Fix deployment and requirements

.gitignore

@@ -0,0 +1,32 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Streamlit
+.streamlit/secrets.toml
+.streamlit/config.toml
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Environment variables
+.env
+.env.local

requirements.txt

@@ -1,715 +1,3 @@
-import streamlit as st
-import plotly.graph_objects as go
-import plotly.express as px
-import pandas as pd
-import hmac
-
-# Page config
-st.set_page_config(
-    page_title="ATP Trust Platform - Investor Dashboard",
-    layout="wide",
-    initial_sidebar_state="collapsed"
-)
-
-# Password protection
-def check_password():
-    """Returns `True` if the user had the correct password."""
-    
-    def password_entered():
-        """Checks whether a password entered by the user is correct."""
-        if hmac.compare_digest(st.session_state["password"], st.secrets["password"]):
-            st.session_state["password_correct"] = True
-            del st.session_state["password"]  # Don't store password
-        else:
-            st.session_state["password_correct"] = False
-
-    # First run, show input for password
-    if "password_correct" not in st.session_state:
-        st.markdown("## 🔐 ATP Trust Platform - Investor Dashboard")
-        st.text_input(
-            "Please enter password to view dashboard", 
-            type="password", 
-            on_change=password_entered, 
-            key="password"
-        )
-        return False
-    
-    # Password not correct, show input + error
-    elif not st.session_state["password_correct"]:
-        st.markdown("## 🔐 ATP Trust Platform - Investor Dashboard")
-        st.text_input(
-            "Please enter password to view dashboard", 
-            type="password", 
-            on_change=password_entered, 
-            key="password"
-        )
-        st.error("😕 Password incorrect")
-        return False
-    
-    # Password correct
-    else:
-        return True
-
-# Only show the dashboard if password is correct
-if not check_password():
-    st.stop()
-
-# Custom CSS
-st.markdown("""
-<style>
-.main {padding-top: 0rem;}
-.stTabs [data-baseweb="tab-list"] button {font-size: 20px;}
-.metric-card {
-    background-color: #f0f2f6;
-    padding: 20px;
-    border-radius: 10px;
-    text-align: center;
-}
-</style>
-""", unsafe_allow_html=True)
-
-# Header
-st.title("🛡️ ATP Trust Platform - 24 Month Journey")
-st.markdown("**Building Agentic AI Trust Platform**")
-
-# Executive Metrics
-col1, col2, col3 = st.columns(3)
-with col1:
-    st.metric("Timeline", "24 Months", "5 Phases")
-with col2:
-    st.metric("Team Growth", "2 → 36", "Lean & Expert")
-with col3:
-    st.metric("Focus", "Quality > Quantity", "AI-Augmented")
-
-st.divider()
-
-# Phase Data
-phases = {
-    "Phase 0: Research": {
-        "months": "1-4",
-        "duration": 4,
-        "team_start": 2,
-        "team_end": 7,
-        "key_hires": [
-            {"role": "Sr. AI Security Researcher", "inr_lakhs": 54, "usd": 65000},
-            {"role": "ML/AI Engineering Lead", "inr_lakhs": 60, "usd": 72000},
-            {"role": "Mid-Level AI Security Researcher", "inr_lakhs": 27, "usd": 32000},
-            {"role": "Security Expert", "inr_lakhs": 39, "usd": 47000},
-            {"role": "Data Engineer (ML Pipeline)", "inr_lakhs": 33, "usd": 40000},
-        ],
-        "deliverables": ["Complete Security Architecture", "Threat Taxonomy (150+)", "Agentic AI Framework", "3 Research Whitepapers", "5 Patent Applications"],
-    },
-    "Phase 1: Security MVP": {
-        "months": "5-10",
-        "duration": 6,
-        "team_start": 7,
-        "team_end": 11,
-        "key_hires": [
-            {"role": "Senior Backend Engineer", "inr_lakhs": 27, "usd": 32000},
-            {"role": "Senior Frontend Engineer", "inr_lakhs": 27, "usd": 32000},
-            {"role": "Junior ML Engineer", "inr_lakhs": 17, "usd": 20000},
