app.py

app.py

@@ -1,61 +1,84 @@
-# 1. MODIFY THE SIDEBAR INTERNET SIZE SELECTOR
-# Replace this section in the main() function:
+import streamlit as st
+import pandas as pd
+import numpy as np
+import networkit as nk
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import time
+import gc
+from io import StringIO
+import random
+from collections import defaultdict
 
-st.sidebar.markdown("### 🎯 **Step 2: Test Settings**")
-
-num_tests = st.sidebar.select_slider(
-    "How many tests to run?", 
-    options=[5, 10, 15, 20, 25, 30],
-    value=10,
-    help="More tests = more reliable results, but takes longer"
-)
-
-# CHANGE THIS PART:
-internet_size = st.sidebar.select_slider(
-    "Internet simulation size",
-    options=[
-        "Large (100K sites)", 
-        "Very Large (250K sites)", 
-        "Huge (500K sites)", 
-        "Massive (750K sites)",
-        "Ultra (1M sites)"
-    ],
-    value="Large (100K sites)",
-    help="Larger = more realistic but much slower. WARNING: 500K+ may take several minutes per test!"
+# Set page config
+st.set_page_config(
+    page_title="Website Link Impact Analyzer",
+    page_icon="🔗",
+    layout="wide",
+    initial_sidebar_state="expanded"
 )
 
-# Convert internet size to numbers
-size_map = {
-    "Large (100K sites)": 100000,
-    "Very Large (250K sites)": 250000, 
-    "Huge (500K sites)": 500000,
-    "Massive (750K sites)": 750000,
-    "Ultra (1M sites)": 1000000
-}
-www_nodes = size_map[internet_size]
-
-# 2. ADD PERFORMANCE WARNINGS
-# Add this right after the internet size selector:
-
-if www_nodes >= 500000:
-    st.sidebar.warning(f"""
-    ⚠️ **Performance Warning**: 
-    {internet_size} will be very slow! 
-    Expect 2-10 minutes per test.
-    Consider using fewer tests.
-    """)
-elif www_nodes >= 250000:
-    st.sidebar.info(f"""
-    ℹ️ **Note**: {internet_size} may take 
-    30-60 seconds per test.
-    """)
+# Global cache for WWW graph
+if 'www_graph_cache' not in st.session_state:
+    st.session_state.www_graph_cache = None
 
-# 3. OPTIMIZE THE WWW GRAPH CREATION FUNCTION
-# Replace the create_www_graph_networkit function with this optimized version:
+def load_graph_from_csv_networkit(file_content, file_name):
+    """
+    Load page links from CSV file using NetworKit.
+    """
+    try:
+        # Read CSV content
+        df = pd.read_csv(StringIO(file_content))
+        
+        # Check required columns with user-friendly names
+        required_cols = ['FROM', 'TO']
+        if not all(col in df.columns for col in required_cols):
+            st.error(f"""
+            ❌ **File Format Error**
+            
+            Your CSV file needs these column names:
+            - **FROM** (the page that has the link)
+            - **TO** (the page being linked to)
+            
+            Your file has: {', '.join(df.columns)}
+            """)
+            return None, None, None
+        
+        # Clean data
+        df = df.dropna(subset=['FROM', 'TO'])
+        df['FROM'] = df['FROM'].astype(str)
+        df['TO'] = df['TO'].astype(str)
+        
+        if len(df) == 0:
+            st.error(f"❌ No valid page links found in {file_name}")
+            return None, None, None
+        
+        # Get unique nodes and create mapping
+        all_nodes = list(set(df['FROM'].tolist() + df['TO'].tolist()))
+        node_to_idx = {node: i for i, node in enumerate(all_nodes)}
+        
+        # Create NetworKit graph
+        G = nk.Graph(n=len(all_nodes), weighted=False, directed=True)
+        
+        # Add edges
+        for _, row in df.iterrows():
+            source_idx = node_to_idx[row['FROM']]
+            target_idx = node_to_idx[row['TO']]
+            G.addEdge(source_idx, target_idx)
+        
+        return G, all_nodes, node_to_idx
+        
+    except Exception as e:
+        st.error(f"❌ **Error reading file**: {str(e)}")
+        st.info("💡 **Tip**: Make sure your file is a valid CSV with FROM and TO columns for page links")
+        return None, None, None
 
 def create_www_graph_networkit(n_nodes, m_edges, seed=42):
     """
-    Create a realistic internet simulation using NetworKit - optimized for large graphs.
+    Create a realistic internet simulation using NetworKit with Barabási-Albert model.
     """
     cache_key = (n_nodes, m_edges, seed)
     
