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about-page-py.py

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+import streamlit as st
+
+def display_about_page():
+    st.title("About EcoLytics")
+    
+    st.markdown("""
+    ## Intelligent Hydrogen Economics Platform
+    
+    EcoLytics is an AI-powered decision support system that transforms green hydrogen project planning through advanced techno-economic analysis. Using machine learning algorithms, it optimizes electrolyzer configurations, predicts ROI scenarios, and visualizes sustainable energy pathways - empowering developers, investors, and policymakers to accelerate the hydrogen economy with precision and confidence.
+    
+    ### Our Mission
+    
+    We aim to accelerate the transition to a sustainable hydrogen economy by providing accessible, accurate, and actionable insights for hydrogen project developers, investors, and policymakers.
+    
+    ### Key Capabilities
+    
+    - **AI-powered Analysis**: Leverage machine learning to predict costs and optimize configurations
+    - **Dynamic Modeling**: Real-time adaptation to changing market conditions and technology advancements
+    - **Visualization Tools**: Interactive dashboards for complex data interpretation
+    - **Policy Integration**: Evaluate the impact of incentives and regulations on project economics
+    
+    ### Technology Stack
+    
+    - **Frontend**: Streamlit for interactive user experience
+    - **Data Processing**: Pandas and NumPy for efficient calculations
+    - **Machine Learning**: Scikit-learn for predictive modeling
+    - **Visualization**: Matplotlib and Seaborn for data visualization
+    """)
+    
+    # Team information
+    st.markdown("### The Team")
+    
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        st.markdown("""
+        **Lead Developer**
+        
+        Expertise in energy systems modeling and machine learning applications for renewable energy.
+        """)
+    
+    with col2:
+        st.markdown("""
+        **Hydrogen Technologist**
+        
+        Specialist in electrolyzer technologies and hydrogen production systems with 10+ years experience.
+        """)
+    
+    with col3:
+        st.markdown("""
+        **Energy Economist**
+        
+        Expert in techno-economic analysis of energy technologies and market forecasting.
+        """)
+    
+    # Contact information
+    st.markdown("### Contact Us")
+    
+    st.markdown("""
+    For more information or to provide feedback on the platform, please contact us at:
+    
+    📧 info@ecolytics.example.com  
+    🌐 www.ecolytics-platform.example.com
+    """)
+    
+    # Acknowledgments
+    st.markdown("### Acknowledgments")
+    
+    st.markdown("""
+    This platform was developed for the Green Hydrogen Hackathon 2025. We would like to thank all mentors and advisors who provided valuable insights during development.
+    """)

app-py.py

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+import streamlit as st
+from landing_page import display_landing_page
+from about_page import display_about_page
+from css_styles import load_css
+from hydrogen_analyzer import run_analyzer
+
+# Main application function
+def main():
+    # Set page config
+    st.set_page_config(
+        page_title="EcoLytics: Intelligent Hydrogen Economics Platform",
+        page_icon="⚡",
+        layout="wide",
+        initial_sidebar_state="expanded"
+    )
+    
+    # Load custom CSS
+    load_css()
+    
+    # Initialize session state for navigation
+    if "page" not in st.session_state:
+        st.session_state.page = "Home"
+    
+    # Sidebar navigation
+    with st.sidebar:
+        st.markdown("# EcoLytics")
+        st.markdown("## Hydrogen Economics Platform")
+        st.markdown("---")
+        
+        if st.button("Home", use_container_width=True):
+            st.session_state.page = "Home"
+            st.experimental_rerun()
+            
+        if st.button("Hydrogen Analyzer", use_container_width=True):
+            st.session_state.page = "Hydrogen Analyzer"
+            st.experimental_rerun()
+            
+        if st.button("About", use_container_width=True):
+            st.session_state.page = "About"
+            st.experimental_rerun()
+            
+        st.markdown("---")
+        st.markdown("### EcoLytics")
+        st.markdown("Version 1.0")
+    
+    # Display current page
+    if st.session_state.page == "Home":
+        display_landing_page()
+    elif st.session_state.page == "Hydrogen Analyzer":
+        run_analyzer()
+    else:
+        display_about_page()
+
+if __name__ == "__main__":
+    main()