-            {"role": "Governance Research Lead", "inr_lakhs": 45, "usd": 54000},
-        ],
-        "deliverables": ["Security MVP Launch", "50+ Beta Customers", "99% Detection Rate", "Governance/Compliance Research Complete"],
-    },
-    "Phase 2: Governance": {
-        "months": "11-16",
-        "duration": 6,
-        "team_start": 11,
-        "team_end": 19,
-        "key_hires": [
-            {"role": "Senior Product Manager", "inr_lakhs": 57, "usd": 68000},
-            {"role": "Platform Engineer", "inr_lakhs": 39, "usd": 47000},
-            {"role": "Backend Engineer", "inr_lakhs": 22, "usd": 26000},
-            {"role": "ML Engineer", "inr_lakhs": 33, "usd": 40000},
-            {"role": "Platform Engineer", "inr_lakhs": 28, "usd": 34000},
-            {"role": "Sales Engineer", "inr_lakhs": 39, "usd": 47000},
-            {"role": "Customer Success Lead", "inr_lakhs": 25, "usd": 30000},
-            {"role": "Technical Writer", "inr_lakhs": 20, "usd": 24000},
-        ],
-        "deliverables": ["Governance Module Complete", "200+ Customers", "Policy Engine Live", "RBAC Implementation"],
-    },
-    "Phase 3: Compliance": {
-        "months": "17-22",
-        "duration": 6,
-        "team_start": 19,
-        "team_end": 29,
-        "key_hires": [
-            {"role": "Compliance Engineer", "inr_lakhs": 39, "usd": 47000},
-            {"role": "Senior Backend Engineer", "inr_lakhs": 33, "usd": 40000},
-            {"role": "Platform Engineer", "inr_lakhs": 28, "usd": 34000},
-            {"role": "Frontend Engineer", "inr_lakhs": 24, "usd": 29000},
-            {"role": "Data Engineer", "inr_lakhs": 33, "usd": 40000},
-            {"role": "Sales Lead", "inr_lakhs": 46, "usd": 55000},
-            {"role": "Sales Rep", "inr_lakhs": 21, "usd": 25000},
-            {"role": "Customer Success Rep", "inr_lakhs": 18, "usd": 22000},
-            {"role": "Marketing Manager", "inr_lakhs": 33, "usd": 40000},
-            {"role": "Solutions Architect", "inr_lakhs": 42, "usd": 50000},
-        ],
-        "deliverables": ["Compliance Module Complete", "500+ Customers", "Multi-Jurisdiction Support", "Automated Reporting"],
-    },
-    "Phase 4: Full Platform": {
-        "months": "23-24",
-        "duration": 2,
-        "team_start": 29,
-        "team_end": 36,
-        "key_hires": [
-            {"role": "Senior AI Researcher", "inr_lakhs": 60, "usd": 72000},
-            {"role": "Security Engineer", "inr_lakhs": 39, "usd": 47000},
-            {"role": "ML Engineer", "inr_lakhs": 39, "usd": 47000},
-            {"role": "Backend Engineer", "inr_lakhs": 24, "usd": 29000},
-            {"role": "Backend Engineer", "inr_lakhs": 22, "usd": 26000},
-            {"role": "Customer Success Rep", "inr_lakhs": 18, "usd": 22000},
-            {"role": "Sales Rep", "inr_lakhs": 21, "usd": 25000},
-            {"role": "Operations Manager", "inr_lakhs": 33, "usd": 40000},
-        ],
-        "deliverables": ["GA Release", "1000+ Customers", "Full Platform Integration", "Global Deployment"],
-    }
-}
-
-# Tabs
-tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["📊 Phase Timeline", "👥 Team Scaling", "💰 Team Cost Structure", "🎯 Phase Details", "🚀 Ongoing Operations", "👨‍💻 Engineering Breakdown"])
-
-with tab1:
-    st.header("24-Month Phase Progression")
-    
-    # Gantt Chart
-    gantt_data = []
-    for phase, details in phases.items():
-        gantt_data.append({
-            "Phase": phase,
-            "Start": details["months"].split("-")[0],
-            "End": details["months"].split("-")[1],
-            "Team Size": f"{details['team_start']} → {details['team_end']}"
-        })
-    
-    # Timeline visualization
-    fig = go.Figure()
-    
-    y_pos = 0
-    colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#F7DC6F', '#BB8FCE']
-    
-    for i, (phase, details) in enumerate(phases.items()):
-        start_month = int(details["months"].split("-")[0])