@@ -66,98 +89,610 @@ def create_www_graph_networkit(n_nodes, m_edges, seed=42):
     # Set random seed for NetworKit
     nk.setSeed(seed, False)
     
-    # For very large graphs, use different approach
-    if n_nodes >= 500000:
-        # Use R-MAT generator for very large graphs (faster)
-        generator = nk.generators.RmatGenerator(
-            scale=int(np.log2(n_nodes)), 
-            edgeFactor=m_edges,
-            a=0.57, b=0.19, c=0.19, d=0.05
-        )
-        www_graph = generator.generate()
-        
-        # Convert to directed if needed
-        if not www_graph.isDirected():
-            directed_graph = nk.Graph(n=www_graph.numberOfNodes(), weighted=False, directed=True)
-            for u, v in www_graph.iterEdges():
-                directed_graph.addEdge(u, v)
-            www_graph = directed_graph
-    else:
-        # Use Barabási-Albert for smaller graphs
-        generator = nk.generators.BarabasiAlbertGenerator(k=m_edges, nMax=n_nodes, n0=m_edges)
-        www_graph = generator.generate()
-        
-        # Make it directed
-        if not www_graph.isDirected():
-            directed_graph = nk.Graph(n=www_graph.numberOfNodes(), weighted=False, directed=True)
-            for u, v in www_graph.iterEdges():
-                directed_graph.addEdge(u, v)
-                directed_graph.addEdge(v, u)  # Make bidirectional
-            www_graph = directed_graph
+    # Always use Barabási-Albert generator for realistic web structure
+    generator = nk.generators.BarabasiAlbertGenerator(k=m_edges, nMax=n_nodes, n0=m_edges)
+    www_graph = generator.generate()
+    
+    # Make it directed
+    if not www_graph.isDirected():
+        directed_graph = nk.Graph(n=www_graph.numberOfNodes(), weighted=False, directed=True)
+        for u, v in www_graph.iterEdges():
+            directed_graph.addEdge(u, v)
+            directed_graph.addEdge(v, u)  # Make bidirectional
+        www_graph = directed_graph
     
     # Cache the result
     st.session_state.www_graph_cache = (cache_key, www_graph)
     return www_graph
 
-# 4. OPTIMIZE THE PAGERANK CALCULATION
-# Replace the PageRank section in process_configuration_networkit with:
+def cleanup_memory():
+    """Clean up memory after large graph operations."""
+    gc.collect()  # Force garbage collection
 
-# Calculate PageRank using NetworKit with optimized settings for large graphs
-try:
-    if total_nodes >= 500000:
-        # Use more relaxed tolerance for very large graphs
-        pagerank_algo = nk.centrality.PageRank(merged_graph, damp=0.85, tol=1e-4)
-    else:
-        pagerank_algo = nk.centrality.PageRank(merged_graph, damp=0.85, tol=1e-6)
+def process_configuration_networkit(www_graph, kalicube_graph, kalicube_nodes, 
+                                   min_connections=5, max_connections=50):
+    """
+    Test how your page network performs in the real internet using NetworKit.
+    """
+    # Get WWW graph info
+    www_node_count = www_graph.numberOfNodes()
+    kalicube_node_count = len(kalicube_nodes)
     
-    pagerank_algo.run()
-    pagerank_values = pagerank_algo.scores()
+    # Create node mapping for kalicube nodes
+    kalicube_offset = www_node_count
+    kalicube_node_mapping = {}
     