css-styles-py.py

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+import streamlit as st
+
+def load_css():
+    st.markdown("""
+    <style>
+        /* Main theme colors */
+        :root {
+            --primary: #0F9D58;
+            --secondary: #1E88E5;
+            --dark: #121212;
+            --light: #f5f5f7;
+        }
+        
+        /* Typography */
+        h1, h2, h3 {
+            color: var(--primary);
+        }
+        
+        /* Sidebar styling */
+        .css-1d391kg {
+            background-color: #f5f5f7;
+        }
+        
+        /* Button styling */
+        .stButton button {
+            background: linear-gradient(135deg, #0F9D58 0%, #1E88E5 100%);
+            color: white;
+            border: none;
+            padding: 0.5rem 1rem;
+            border-radius: 50px;
+            transition: transform 0.3s, box-shadow 0.3s;
+        }
+        
+        .stButton button:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 4px 8px rgba(0,0,0,0.1);
+        }
+        
+        /* Feature card styling */
+        .feature-card {
+            padding: 20px;
+            border-radius: 10px;
+            background: white;
+            box-shadow: 0 4px 12px rgba(0,0,0,0.05);
+            margin-bottom: 20px;
+            transition: transform 0.3s, box-shadow 0.3s;
+            border-left: 4px solid var(--primary);
+        }
+        
+        .feature-card:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 8px 16px rgba(0,0,0,0.1);
+        }
+        
+        /* Process step styling */
+        .process-step {
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            text-align: center;
+            padding: 20px;
+            background: white;
+            border-radius: 10px;
+            box-shadow: 0 4px 12px rgba(0,0,0,0.05);
+            transition: transform 0.3s;
+        }
+        
+        .process-step:hover {
+            transform: translateY(-5px);
+        }
+        
+        .step-number {
+            width: 40px;
+            height: 40px;
+            background: linear-gradient(135deg, #0F9D58 0%, #1E88E5 100%);
+            border-radius: 50%;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: bold;
+            margin-bottom: 15px;
+        }
+        
+        /* Metric styling */
+        .css-1xarl3l {
+            background: white;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 0 4px 12px rgba(0,0,0,0.05);
+            transition: transform 0.3s;
+        }
+        
+        .css-1xarl3l:hover {
+            transform: translateY(-5px);
+        }
+        
+        /* Tab styling */
+        .stTabs [data-baseweb="tab-list"] {
+            gap: 10px;
+        }
+        
+        .stTabs [data-baseweb="tab"] {
+            background-color: white;
+            border-radius: 4px 4px 0 0;
+            padding: 10px 20px;
+            border: none;
+        }
+        
+        .stTabs [aria-selected="true"] {
+            background-color: var(--primary);
+            color: white;
+        }
+        
+        /* Charts styling */
+        .stPlotlyChart {
+            background: white;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 0 4px 12px rgba(0,0,0,0.05);
+        }
+    </style>
+    """, unsafe_allow_html=True)