-        end_month = int(details["months"].split("-")[1])
-        
-        fig.add_trace(go.Bar(
-            x=[end_month - start_month + 1],
-            y=[phase],
-            base=start_month - 1,
-            orientation='h',
-            name=phase,
-            text=f"{details['team_start']} → {details['team_end']} people",
-            textposition='inside',
-            hovertemplate='%{y}<br>Months %{base} to %{x}<br>%{text}<extra></extra>',
-            marker_color=colors[i],
-            showlegend=False
-        ))
-    
-    fig.update_layout(
-        title="Phase Timeline with Team Scaling",
-        xaxis_title="Months",
-        height=400,
-        xaxis=dict(range=[0, 25], tickmode='linear', tick0=0, dtick=3),
-        barmode='stack'
-    )
-    
-    st.plotly_chart(fig, use_container_width=True)
-
-with tab2:
-    st.header("Team Scaling Journey")
-    
-    # Team growth visualization
-    team_data = []
-    cumulative_team = 3
-    
-    for month in range(1, 25):
-        if month <= 4:
-            team_size = 2 + (month - 1) * 1.25  # Slower ramp in research phase
-        elif month <= 10:
-            team_size = 7 + (month - 4) * 0.67  # Phase 1: 7->11
-        elif month <= 16:
-            team_size = 11 + (month - 10) * 1.33  # Phase 2: 11->19
-        elif month <= 22:
-            team_size = 19 + (month - 16) * 1.67  # Phase 3: 19->29
-        else:
-            team_size = 29 + (month - 22) * 3.5  # Phase 4: 29->36
-        
-        team_data.append({"Month": month, "Team Size": int(team_size)})
-    
-    df_team = pd.DataFrame(team_data)
-    
-    fig3 = px.area(df_team, x='Month', y='Team Size', 
-                   title='Team Growth Over 24 Months',
-                   color_discrete_sequence=['#45B7D1'])
-    
-    # Add phase markers
-    for phase, details in phases.items():
-        start = int(details["months"].split("-")[0])
-        fig3.add_vline(x=start, line_dash="dot", line_color="gray", opacity=0.5)
-        fig3.add_annotation(x=start, y=details["team_end"], text=phase.split(":")[0], showarrow=False)
-    
-    fig3.update_layout(height=500)
-    st.plotly_chart(fig3, use_container_width=True)
-    
-    # Department breakdown
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.subheader("Team Composition by Phase 4")
-        dept_data = {
-            "Department": ["Engineering", "Sales", "Customer Success", "Product", "Leadership & Ops"],
-            "Headcount": [23, 4, 3, 3, 3]
-        }
-        fig4 = px.pie(dept_data, values='Headcount', names='Department', 
-                     title='36-Person Team Breakdown (64% Engineering)')
-        st.plotly_chart(fig4)
-    
-    with col2:
-        st.subheader("Hiring Velocity")
-        hiring_data = {
-            "Phase": ["Phase 0", "Phase 1", "Phase 2", "Phase 3", "Phase 4"],
-            "New Hires": [5, 4, 9, 10, 8]
-        }
-        fig5 = px.bar(hiring_data, x='Phase', y='New Hires', 
-                     title='Hiring by Phase',
-                     color_discrete_sequence=['#F7DC6F'])
-        st.plotly_chart(fig5)
-
-with tab3:
-    st.header("Team Cost Structure")
-    
-    # Department-wise breakdown
-    dept_breakdown = {
-        "Department": ["Engineering", "Product", "Sales", "Customer Success", "Leadership & Ops"],
-        "Headcount": [23, 3, 4, 3, 3],
-        "Avg Salary (₹L)": [36, 37, 32, 20, 44],
-        "Annual Cost (₹Cr)": [8.28, 1.10, 1.27, 0.61, 1.33],
-        "Annual Cost ($M)": [0.99, 0.13, 0.15, 0.07, 0.16]
-    }
-    
-    df_dept = pd.DataFrame(dept_breakdown)
-    
-    # Visualization 1: Department Cost Distribution
-    fig6 = go.Figure()
-    fig6.add_trace(go.Bar(
-        x=df_dept['Department'], 
-        y=df_dept['Annual Cost ($M)'],
-        text=[f"${val}M<br>{hc} people" for val, hc in zip(df_dept['Annual Cost ($M)'], df_dept['Headcount'])],
-        textposition='outside',