-except Exception as e:
-    st.warning(f"PageRank calculation failed: {e}. Using degree centrality instead.")
-    # Fallback to degree centrality
-    degree_algo = nk.centrality.DegreeCentrality(merged_graph, normalized=True)
-    degree_algo.run()
-    pagerank_values = degree_algo.scores()
+    for i, node in enumerate(kalicube_nodes):
+        new_node_id = kalicube_offset + i
+        kalicube_node_mapping[node] = new_node_id
+    
+    # Create merged graph
+    total_nodes = www_node_count + kalicube_node_count
+    merged_graph = nk.Graph(n=total_nodes, weighted=False, directed=True)
+    
+    # Add WWW edges
+    for u, v in www_graph.iterEdges():
+        merged_graph.addEdge(u, v)
+    
+    # Add kalicube edges with new node IDs
+    kalicube_idx_to_node = {i: node for i, node in enumerate(kalicube_nodes)}
+    
+    for u, v in kalicube_graph.iterEdges():
+        source_node = kalicube_idx_to_node[u]
+        target_node = kalicube_idx_to_node[v]
+        new_source_id = kalicube_node_mapping[source_node]
+        new_target_id = kalicube_node_mapping[target_node]
+        merged_graph.addEdge(new_source_id, new_target_id)
+    
+    # Randomly connect kalicube pages to WWW
+    n_connections = min(min_connections, www_node_count, kalicube_node_count)
+    
+    www_sample = random.sample(range(www_node_count), n_connections)
+    kalicube_sample = random.sample(list(kalicube_node_mapping.values()), n_connections)
+    
+    for www_node, kalicube_node in zip(www_sample, kalicube_sample):
+        merged_graph.addEdge(www_node, kalicube_node)
+    
+    # Calculate PageRank using NetworKit with optimized settings for large graphs
+    try:
+        if total_nodes >= 500000:
+            # Use more relaxed tolerance for very large graphs
+            pagerank_algo = nk.centrality.PageRank(merged_graph, damp=0.85, tol=1e-4)
+        else:
+            pagerank_algo = nk.centrality.PageRank(merged_graph, damp=0.85, tol=1e-6)
+        
+        pagerank_algo.run()
+        pagerank_values = pagerank_algo.scores()
+        
+    except Exception as e:
+        st.warning(f"PageRank calculation failed: {e}. Using degree centrality instead.")
+        # Fallback to degree centrality
+        degree_algo = nk.centrality.DegreeCentrality(merged_graph, normalized=True)
+        degree_algo.run()
+        pagerank_values = degree_algo.scores()
+    
+    # Extract PageRank values for kalicube nodes
+    pagerank_dict = {}
+    for node, node_id in kalicube_node_mapping.items():
+        pagerank_dict[node] = pagerank_values[node_id] if node_id < len(pagerank_values) else 0.0
+    
+    return pagerank_dict
 
-# 5. ADD MEMORY MANAGEMENT
-# Add this function and call it after each simulation:
+def create_comparison_dataframe(pagerank_old_dict, pagerank_new_dict, simulation_id):
+    """
+    Compare before and after results.
+    """
+    # Find pages that appear in both tests
+    old_urls = set(pagerank_old_dict.keys())
+    new_urls = set(pagerank_new_dict.keys())
+    common_urls = old_urls & new_urls
+    
+    if not common_urls:
+        return pd.DataFrame()
+    
+    # Create comparison data
+    comparison_data = []
+    
+    # Sort pages by importance for ranking
+    old_sorted = sorted(pagerank_old_dict.items(), key=lambda x: x[1], reverse=True)
+    new_sorted = sorted(pagerank_new_dict.items(), key=lambda x: x[1], reverse=True)
+    
+    # Create ranking mappings
+    old_ranks = {url: rank + 1 for rank, (url, _) in enumerate(old_sorted)}
+    new_ranks = {url: rank + 1 for rank, (url, _) in enumerate(new_sorted)}
+    
+    for url in common_urls:
+        importance_before = pagerank_old_dict[url]
+        importance_after = pagerank_new_dict[url]
+        rank_before = old_ranks[url]
+        rank_after = new_ranks[url]
+        
+        importance_change = importance_after - importance_before
+        importance_change_pct = (importance_change / importance_before) * 100 if importance_before > 0 else 0
+        rank_change = rank_after - rank_before
+        rank_change_pct = (rank_change / rank_before) * 100 if rank_before > 0 else 0
+        
+        comparison_data.append({
+            'Page_URL': url,
+            'Importance_Before': importance_before,
+            'Importance_After': importance_after,
+            'Rank_Before': rank_before,
+            'Rank_After': rank_after,
+            'Importance_Change': importance_change,
+            'Importance_Change_%': importance_change_pct,
+            'Rank_Change': rank_change,
+            'Rank_Change_%': rank_change_pct,
+            'Test_Number': simulation_id
+        })
+    
+    return pd.DataFrame(comparison_data)
 