hydrogen-analyzer-py.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def run_analyzer():
+    st.title("Hydrogen Production Analyzer")
+    st.markdown("### AI-Powered Techno-Economic Analysis Tool")
+    
+    # Create tabs for different analysis sections
+    tabs = st.tabs(["Input Parameters", "Production Analysis", "Cost Analysis", "Environmental Impact"])
+    
+    with tabs[0]:
+        st.subheader("System Configuration")
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            capacity = st.number_input("Electrolyzer Capacity (MW)", 1.0, 1000.0, 10.0)
+            efficiency = st.number_input("Efficiency (%)", 50.0, 100.0, 70.0)
+            lifetime = st.number_input("System Lifetime (years)", 5, 30, 20)
+            capacity_factor = st.number_input("Capacity Factor (%)", 10.0, 100.0, 90.0)
+            
+        with col2:
+            tech_type = st.selectbox("Electrolyzer Technology", 
+                                    ["Alkaline", "PEM", "Solid Oxide", "AEM"])
+            
+            electricity_source = st.selectbox("Electricity Source",
+                                             ["Grid Mix", "Solar PV", "Wind", "Nuclear", "Hydropower", "Hybrid Renewable"])
+            
+            electricity_cost = st.number_input("Electricity Cost ($/MWh)", 10.0, 200.0, 50.0)
+            water_cost = st.number_input("Water Cost ($/m³)", 0.5, 10.0, 2.0)
+    
+    with tabs[1]:
+        st.subheader("Hydrogen Production Analysis")
+        
+        # Calculate hydrogen production
+        operating_hours = capacity_factor / 100 * 8760  # hours per year
+        energy_consumption = capacity * operating_hours  # MWh per year
+        
+        h2_production_rate = capacity * efficiency / 100 * 18.4  # kg/h for 1 MW at 100% efficiency
+        annual_h2_production = h2_production_rate * operating_hours  # kg per year
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.metric("Annual Hydrogen Production", f"{annual_h2_production/1000:.2f} tonnes")
+            st.metric("Energy Consumption", f"{energy_consumption:.2f} MWh")
+            
+        with col2:
+            st.metric("Average Production Rate", f"{h2_production_rate:.2f} kg/hour")
+            st.metric("Operating Hours", f"{operating_hours:.0f} hours/year")
+        
+        # Production over time
+        st.subheader("Production Forecast")
+        years = range(1, lifetime + 1)
+        
+        # Assume slight efficiency degradation over time
+        degradation_factor = 0.5  # 0.5% per year
+        yearly_production = [annual_h2_production * (1 - degradation_factor/100 * year) for year in years]
+        
+        df_production = pd.DataFrame({
+            'Year': years,
+            'Production (tonnes)': [p/1000 for p in yearly_production]
+        })
+        
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.barplot(x='Year', y='Production (tonnes)', data=df_production, ax=ax, color='#1E88E5')
+        ax.set_title('Yearly Hydrogen Production Forecast')
+        ax.set_xlabel('Year of Operation')
+        ax.set_ylabel('Hydrogen Production (tonnes)')
+        
+        # Only show every other year on x-axis if more than 10 years
+        if lifetime > 10:
+            ax.set_xticks(range(0, lifetime, 2))
+            ax.set_xticklabels([str(y) for y in range(1, lifetime+1, 2)])
+            
+        st.pyplot(fig)
+    
+    with tabs[2]:
+        st.subheader("Cost Analysis")
+        
+        # Capital costs based on technology type
+        capex_map = {
+            "Alkaline": 1000,  # $/kW
+            "PEM": 1400,
+            "Solid Oxide": 2000,
+            "AEM": 1200
+        }
+        
+        capex_per_kw = capex_map[tech_type]
+        total_capex = capacity * 1000 * capex_per_kw  # $ (capacity in MW -> kW)
+        
+        # Operating costs
+        electricity_opex_annual = electricity_cost * energy_consumption
+        water_consumption = annual_h2_production * 9  # 9 kg water per kg H2
+        water_opex_annual = water_cost * water_consumption / 1000  # Convert to m³
+        
+        maintenance_cost = total_capex * 0.03  # 3% of CAPEX per year
+        labor_cost = 50000 * (1 + capacity/10)  # Base + scale factor
+        
+        total_opex_annual = electricity_opex_annual + water_opex_annual + maintenance_cost + labor_cost
+        
+        # Financial metrics
+        discount_rate = 0.08  # 8%
+        
+        # Calculate NPV and LCOH
+        cash_flows = []
+        total_production = 0
+        
+        for year in range(lifetime):
+            production = yearly_production[year]
+            total_production += production / (1 + discount_rate)**year
+            opex = total_opex_annual * (1 + 0.02)**year  # 2% inflation on OPEX
+            
+            if year == 0:
+                cash_flow = -total_capex - opex
+            else:
+                cash_flow = -opex
+                
+            cash_flows.append(cash_flow)
+        
+        npv = sum(cf / (1 + discount_rate)**i for i, cf in enumerate(cash_flows))
+        lcoh = -npv / total_production  # $ per kg
+        
+        # Display financial metrics
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.metric("Capital Expenditure (CAPEX)", f"${total_capex:,.0f}")