-        marker_color=['#4ECDC4', '#45B7D1', '#F7DC6F', '#BB8FCE', '#FF6B6B']
-    ))
-    
-    fig6.update_layout(
-        title='Annual Department Costs (35-Person Team)',
-        yaxis_title='Annual Cost (USD Millions)',
-        height=400,
-        showlegend=False
-    )
-    
-    st.plotly_chart(fig6, use_container_width=True)
-    
-    # Visualization 2: Headcount vs Cost pie charts
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        fig_hc = px.pie(df_dept, values='Headcount', names='Department', 
-                       title='Team Distribution by Headcount')
-        st.plotly_chart(fig_hc)
-    
-    with col2:
-        fig_cost = px.pie(df_dept, values='Annual Cost ($M)', names='Department',
-                         title='Cost Distribution by Department')
-        st.plotly_chart(fig_cost)
-    
-    # Key metrics
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        st.metric("Total Team Size", "36 people", "Lean & Expert")
-    with col2:
-        st.metric("Total Annual Cost", "$1.50M", "₹12.6 Cr")
-    with col3:
-        st.metric("Avg Cost per Person", "$41K", "₹34L")
-    
-    # Note about international hires
-    st.info("💡 **Note**: For specialized roles like LLM Security Experts or Framework Contributors, we may hire senior researchers from UK/Europe/USA on remote/consulting basis.")
-    
-    # Location benefits
-    st.subheader("Bangalore & Hyderabad Advantages")
-    
-    benefits = {
-        "Factor": ["Talent Pool", "Infrastructure", "Time Zone", "English Proficiency", "Startup Ecosystem"],
-        "Bangalore": ["IIT/IISc graduates, 2M+ IT professionals", "24/7 power, fiber connectivity", 
-                     "2.5 hrs ahead of EU", "95%+ fluency", "#3 startup hub globally"],
-        "Hyderabad": ["IIIT, ISB graduates, 600K+ IT professionals", "HITEC City, T-Hub", 
-                     "Perfect for US East Coast", "90%+ fluency", "Fastest growing tech hub"]
-    }
-    
-    df_benefits = pd.DataFrame(benefits)
-    st.table(df_benefits)
-
-with tab4:
-    st.header("Deep Dive: Phase Details")
-    
-    selected_phase = st.selectbox("Select a phase to explore", list(phases.keys()))
-    
-    phase_info = phases[selected_phase]
-    
-    col1, col2 = st.columns(2)
-    with col1:
-        st.metric("Duration", f"{phase_info['duration']} months", f"Months {phase_info['months']}")
-    with col2:
-        st.metric("Team Growth", f"{phase_info['team_start']} → {phase_info['team_end']}", 
-                 f"+{phase_info['team_end'] - phase_info['team_start']} people")
-    
-    st.divider()
-    
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.subheader("🎯 Key Deliverables")
-        for deliverable in phase_info['deliverables']:
-            st.write(f"✓ {deliverable}")
-    
-    with col2:
-        st.subheader("👥 Key Hires")
-        hire_df = pd.DataFrame(phase_info['key_hires'])
-        hire_df['India Cost'] = hire_df['inr_lakhs'].apply(lambda x: f"₹{x}L (${int(x*1200):,})")
-        st.dataframe(hire_df[['role', 'India Cost']], hide_index=True)
-
-with tab5:
-    st.header("🚀 Ongoing Operations Team Structure")
-    st.markdown("### Post-Launch Team (After Month 24)")
-    
-    # Ongoing team structure
-    ongoing_team = {
-        "Department": ["Engineering", "Customer Success", "Onboarding", "Sales", "Product", "Leadership & Ops"],
-        "Headcount": [23, 8, 4, 4, 3, 4],
-        "Purpose": [
-            "Maintain & enhance platform",
-            "24/7 customer support",
-            "Customer implementation",
-            "New customer acquisition",
-            "Product roadmap & features",
-            "Strategic direction & operations"
-        ],
-        "Key Roles": [
-            "Engineers, Researchers, Architects",
-            "Support Engineers, Success Managers",
-            "Implementation Specialists",
-            "Sales Reps, Sales Engineers",
-            "Product Managers, Technical Writers",