-def cleanup_memory():
-    """Clean up memory after large graph operations."""
-    gc.collect()  # Force garbage collection
+def run_single_simulation(simulation_id, kalicube_graph_old, kalicube_graph_new,
+                         kalicube_nodes_old, kalicube_nodes_new,
+                         www_nodes, www_edges, min_conn, max_conn):
+    """
+    Run one test comparing before and after.
+    """
+    sim_seed = 42 + simulation_id
+    random.seed(sim_seed)
+    np.random.seed(sim_seed)
+    
+    # Create internet simulation
+    www_graph = create_www_graph_networkit(www_nodes, www_edges, sim_seed)
+    
+    # Test original setup
+    importance_old_dict = process_configuration_networkit(
+        www_graph, kalicube_graph_old, kalicube_nodes_old, min_conn, max_conn
+    )
+    
+    # Test new setup
+    importance_new_dict = process_configuration_networkit(
+        www_graph, kalicube_graph_new, kalicube_nodes_new, min_conn, max_conn
+    )
+    
+    # Compare results
+    comparison_df = create_comparison_dataframe(
+        importance_old_dict, importance_new_dict, simulation_id
+    )
+    
+    # Clean up memory after large operations
+    cleanup_memory()
     
-# Call this in run_single_simulation after processing:
-# ... existing simulation code ...
-cleanup_memory()  # Add this line before returning results
+    if comparison_df.empty:
+        return None, None
+    
+    # Calculate summary
+    total_before = comparison_df['Importance_Before'].sum()
+    total_after = comparison_df['Importance_After'].sum()
+    total_change = total_after - total_before
+    change_pct = (total_change / total_before) * 100 if total_before > 0 else 0
+    
+    rank_changes = comparison_df['Rank_Change'].values
+    rank_improvements = np.sum(rank_changes < 0)  # Lower rank number = better
+    rank_drops = np.sum(rank_changes > 0)
+    rank_unchanged = np.sum(rank_changes == 0)
+    avg_rank_change = np.mean(rank_changes)
+    
+    result = {
+        'Test_Number': simulation_id + 1,
+        'Total_Before': total_before,
+        'Total_After': total_after,
+        'Total_Change': total_change,
+        'Change_Percent': change_pct,
+        'Pages_Improved': rank_improvements,
+        'Pages_Dropped': rank_drops,
+        'Pages_Unchanged': rank_unchanged,
+        'Avg_Rank_Change': avg_rank_change
+    }
+    
+    return result, comparison_df
 
-# 6. MODIFY THE PROGRESS MESSAGES FOR LARGE SIMULATIONS
-# Update the encouraging_messages list in main():
+def get_traffic_light_status(results_df, confidence_threshold=0.7):
+    """
+    Simple decision guidance based on test results.
+    """
+    total_tests = len(results_df)
+    positive_outcomes = (results_df['Total_Change'] > 0).sum()
+    negative_outcomes = (results_df['Total_Change'] < 0).sum()
+    
+    positive_ratio = positive_outcomes / total_tests
+    negative_ratio = negative_outcomes / total_tests
+    mean_impact = results_df['Change_Percent'].mean()
+    
+    # Simple traffic light logic
+    if positive_ratio >= confidence_threshold and mean_impact > 1.0:
+        return "🟢", "✅ GO AHEAD - Your changes look great!", "go", "Most tests show good results. Your changes should help your page rankings."
+    elif positive_ratio >= confidence_threshold and mean_impact > 0:
+        return "🟡", "⚠️ PROCEED CAREFULLY - Small improvements expected", "caution", "Tests show some improvement, but it's modest. Consider if the effort is worth it."
+    elif negative_ratio >= confidence_threshold and mean_impact < -1.0:
+        return "🔴", "❌ STOP - Your changes may hurt your page rankings", "stop", "Most tests show negative results. Consider revising your changes before implementing."
+    elif negative_ratio >= confidence_threshold and mean_impact < 0:
+        return "🟡", "⚠️ PROCEED CAREFULLY - Some negative impact expected", "caution", "Tests show some negative impact. Monitor closely if you proceed."
+    else:
+        return "🟡", "🤷 MIXED RESULTS - Hard to predict", "caution", "Test results are mixed. Consider running more tests or getting expert advice."
 