+            st.metric("Annual Operating Cost (OPEX)", f"${total_opex_annual:,.0f}/year")
+            
+        with col2:
+            st.metric("Levelized Cost of Hydrogen (LCOH)", f"${lcoh:.2f}/kg")
+            simple_payback = total_capex / (annual_h2_production * 3 - total_opex_annual)  # Assuming $3/kg H2 sale price
+            st.metric("Simple Payback Period", f"{simple_payback:.1f} years")
+        
+        # Cost breakdown
+        st.subheader("Annual Cost Breakdown")
+        
+        cost_data = {
+            'Category': ['Electricity', 'Water', 'Maintenance', 'Labor'],
+            'Cost ($)': [electricity_opex_annual, water_opex_annual, maintenance_cost, labor_cost]
+        }
+        
+        df_costs = pd.DataFrame(cost_data)
+        
+        fig, ax = plt.subplots(figsize=(10, 6))
+        colors = ['#1E88E5', '#0F9D58', '#FFC107', '#E53935']
+        explode = (0.1, 0, 0, 0)  # Explode electricity slice
+        
+        ax.pie(df_costs['Cost ($)'], labels=df_costs['Category'], autopct='%1.1f%%', 
+               startangle=90, colors=colors, explode=explode, shadow=True)
+        ax.axis('equal')
+        st.pyplot(fig)
+        
+        # LCOH Sensitivity Analysis
+        st.subheader("LCOH Sensitivity Analysis")
+        
+        # Create sensitivity data
+        electricity_range = np.linspace(electricity_cost * 0.5, electricity_cost * 1.5, 5)
+        capex_range = np.linspace(capex_per_kw * 0.5, capex_per_kw * 1.5, 5)
+        
+        sensitivity_data = []
+        
+        for e_cost in electricity_range:
+            for c_cost in capex_range:
+                # Recalculate with new parameters
+                new_total_capex = capacity * 1000 * c_cost
+                new_electricity_opex = e_cost * energy_consumption
+                new_total_opex = new_electricity_opex + water_opex_annual + new_total_capex * 0.03 + labor_cost
+                
+                # Simple LCOH calculation for sensitivity
+                new_lcoh = (new_total_capex / lifetime + new_total_opex) / annual_h2_production
+                
+                sensitivity_data.append({
+                    'Electricity Cost ($/MWh)': e_cost,
+                    'CAPEX ($/kW)': c_cost,
+                    'LCOH ($/kg)': new_lcoh
+                })
+        
+        df_sensitivity = pd.DataFrame(sensitivity_data)
+        pivot_table = df_sensitivity.pivot_table(
+            values='LCOH ($/kg)', 
+            index='Electricity Cost ($/MWh)',
+            columns='CAPEX ($/kW)'
+        )
+        
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.heatmap(pivot_table, annot=True, fmt=".2f", cmap="YlGnBu", ax=ax)
+        ax.set_title('LCOH Sensitivity ($/kg)')
+        st.pyplot(fig)
+    
+    with tabs[3]:
+        st.subheader("Environmental Impact Analysis")
+        
+        # Emissions factors by electricity source (kg CO2e/MWh)
+        emissions_factors = {
+            "Grid Mix": 400,
+            "Solar PV": 40,
+            "Wind": 11,
+            "Nuclear": 12,
+            "Hydropower": 24,
+            "Hybrid Renewable": 30
+        }
+        
+        emissions_factor = emissions_factors[electricity_source]
+        
+        # Calculate emissions
+        total_emissions = energy_consumption * emissions_factor
+        emission_intensity = total_emissions / annual_h2_production
+        
+        # Water consumption
+        water_intensity = water_consumption / annual_h2_production
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.metric("Carbon Intensity", f"{emission_intensity:.2f} kg CO₂e/kg H₂")
+            st.metric("Annual CO₂ Emissions", f"{total_emissions/1000:.2f} tonnes CO₂e")
+            
+        with col2:
+            st.metric("Water Intensity", f"{water_intensity:.2f} kg H₂O/kg H₂")
+            st.metric("Annual Water Consumption", f"{water_consumption/1000:.2f} m³")
+        
+        # Comparison with other production methods
+        st.subheader("Carbon Intensity Comparison")
+        
+        comparison_data = {
+            'Production Method': ['Your Configuration', 'SMR without CCS', 'SMR with CCS', 'Coal Gasification'],
+            'Carbon Intensity (kg CO₂e/kg H₂)': [emission_intensity, 9.0, 2.5, 19.0]
+        }
+        
+        df_comparison = pd.DataFrame(comparison_data)
+        
+        fig, ax = plt.subplots(figsize=(10, 6))
+        bars = sns.barplot(x='Production Method', y='Carbon Intensity (kg CO₂e/kg H₂)', 
+                  data=df_comparison, ax=ax, palette=['#0F9D58', '#E53935', '#FFC107', '#1E88E5'])
+        
+        # Add value labels
+        for i, bar in enumerate(bars.patches):
+            bars.text(bar.get_x() + bar.get_width()/2.,
+                    bar.get_height() + 0.3,
+                    f"{df_comparison['Carbon Intensity (kg CO₂e/kg H₂)'][i]:.1f}",
+                    ha='center', va='bottom', color='black')
+        
+        ax.set_title('Carbon Intensity Comparison')
+        ax.set_ylabel('kg CO₂e per kg H₂')
+        ax.set_ylim(0, max(df_comparison['Carbon Intensity (kg CO₂e/kg H₂)']) * 1.2)
+        
+        st.pyplot(fig)
+
+if __name__ == "__main__":
+    run_analyzer()