-            "CEO, CTO, Operations Manager"
-        ]
-    }
-    
-    df_ongoing = pd.DataFrame(ongoing_team)
-    
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        # Team size comparison
-        comparison_data = {
-            "Phase": ["Phase 4 (Launch)", "Ongoing Operations"],
-            "Total Team": [36, 46],
-            "Engineering": [23, 23],
-            "Customer Facing": [6, 16],
-            "Others": [6, 7]
-        }
-        df_compare = pd.DataFrame(comparison_data)
-        
-        fig_compare = go.Figure()
-        fig_compare.add_trace(go.Bar(name='Engineering', x=df_compare['Phase'], y=df_compare['Engineering']))
-        fig_compare.add_trace(go.Bar(name='Customer Facing', x=df_compare['Phase'], y=df_compare['Customer Facing']))
-        fig_compare.add_trace(go.Bar(name='Others', x=df_compare['Phase'], y=df_compare['Others']))
-        
-        fig_compare.update_layout(
-            title='Team Evolution: Launch vs Ongoing',
-            barmode='stack',
-            height=400
-        )
-        st.plotly_chart(fig_compare)
-    
-    with col2:
-        # Ongoing team distribution
-        fig_ongoing = px.pie(
-            values=df_ongoing['Headcount'], 
-            names=df_ongoing['Department'],
-            title='Ongoing Operations Team (45 People)'
-        )
-        st.plotly_chart(fig_ongoing)
-    
-    # Detailed breakdown
-    st.subheader("📋 Department Details")
-    
-    # Create expandable sections for each department
-    with st.expander("🔧 Engineering (23 people) - No Change"):
-        st.markdown("""
-        **Maintains same size as Phase 4:**
-        - Core platform development
-        - Security updates and patches
-        - New feature development
-        - Performance optimization
-        - Integration maintenance
-        """)
-    
-    with st.expander("🤝 Customer Success (8 people) - Scaled from 3"):
-        st.markdown("""
-        **Expanded for 24/7 support:**
-        - 4 Customer Success Managers (₹25-30L)
-        - 2 Senior Support Engineers (₹20-25L)
-        - 2 Support Engineers (₹15-18L)
-        
-        **Coverage:**
-        - 24/7 support across timezones
-        - Enterprise customer management
-        - Technical troubleshooting
-        - Customer health monitoring
-        """)
-    
-    with st.expander("🚀 Onboarding Team (4 people) - New Addition"):
-        st.markdown("""
-        **New team for customer implementation:**
-        - 2 Implementation Specialists (₹25-30L)
-        - 1 Integration Engineer (₹30-35L)
-        - 1 Training Specialist (₹20-25L)
-        
-        **Responsibilities:**
-        - Customer onboarding (2-4 weeks)
-        - Integration support
-        - Training and documentation
-        - Best practices consultation
-        """)
-    
-    with st.expander("💼 Sales (4 people) - No Change"):
-        st.markdown("""
-        **Maintains current structure:**
-        - 1 Sales Lead
-        - 2 Sales Representatives
-        - 1 Sales Engineer (Solutions Architect)
-        """)
-    
-    # Cost analysis
-    st.subheader("💰 Ongoing Operations Budget")
-    
-    ongoing_costs = {
-        "Department": ["Engineering", "Customer Success", "Onboarding", "Sales", "Product", "Leadership & Ops"],
-        "Headcount": [23, 8, 4, 4, 3, 4],
-        "Avg Salary (₹L)": [36, 23, 27, 32, 37, 44],
-        "Annual Cost (₹Cr)": [8.28, 1.84, 1.08, 1.28, 1.11, 1.76],
-        "Annual Cost ($M)": [0.99, 0.22, 0.13, 0.15, 0.13, 0.21]
-    }
-    
-    df_costs = pd.DataFrame(ongoing_costs)
-    
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        st.metric("Total Team Size", "45 people", "+10 from launch")
-    with col2:
-        st.metric("Annual Cost", "$1.83M", "₹15.4 Cr")
-    with col3:
-        st.metric("Avg Cost/Person", "$39K", "₹33L")
-    
-    st.dataframe(df_costs, hide_index=True)
-    
