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()