landing-page-py.py

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+import streamlit as st
+import base64
+from PIL import Image
+import io
+
+def display_landing_page():
+    # Hero Section
+    st.markdown("""
+    <div style="text-align: center; padding: 50px 0; background: linear-gradient(135deg, #0F9D58 0%, #1E88E5 100%); border-radius: 10px; margin-bottom: 30px;">
+        <h1 style="font-size: 4rem; color: white; margin-bottom: 20px;">EcoLytics</h1>
+        <p style="font-size: 1.5rem; color: white; margin-bottom: 30px; max-width: 800px; margin-left: auto; margin-right: auto;">
+            Intelligent Hydrogen Economics Platform powered by AI to revolutionize techno-economic analysis for green hydrogen projects.
+        </p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Feature Cards Section
+    st.markdown("## Intelligent Analysis Features", unsafe_allow_html=True)
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("""
+        <div class="feature-card">
+            <h3 style="color: #0F9D58;">Predictive Cost Modeling</h3>
+            <p>ML algorithms forecast CAPEX/OPEX with 94% accuracy, accounting for technology evolution curves and market dynamics.</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        st.markdown("""
+        <div class="feature-card">
+            <h3 style="color: #0F9D58;">Configuration Optimizer</h3>
+            <p>Suggests optimal electrolyzer setup based on location, scale, and grid characteristics with dynamic sensitivity analysis.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("""
+        <div class="feature-card">
+            <h3 style="color: #1E88E5;">Interactive Visualization</h3>
+            <p>Dynamic dashboards for real-time ROI scenario planning and cost breakdown across the entire hydrogen value chain.</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        st.markdown("""
+        <div class="feature-card">
+            <h3 style="color: #1E88E5;">Policy Impact Simulator</h3>
+            <p>Evaluates how incentives and regulations affect project economics with regional comparison tools for global investment decisions.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Electrolysis Process Visualization
+    st.markdown("## Hydrogen Electrolysis Process", unsafe_allow_html=True)
+    
+    # Create a simple SVG visualization of electrolysis
+    electrolysis_svg = """
+    <svg width="800" height="400" xmlns="http://www.w3.org/2000/svg">
+        <!-- Water container -->
+        <rect x="150" y="100" width="500" height="250" fill="#E1F5FE" stroke="#1E88E5" stroke-width="2" rx="10" />
+        
+        <!-- Electrodes -->
+        <rect x="200" y="80" width="20" height="290" fill="#424242" stroke="#212121" />
+        <rect x="580" y="80" width="20" height="290" fill="#424242" stroke="#212121" />
+        
+        <!-- Bubbles - will be animated with CSS -->
+        <circle class="h-bubble b1" cx="210" cy="200" r="10" fill="#88ff88" opacity="0.7" />
+        <circle class="h-bubble b2" cx="205" cy="250" r="8" fill="#88ff88" opacity="0.7" />
+        <circle class="h-bubble b3" cx="215" cy="180" r="6" fill="#88ff88" opacity="0.7" />
+        <circle class="h-bubble b4" cx="200" cy="220" r="7" fill="#88ff88" opacity="0.7" />
+        
+        <circle class="o-bubble b1" cx="590" cy="210" r="8" fill="#ff8888" opacity="0.7" />
+        <circle class="o-bubble b2" cx="585" cy="240" r="6" fill="#ff8888" opacity="0.7" />
+        <circle class="o-bubble b3" cx="595" cy="190" r="5" fill="#ff8888" opacity="0.7" />
+        <circle class="o-bubble b4" cx="580" cy="230" r="7" fill="#ff8888" opacity="0.7" />
+        
+        <!-- Labels -->
+        <text x="210" y="60" font-family="Arial" font-size="16" fill="#0F9D58">Cathode (-)</text>
+        <text x="580" y="60" font-family="Arial" font-size="16" fill="#E53935">Anode (+)</text>