-    st.info("💡 **Key Strategy**: Engineering team remains stable while customer-facing teams scale to support 1000+ customers")
-
-with tab6:
-    st.header("👨‍💻 Engineering Team Breakdown")
-    st.markdown("### Detailed Engineering Organization (23 People)")
-    
-    # Engineering breakdown data
-    eng_breakdown = {
-        "Department": ["Research & AI", "Backend Engineering", "Frontend Engineering", "ML/Data Engineering", "Platform Engineering", "Security", "Solutions & Architecture"],
-        "Headcount": [6, 5, 2, 5, 3, 1, 1],
-        "Roles": [
-            "AI Security Researchers, Governance/Compliance Researchers",
-            "Senior & Mid-level Backend Engineers, Platform Engineers",
-            "Senior & Mid-level Frontend Engineers",
-            "ML Engineers, Data Engineers, MLOps",
-            "Platform Engineers, SRE, DevOps",
-            "Security Expert",
-            "Solutions Architect"
-        ],
-        "Focus Areas": [
-            "Threat research, AI safety, Governance frameworks",
-            "Core APIs, Microservices, Scalability",
-            "Dashboard, User experience, Real-time UI",
-            "Models, Data pipelines, Performance",
-            "Infrastructure, CI/CD, Monitoring, Scalability",
-            "Security architecture, Threat modeling",
-            "Customer integration, Best practices"
-        ]
-    }
-    
-    df_eng = pd.DataFrame(eng_breakdown)
-    
-    # Visualization
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        fig_eng = px.bar(
-            df_eng, 
-            x='Department', 
-            y='Headcount',
-            title='Engineering Team Distribution',
-            color='Headcount',
-            color_continuous_scale='Blues'
-        )
-        fig_eng.update_layout(height=400, showlegend=False)
-        st.plotly_chart(fig_eng)
-    
-    with col2:
-        fig_eng_pie = px.pie(
-            values=df_eng['Headcount'],
-            names=df_eng['Department'],
-            title='Engineering Team Composition'
-        )
-        st.plotly_chart(fig_eng_pie)
-    
-    # Detailed role breakdown
-    st.subheader("📊 Detailed Role Distribution")
-    
-    roles_detail = {
-        "Role": [
-            "Sr. AI Security Researcher",
-            "ML/AI Engineering Lead",
-            "Mid-Level AI Security Researcher",
-            "Governance Research Lead",
-            "Senior AI Researcher",
-            "Compliance Engineer",
-            "Senior Backend Engineer (2)",
-            "Backend Engineer (3)",
-            "Senior Frontend Engineer",
-            "Frontend Engineer",
-            "Junior ML Engineer",
-            "ML Engineer (2)",
-            "Data Engineer (2)",
-            "Senior Platform Engineer",
-            "Platform Engineer (2)",
-            "Security Expert",
-            "Solutions Architect"
-        ],
-        "Department": [
-            "Research & AI", "Research & AI", "Research & AI", "Research & AI", "Research & AI", "Research & AI",
-            "Backend", "Backend",
-            "Frontend", "Frontend",
-            "ML/Data", "ML/Data", "ML/Data",
-            "Platform", "Platform",
-            "Security",
-            "Solutions"
-        ],
-        "Salary (₹L)": [
-            54, 60, 27, 45, 60, 39,
-            27, 22,
-            27, 24,
-            17, 39, 33,
-            39, 28,
-            39,
-            42
-        ]
-    }
-    
-    df_roles = pd.DataFrame(roles_detail)
-    
-    # Group by department
-    dept_groups = df_roles.groupby('Department')
-    
-    for dept, group in dept_groups:
-        with st.expander(f"{dept} Team"):
-            st.dataframe(group[['Role', 'Salary (₹L)']], hide_index=True)
-            avg_salary = group['Salary (₹L)'].mean()
-            total_cost = group['Salary (₹L)'].sum()
-            st.metric("Average Salary", f"₹{avg_salary:.0f}L", f"Total: ₹{total_cost/100:.2f} Cr")
-    