-if www_nodes >= 500000:
-    encouraging_messages = [
-        f"🔬 Creating massive internet simulation ({www_nodes:,} sites)... This will take a while!",
-        "🌐 Building ultra-large network topology...",
-        "📊 Computing importance scores for massive network...",
-        "🎯 Running test with millions of connections...",
-        "📈 Almost there! Processing final calculations..."
-    ]
-else:
-    encouraging_messages = [
-        f"🔬 Setting up large internet simulation ({www_nodes:,} sites)...",
-        "🌐 Connecting your pages to the web...",
-        "📊 Calculating page importance scores...",
-        "🎯 Running tests with different scenarios...",
-        "📈 Almost done! Analyzing results..."
-    ]
+def create_simple_visualizations(results_df, all_comparisons_df, confidence_threshold=0.7):
+    """
+    Create easy-to-understand visualizations.
+    """
+    # Traffic Light Assessment
+    traffic_emoji, traffic_status, traffic_level, explanation = get_traffic_light_status(results_df, confidence_threshold)
+    
+    st.markdown("## 🚦 **Should You Make These Changes?**")
+    
+    # Big, clear recommendation
+    if traffic_level == "go":
+        st.success(f"# {traffic_emoji}")
+        st.success(f"## {traffic_status}")
+        st.info(f"**Why:** {explanation}")
+    elif traffic_level == "stop":
+        st.error(f"# {traffic_emoji}")
+        st.error(f"## {traffic_status}")
+        st.warning(f"**Why:** {explanation}")
+    else:
+        st.warning(f"# {traffic_emoji}")
+        st.warning(f"## {traffic_status}")
+        st.info(f"**Why:** {explanation}")
+    
+    # Simple metrics in plain English
+    st.markdown("### 📊 **Test Results Summary**")
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        positive_tests = (results_df['Total_Change'] > 0).sum()
+        total_tests = len(results_df)
+        st.metric("Tests Showing Improvement", f"{positive_tests} out of {total_tests}", 
+                 delta=f"{positive_tests/total_tests:.0%} positive")
+    
+    with col2:
+        mean_change = results_df['Change_Percent'].mean()
+        st.metric("Average Impact on Rankings", f"{mean_change:.1f}%", 
+                 delta="Higher is better")
+    
+    with col3:
+        improved_sites = results_df['Pages_Improved'].mean()
+        st.metric("Pages That Improved (avg)", f"{improved_sites:.0f}", 
+                 delta="per test")
 
-# 7. ADD OPTION FOR REDUCED TESTS ON LARGE SIMULATIONS
-# Add this logic before the test running section:
+def main():
+    st.title("🔗 Page Link Impact Analyzer (Powered by NetworKit)")
+    st.markdown("**Find out if your page link changes will help or hurt your search rankings**")
+    
+    # Simple intro
+    st.info("""
+    👋 **Welcome!** This tool helps you test page link changes before you make them.
+    
+    **What it does:** Simulates how your link changes might affect your page rankings in search engines.
+    
+    **What you need:** Two CSV files - one with your current page links, one with your planned changes.
+    
+    ⚡ **Now powered by NetworKit** - A high-performance network analysis toolkit for faster and more efficient analysis of large-scale networks!
+    """)
+    
+    # Sidebar - simplified
+    st.sidebar.header("⚙️ Settings")
+    
+    # File uploads with better guidance
+    st.sidebar.markdown("### 📁 **Step 1: Upload Your Files**")
+    st.sidebar.markdown("*Need help with file format? Check the 'File Format Help' section below.*")
+    
+    old_file = st.sidebar.file_uploader("Current Page Links (CSV)", type=['csv'], key="old",
+                                       help="Upload a CSV file with your current page links")
+    new_file = st.sidebar.file_uploader("Planned Page Links (CSV)", type=['csv'], key="new",
+                                       help="Upload a CSV file with your planned page links")
+    
+    # Simplified settings
+    st.sidebar.markdown("### 🎯 **Step 2: Test Settings**")
+    
+    num_tests = st.sidebar.select_slider(
+        "How many tests to run?", 
+        options=[5, 10, 15, 20, 25, 30],
+        value=10,
+        help="More tests = more reliable results, but takes longer"
+    )
+    
+    internet_size = st.sidebar.select_slider(
+        "Internet simulation size",
+        options=[
+            "Large (100K sites)", 
+            "Very Large (250K sites)", 
+            "Huge (500K sites)", 
+            "Massive (750K sites)",
+            "Ultra (1M sites)"
+        ],
+        value="Large (100K sites)",
+        help="Larger = more realistic but much slower. WARNING: 500K+ may take several minutes per test!"
+    )
+    
+    # Convert internet size to numbers
+    size_map = {
+        "Large (100K sites)": 100000,
+        "Very Large (250K sites)": 250000, 
+        "Huge (500K sites)": 500000,
+        "Massive (750K sites)": 750000,
+        "Ultra (1M sites)": 1000000
+    }
+    www_nodes = size_map[internet_size]
+    
+    # Performance warnings
+    if www_nodes >= 500000:
+        st.sidebar.warning(f"""
+        ⚠️ **Performance Warning**: 
+        {internet_size} will be very slow! 
+        Expect 2-10 minutes per test.
+        Consider using fewer tests.
+        """)
+    elif www_nodes >= 250000:
+        st.sidebar.info(f"""
+        ℹ️ **Note**: {internet_size} may take 
+        30-60 seconds per test.
+        """)
+    
+    # Advanced settings (hidden by default)
+    with st.sidebar.expander("🔧 Advanced Settings (Optional)"):
+        confidence_level = st.slider("Confidence level for recommendations", 60, 90, 70, 5,
+                                    help="Higher = stricter requirements for green/red lights")
+        show_details = st.checkbox("Show detailed results", False)
+        auto_run = st.checkbox("Auto-run when files uploaded", False)
+    
+    confidence_threshold = confidence_level / 100
+    
+    # Main content
+    if old_file is not None and new_file is not None:
+        # Load files
+        old_content = old_file.getvalue().decode('utf-8')
+        new_content = new_file.getvalue().decode('utf-8')
+        
+        # Show file status
+        col1, col2 = st.columns(2)
+        with col1:
+            st.success(f"✅ **Current Page Links**: {old_file.name}")
+        with col2:
+            st.success(f"✅ **Planned Page Links**: {new_file.name}")
+        
+        # Load and validate files
+        with st.spinner("Reading your files..."):
+            kalicube_graph_old, kalicube_nodes_old, kalicube_url_mapping_old = \
+                load_graph_from_csv_networkit(old_content, old_file.name)
+            
+            kalicube_graph_new, kalicube_nodes_new, kalicube_url_mapping_new = \
+                load_graph_from_csv_networkit(new_content, new_file.name)
+        
+        if kalicube_graph_old is not None and kalicube_graph_new is not None:
+            # Show what we found
+            st.markdown("### 📈 **What We Found in Your Files**")
+            info_col1, info_col2 = st.columns(2)
+            
+            with info_col1:
+                st.info(f"""
+                **Current Setup:**
+                - {len(kalicube_nodes_old)} pages
+                - {kalicube_graph_old.numberOfEdges()} links between them
+                """)
+            
+            with info_col2:
+                st.info(f"""
+                **Planned Setup:**
+                - {len(kalicube_nodes_new)} pages  
+                - {kalicube_graph_new.numberOfEdges()} links between them
+                """)
+            
+            # Big, obvious run button
+            st.markdown("### 🚀 **Step 3: Run the Test**")
+            
+            run_button = st.button("🔬 Test My Changes", type="primary", use_container_width=True)
+            
+            if run_button or auto_run:
+                # Automatically reduce tests for very large simulations
+                if www_nodes >= 750000 and num_tests > 10:
+                    st.warning(f"⚠️ Automatically reducing tests from {num_tests} to 10 for {internet_size} to prevent timeout.")
+                    num_tests = min(num_tests, 10)
+                elif www_nodes >= 500000 and num_tests > 15:
+                    st.warning(f"⚠️ Automatically reducing tests from {num_tests} to 15 for {internet_size}.")
+                    num_tests = min(num_tests, 15)
+                
+                # Progress with encouraging messages
+                progress_bar = st.progress(0)
+                status_text = st.empty()
+                
+                # Dynamic messages based on simulation size
+                if www_nodes >= 500000:
+                    encouraging_messages = [
+                        f"🔬 Creating massive Barabási-Albert simulation ({www_nodes:,} sites)... This will take a while!",
+                        "🌐 Building scale-free network topology with preferential attachment...",
+                        "���� Computing PageRank scores for massive scale-free network...",
+                        "🎯 Running test with millions of preferential connections...",
+                        "📈 Almost there! Processing final calculations..."
+                    ]
+                else:
+                    encouraging_messages = [
+                        f"🔬 Setting up Barabási-Albert simulation ({www_nodes:,} sites)...",
+                        "🌐 Creating scale-free network with preferential attachment...",
+                        "📊 Calculating page importance scores...",
+                        "🎯 Running tests with different scenarios...",
+                        "📈 Almost done! Analyzing results..."
+                    ]
+                
+                all_results = []
+                all_comparisons = []
+                
+                start_time = time.time()
+                
+                # Run tests with encouragement
+                for i in range(num_tests):
+                    msg_idx = min(i // max(1, num_tests // len(encouraging_messages)), len(encouraging_messages) - 1)
+                    status_text.text(f"{encouraging_messages[msg_idx]} (Test {i+1}/{num_tests})")
+                    progress_bar.progress((i + 1) / num_tests)
+                    
+                    result, comparison_df = run_single_simulation(
+                        i, kalicube_graph_old, kalicube_graph_new,
+                        kalicube_nodes_old, kalicube_nodes_new,
+                        www_nodes, 2, 5, 25  # simplified parameters
+                    )
+                    
+                    if result is not None:
+                        all_results.append(result)
+                        all_comparisons.append(comparison_df)
+                
+                end_time = time.time()
+                
+                # Clear progress
+                progress_bar.empty()
+                status_text.empty()
+                
+                if all_results:
+                    results_df = pd.DataFrame(all_results)
+                    all_comparisons_df = pd.concat(all_comparisons, ignore_index=True) if all_comparisons else pd.DataFrame()
+                    
+                    # Show results
+                    st.success(f"🎉 **Test Complete!** Ran {len(all_results)} tests in {end_time - start_time:.0f} seconds")
+                    
+                    # Create simple visualizations
+                    create_simple_visualizations(results_df, all_comparisons_df, confidence_threshold)
+                    
+                    # Download section
+                    st.markdown("### 💾 **Save Your Results**")
+                    col1, col2 = st.columns(2)
+                    
+                    with col1:
+                        csv_summary = results_df.to_csv(index=False)
+                        st.download_button(
+                            label="📊 Download Summary Report",
+                            data=csv_summary,
+                            file_name=f"website_impact_summary_{int(time.time())}.csv",
+                            mime="text/csv"
+                        )
+                    
+                    with col2:
+                        if not all_comparisons_df.empty:
+                            csv_detailed = all_comparisons_df.to_csv(index=False)
+                            st.download_button(
+                                label="📋 Download Detailed Results",
+                                data=csv_detailed,
+                                file_name=f"website_impact_detailed_{int(time.time())}.csv",
+                                mime="text/csv"
+                            )
+                    
+                    # Show detailed results if requested
+                    if show_details and not all_comparisons_df.empty:
+                        st.markdown("### 🔍 **Detailed Results** (For the curious)")
+                        
+                        # Simple filter
+                        st.markdown("**Filter results:**")
+                        filter_col1, filter_col2 = st.columns(2)
+                        with filter_col1:
+                            min_change = st.number_input("Show changes above (%)", 
+                                                       value=float(all_comparisons_df['Importance_Change_%'].min()),
+                                                       step=0.1)
+                        
+                        # Apply filter and show
+                        filtered_df = all_comparisons_df[all_comparisons_df['Importance_Change_%'] >= min_change]
+                        
+                        # Rename columns for clarity
+                        display_df = filtered_df.copy()
+                        display_df = display_df.rename(columns={
+                            'Page_URL': 'Page URL',
+                            'Importance_Change_%': 'Impact (%)',
+                            'Rank_Change': 'Rank Change',
+                            'Test_Number': 'Test #'
+                        })
+                        
+                        st.dataframe(
+                            display_df[['Page URL', 'Impact (%)', 'Rank_Change', 'Test #']].sort_values('Impact (%)', ascending=False),
+                            use_container_width=True,
+                            height=300
+                        )
+                
+                else:
+                    st.error("❌ No test results generated. Please check your files and try again.")
+    
+    else:
+        # Help section when no files uploaded
+        st.markdown("---")
+        
+        # File format help
+        with st.expander("📋 **File Format Help** - How to prepare your CSV files"):
+            st.markdown("""
+            ### ✅ **Correct Format**
+            Your CSV files need exactly these column names:
+            - **FROM** = the page that has the link
+            - **TO** = the page being linked to
+            
+            ### 📝 **Example:**
+            ```
+            FROM,TO
+            mysite.com/about,mysite.com/contact
+            mysite.com/blog/post1,partner.com/resource
+            partner.com/page,mysite.com/services
+            ```
+            
+            ### 💡 **Tips:**
+            - Use any spreadsheet program (Excel, Google Sheets) to create these
+            - Save as CSV format
+            - Include full URLs or page paths
+            - Make sure page URLs are consistent (mysite.com/page vs mysite.com/page/ are different!)
+            - Each row represents one link from one page to another
+            """)
+        
+        with st.expander("🤔 **What This Tool Actually Does** - Explained Simply"):
+            st.markdown("""
+            ### 🌐 **The Big Picture**
+            When you change links between your pages, it affects how search engines see your site. But it's hard to predict the exact impact because the internet is huge and constantly changing.
+            
+            ### 🧪 **Our Solution: Virtual Testing**
+            1. **We simulate the internet** - Create a virtual version with hundreds of thousands or millions of pages
+            2. **We test your changes** - Run your current page links vs. your planned links
+            3. **We repeat many times** - Each test uses slightly different internet conditions
+            4. **We analyze the pattern** - Look at whether your changes usually help or hurt
+            
+            ### 🚦 **The Traffic Light System**
+            - **🟢 Green = Go ahead** - Most tests show your changes help
+            - **🟡 Yellow = Be careful** - Mixed results or small impact  
+            - **🔴 Red = Stop** - Most tests show your changes hurt
+            
+            ### 🎯 **Why This Works**
+            Instead of guessing, you get data-driven confidence about your page link changes!
+            
+            ### ⚡ **Powered by NetworKit**
+            This version uses NetworKit, a high-performance network analysis toolkit that's much faster than traditional tools for analyzing large networks. It can now handle simulations of 100K to 1M sites!
+            
+            ### 🔬 **Large-Scale Simulations**
+            - **100K sites**: ~10-30 seconds per test
+            - **250K sites**: ~30-60 seconds per test
+            - **500K sites**: ~1-3 minutes per test
+            - **750K sites**: ~2-5 minutes per test
+            - **1M sites**: ~3-10 minutes per test
+            """)
+        
+        with st.expander("❓ **Common Questions**"):
+            st.markdown("""
+            **Q: How accurate is this?**
+            A: The tool shows trends and probabilities, not exact predictions. It's like weather forecasting - very useful for planning!
+            
+            **Q: How long does it take?**
+            A: From 30 seconds to 10 minutes per test, depending on simulation size. NetworKit makes it much faster than before!
+            
+            **Q: What if I get yellow results?**
+            A: Yellow means proceed carefully. Consider running more tests, getting expert advice, or monitoring closely if you implement.
+            
+            **Q: Can I test multiple scenarios?**
+            A: Yes! Just upload different "planned changes" files to compare options.
+            
+            **Q: What file size limits?**
+            A: Works best with up to 50,000 page links. Larger files may be slow.
+            
+            **Q: What's the difference between pages and websites?**
+            A: Pages are specific URLs (like mysite.com/about), while websites are domains (like mysite.com). This tool analyzes individual page links.
+            
+            **Q: What's NetworKit?**
+            A: NetworKit is a high-performance network analysis toolkit with optimized C++ algorithms that makes calculations much faster and can handle larger datasets more efficiently.
+            
+            **Q: Which simulation size should I choose?**
+            A: Start with 100K for testing. Use 250K-500K for realistic results. Only use 750K+ if you have time and want maximum realism.
+            
+            **Q: Why does it take longer with bigger simulations?**
+            A: Larger simulations are more realistic but require more computation. The tool automatically adjusts algorithms for efficiency at different scales.
+            """)
 
-# Automatically reduce tests for very large simulations
-if www_nodes >= 750000 and num_tests > 10:
-    st.warning(f"⚠️ Automatically reducing tests from {num_tests} to 10 for {internet_size} to prevent timeout.")
-    num_tests = min(num_tests, 10)
-elif www_nodes >= 500000 and num_tests > 15:
-    st.warning(f"⚠️ Automatically reducing tests from {num_tests} to 15 for {internet_size}.")
-    num_tests = min(num_tests, 15)
+if __name__ == "__main__":
+    main()