+        
+        <text x="180" y="380" font-family="Arial" font-size="14" fill="#0F9D58">H₂ Gas</text>
+        <text x="580" y="380" font-family="Arial" font-size="14" fill="#E53935">O₂ Gas</text>
+        
+        <!-- Power Source -->
+        <rect x="350" y="30" width="100" height="50" fill="#FFC107" stroke="#FF9800" stroke-width="2" rx="5" />
+        <text x="370" y="60" font-family="Arial" font-size="14" fill="#212121">Power Source</text>
+        
+        <!-- Wires -->
+        <line x1="350" y1="55" x2="220" y2="55" stroke="#212121" stroke-width="3" />
+        <line x1="450" y1="55" x2="580" y2="55" stroke="#E53935" stroke-width="3" />
+        
+        <style>
+            .h-bubble {
+                animation: rise 3s infinite;
+            }
+            .o-bubble {
+                animation: rise 4s infinite;
+            }
+            .b1 { animation-delay: 0s; }
+            .b2 { animation-delay: 0.8s; }
+            .b3 { animation-delay: 1.6s; }
+            .b4 { animation-delay: 2.4s; }
+            
+            @keyframes rise {
+                0% { transform: translateY(0); }
+                100% { transform: translateY(-100px); opacity: 0; }
+            }
+        </style>
+    </svg>
+    """
+    
+    st.markdown(electrolysis_svg, unsafe_allow_html=True)
+    
+    # Process Steps
+    st.markdown("## How Our Platform Works", unsafe_allow_html=True)
+    
+    col1, col2, col3, col4 = st.columns(4)
+    
+    with col1:
+        st.markdown("""
+        <div class="process-step">
+            <div class="step-number">1</div>
+            <h4>Input Configuration</h4>
+            <p>Define project parameters including location, capacity, and renewable energy sources.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("""
+        <div class="process-step">
+            <div class="step-number">2</div>
+            <h4>AI Analysis</h4>
+            <p>Our machine learning models analyze your configuration against market variables.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col3:
+        st.markdown("""
+        <div class="process-step">
+            <div class="step-number">3</div>
+            <h4>Optimization</h4>
+            <p>Receive detailed recommendations for optimizing electrolyzer efficiency.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col4:
+        st.markdown("""
+        <div class="process-step">
+            <div class="step-number">4</div>
+            <h4>Scenario Planning</h4>
+            <p>Explore multiple scenarios to identify the most robust configuration.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Call to Action
+    st.markdown("---")
+    st.markdown("""
+    <div style="text-align: center; padding: 30px 0; background: #f5f5f7; border-radius: 10px; margin-top: 30px;">
+        <h2 style="color: #0F9D58; margin-bottom: 20px;">Ready to Transform Your Hydrogen Projects?</h2>
+        <p style="font-size: 1.2rem; margin-bottom: 30px;">Start analyzing your hydrogen project economics now.</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    col1, col2, col3 = st.columns([1,2,1])
+    with col2:
+        if st.button("Launch Hydrogen Analyzer", use_container_width=True):
+            st.session_state.page = "Hydrogen Analyzer"
+            st.experimental_rerun()

readme-md.txt

@@ -0,0 +1,3 @@
+# EcoLytics: Intelligent Hydrogen Economics Platform
+
+EcoLytics is an AI-powered decision support system that transforms green hydrogen project planning through advanced techno-economic analysis. Using machine learning algorithms, it optimizes electrolyzer configurations, predicts ROI scenarios, and visualizes sustainable energy pathways -

requirements-txt.txt

@@ -0,0 +1,6 @@
+streamlit==1.25.0
+pandas==1.5.3
+numpy==1.24.3
+matplotlib==3.7.1
+seaborn==0.12.2
+pillow==9.5.0