-    # Engineering metrics
-    st.subheader("🎯 Engineering Team Metrics")
-    
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        st.metric("Research Focus", "26%", "6 of 23 engineers")
-    with col2:
-        st.metric("Core Development", "30%", "7 backend/frontend")
-    with col3:
-        st.metric("ML/Data Focus", "22%", "5 engineers")
-    with col4:
-        st.metric("Avg Engineering Salary", "₹36L", "$43K")
-    
-    # Skills distribution
-    st.subheader("🛠️ Key Technical Skills Coverage")
-    
-    skills_data = {
-        "Skill Category": ["AI/ML", "Security", "Backend", "Frontend", "Data", "DevOps", "Architecture"],
-        "Coverage": [9, 8, 7, 2, 5, 3, 4],
-        "Percentage": ["39%", "35%", "30%", "9%", "22%", "13%", "17%"]
-    }
-    
-    df_skills = pd.DataFrame(skills_data)
-    
-    fig_skills = go.Figure()
-    fig_skills.add_trace(go.Bar(
-        x=df_skills['Skill Category'],
-        y=df_skills['Coverage'],
-        text=df_skills['Percentage'],
-        textposition='outside',
-        marker_color='lightblue'
-    ))
-    
-    fig_skills.update_layout(
-        title='Technical Skills Distribution (People can have multiple skills)',
-        yaxis_title='Number of Engineers',
-        height=400
-    )
-    
-    st.plotly_chart(fig_skills, use_container_width=True)
-    
-    st.info("💡 **Engineering Philosophy**: Deep expertise in AI/Security (61% of team) with strong full-stack capabilities")
-    
-
-# Footer
-st.divider()
-st.markdown("""
-### 📌 Key Takeaways
-- **Lean Start**: Begin with just 5 strategic core team members in Phase 0
-- **AI-Augmented Team**: 36 experts doing work of 70+ with AI tools
-- **India Advantage**: 64% cost savings vs US hiring (with 20% premium)
-- **Clear Milestones**: Each phase has specific technical deliverables
-- **Engineering Heavy**: 64% engineers, minimal overhead
-""")
-
-# Download button for executive summary
-exec_summary = f"""
-ATP TRUST PLATFORM - EXECUTIVE SUMMARY
-======================================
-
-TIMELINE: 24 months (5 phases)
-TEAM: 2 → 35 people (Lean & Expert)
-
-PHASE BREAKDOWN:
-- Phase 0 (M1-4): Research & Architecture | 2→7 people
-- Phase 1 (M5-10): Security MVP + Gov/Comp Research | 7→11 people  
-- Phase 2 (M11-16): Governance Module | 11→19 people
-- Phase 3 (M17-22): Compliance Module | 19→29 people
-- Phase 4 (M23-24): Full Platform GA | 29→36 people
-
-INDIA ADVANTAGE:
-- Average salary: $41K (vs $115K in US)
-- 64% cost savings
-- Access to IIT/IIIT talent pool
-- Bangalore & Hyderabad tech hubs
-
-KEY DELIVERABLES:
-- AI Security Platform with 99% detection rate
-- Governance & Policy Engine
-- Multi-jurisdiction Compliance
-- Enterprise-ready Platform
-"""
-
-st.download_button(
-    label="📥 Download Executive Summary",
-    data=exec_summary,
-    file_name="ATP_Executive_Summary.txt",
-    mime="text/plain"
-)
+streamlit==1.32.0
+plotly==5.19.0
+pandas==2.2.0

streamlit-dashboard.py

@@ -3,6 +3,7 @@ import plotly.graph_objects as go
 import plotly.express as px
 import pandas as pd
 import hmac
+import os
 
 # Page config
 st.set_page_config(
@@ -17,7 +18,15 @@ def check_password():
     
     def password_entered():
         """Checks whether a password entered by the user is correct."""
-        if hmac.compare_digest(st.session_state["password"], st.secrets["password"]):
+        # Try environment variable first, then st.secrets, then default
+        correct_password = os.environ.get("password", None)
+        if correct_password is None:
+            try:
+                correct_password = st.secrets["password"]
+            except:
+                correct_password = "LangGraph@ATP2025!"  # Fallback
+        
+        if hmac.compare_digest(st.session_state["password"], correct_password):
             st.session_state["password_correct"] = True
             del st.session_state["password"]  # Don't store password
         else: