Create app.py

app.py

@@ -0,0 +1,684 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import grape
+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
+
+# Set page config
+st.set_page_config(
+    page_title="Website Link Impact Analyzer",
+    page_icon="🔗",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Global cache for WWW graph
+if 'www_graph_cache' not in st.session_state:
+    st.session_state.www_graph_cache = None
+
+def load_graph_from_csv_grape(file_content, file_name):
+    """
+    Load page links from CSV file using Grape.
+    """
+    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 edge list with indices
+        edge_list = []
+        for _, row in df.iterrows():
+            source_idx = node_to_idx[row['FROM']]
+            target_idx = node_to_idx[row['TO']]
+            edge_list.append((source_idx, target_idx))
+        
+        # Create Grape graph
+        G = grape.Graph.from_edge_list(
+            edge_list=edge_list,
+            directed=True,
+            node_names=[str(i) for i in range(len(all_nodes))],
+            name=f"graph_{file_name}"
+        )
+        
+        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_grape(n_nodes, m_edges, seed=42):
+    """
+    Create a realistic internet simulation using Grape.
+    """
+    cache_key = (n_nodes, m_edges, seed)
+    
+    if (st.session_state.www_graph_cache is not None and 
+        st.session_state.www_graph_cache[0] == cache_key):
+        return st.session_state.www_graph_cache[1]
+    
+    # Set random seed
+    random.seed(seed)
+    np.random.seed(seed)
+    
+    # Create Barabási-Albert graph manually since Grape doesn't have this built-in
+    # Start with a complete graph of m_edges nodes
+    edges = []
+    for i in range(m_edges):
+        for j in range(i + 1, m_edges):
+            edges.append((i, j))
+            edges.append((j, i))  # Make it directed
+    
+    # Add remaining nodes with preferential attachment
+    degrees = [2 * m_edges] * m_edges  # Initial degrees
+    
+    for new_node in range(m_edges, n_nodes):
+        # Select m_edges nodes to connect to based on preferential attachment
+        total_degree = sum(degrees)
+        targets = set()
+        
+        while len(targets) < min(m_edges, new_node):
+            # Probability proportional to degree
+            rand_val = random.random() * total_degree
+            cumsum = 0
+            for i, degree in enumerate(degrees):
+                cumsum += degree
+                if cumsum >= rand_val and i not in targets:
+                    targets.add(i)
+                    break
+        
+        # Add edges
+        for target in targets:
+            edges.append((new_node, target))
+            edges.append((target, new_node))  # Bidirectional
+        
+        # Update degrees
+        degrees.append(2 * len(targets))
+        for target in targets:
+            degrees[target] += 2
+    
+    # Create Grape graph
+    www_graph = grape.Graph.from_edge_list(
+        edge_list=edges,
+        directed=True,
+        node_names=[str(i) for i in range(n_nodes)],
+        name="www_simulation"
+    )
+    
+    # Cache the result
+    st.session_state.www_graph_cache = (cache_key, www_graph)
+    return www_graph
+
+def process_configuration_grape(www_graph, kalicube_graph, kalicube_nodes, 
+                               min_connections=5, max_connections=50):
+    """
+    Test how your page network performs in the real internet using Grape.
+    """
+    # Get WWW graph info
+    www_node_count = www_graph.get_number_of_nodes()
+    kalicube_node_count = len(kalicube_nodes)
+    
+    # Create node mapping for kalicube nodes
+    kalicube_offset = www_node_count
+    kalicube_node_mapping = {}
+    
+    for i, node in enumerate(kalicube_nodes):
+        new_node_id = kalicube_offset + i
+        kalicube_node_mapping[node] = new_node_id
+    
+    # Get edges from both graphs
+    www_edges = www_graph.get_edge_list()
+    kalicube_edges = kalicube_graph.get_edge_list()
+    
+    # Convert kalicube edges to use new node IDs
+    kalicube_mapped_edges = []
+    kalicube_idx_to_node = {i: node for node, i in kalicube_graph.get_node_name_to_node_id_map().items()}
+    
+    for source_idx, target_idx in kalicube_edges:
+        source_node = kalicube_idx_to_node[source_idx]
+        target_node = kalicube_idx_to_node[target_idx]
+        new_source_id = kalicube_node_mapping[source_node]
+        new_target_id = kalicube_node_mapping[target_node]
+        kalicube_mapped_edges.append((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)
+    
+    connection_edges = []
+    for www_node, kalicube_node in zip(www_sample, kalicube_sample):
+        connection_edges.append((www_node, kalicube_node))
+    
+    # Combine all edges
+    all_edges = list(www_edges) + kalicube_mapped_edges + connection_edges
+    total_nodes = www_node_count + kalicube_node_count
+    
+    # Create merged graph
+    merged_graph = grape.Graph.from_edge_list(
+        edge_list=all_edges,
+        directed=True,
+        node_names=[str(i) for i in range(total_nodes)],
+        name="merged_simulation"
+    )
+    
+    # Calculate PageRank
+    try:
+        pagerank_values = merged_graph.pagerank(
+            damping_factor=0.85,
+            maximum_iterations=100,
+            tolerance=1e-6
+        )
+    except Exception as e:
+        st.warning(f"PageRank calculation failed: {e}. Using degree centrality instead.")
+        # Fallback to degree centrality
+        degrees = merged_graph.get_node_degrees()
+        total_degree = sum(degrees)
+        pagerank_values = [deg / total_degree if total_degree > 0 else 0 for deg in degrees]
+    
+    # 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
+
+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 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_grape(www_nodes, www_edges, sim_seed)
+    
+    # Test original setup
+    importance_old_dict = process_configuration_grape(
+        www_graph, kalicube_graph_old, kalicube_nodes_old, min_conn, max_conn
+    )
+    
+    # Test new setup
+    importance_new_dict = process_configuration_grape(
+        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
+    )
+    
+    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
+
+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."
+
+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")
+
+def main():
+    st.title("🔗 Page Link Impact Analyzer (Powered by Grape)")
+    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 Grape** - A high-performance graph library for faster and more efficient analysis!
+    """)
+    
+    # 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=["Small (5K sites)", "Medium (10K sites)", "Large (25K sites)", "Huge (50K sites)"],
+        value="Medium (10K sites)",
+        help="Larger = more realistic but slower"
+    )
+    
+    # Convert internet size to numbers
+    size_map = {
+        "Small (5K sites)": 5000,
+        "Medium (10K sites)": 10000, 
+        "Large (25K sites)": 25000,
+        "Huge (50K sites)": 50000
+    }
+    www_nodes = size_map[internet_size]
+    
+    # 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_grape(old_content, old_file.name)
+            
+            kalicube_graph_new, kalicube_nodes_new, kalicube_url_mapping_new = \
+                load_graph_from_csv_grape(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.get_number_of_edges()} links between them
+                """)
+            
+            with info_col2:
+                st.info(f"""
+                **Planned Setup:**
+                - {len(kalicube_nodes_new)} pages  
+                - {kalicube_graph_new.get_number_of_edges()} 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:
+                # Progress with encouraging messages
+                progress_bar = st.progress(0)
+                status_text = st.empty()
+                
+                encouraging_messages = [
+                    "🔬 Setting up internet simulation...",
+                    "🌐 Connecting your pages to the web...",
+                    "📊 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 thousands 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 Grape**
+            This version uses Grape, a high-performance graph library that's much faster than traditional tools for analyzing large networks.
+            """)
+        
+        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: Usually 30 seconds to 2 minutes, depending on your settings. Grape makes it 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 10,000 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 new with Grape?**
+            A: Grape is a high-performance graph library that makes calculations much faster and can handle larger datasets more efficiently than NetworkX.
+            """)
+
+if __name__ == "__main__":
+    main()