import streamlit as st

purchased_goods_values = ["Cement", "Plaster", "Paint", "Timber", "Concrete"]
supplier_values = ["Supplier C", "Supplier D", "Supplier E", "Supplier F", "Supplier G"]
scope_values = ["Electricity", "Natural Gas"]
material_inputs_values = ["Cotton", "Polymer", "Chemical A", "Chemical B"]
transport_values = ["Cotton", "Polymer", "Chemical A", "Chemical B"]
waste_output_values = ["Waste sent to landfill"]

def calculate_emissions_supplier_specific(purchased_goods_data):
    total_emissions = sum([qty * emission_factor for _, _, qty, emission_factor in purchased_goods_data])
    st.header(f"Total Emissions for Supplier-specific Method: {total_emissions} kg CO2e")
    return total_emissions

def calculate_emissions_hybrid(scope1_and_scope2_data, material_inputs_data, transport_data, waste_output_data):
    scope1_and_scope2_emissions = sum([float(item['Amount (kWh)']) * float(item['Emission factor (kg CO2e/kWh)']) for item in scope1_and_scope2_data])
    waste_output_emissions = sum([float(item['Amount (kg)']) * float(item['Emission factor (kg CO2e/kg of waste sent to landfill)']) for item in waste_output_data])
    other_upstream_emissions = sum([float(item['Mass purchased (kg)']) * float(item['Emission factor (kg CO2e/kg)']) for item in material_inputs_data])
    total_emissions = scope1_and_scope2_emissions + waste_output_emissions + other_upstream_emissions

    transport_emissions_per_item = [
        float(item['Distance of transport (km)']) * float(item1['Mass purchased (kg)']) * float(item['Vehicle type emission factor (kg CO2e/kg/km)'])
        for item in transport_data for item1 in material_inputs_data if item["Purchased Goods"] == item1["Purchased Goods"]
    ]

    for i, item in enumerate(transport_data):
        st.header(f"Emissions for Purchased Item {i + 1}: {transport_emissions_per_item[i]} kg CO2e")

    return total_emissions

def calculate_emissions_hybrid_pro(tshirt_data, scope_data, waste_output_data):
    scope1_and_scope2_emissions = sum([float(item['Amount (kWh)']) * float(item['Emission factor (kg CO2e/kWh)']) for item in scope_data])
    waste_output_emissions = sum([float(item['Amount (kg)']) * float(item['Emission factor (kg CO2e/kg of waste sent to landfill)']) for item in waste_output_data])
    other_upstream_emissions = sum([float(item['Number of t-shirts purchased']) * float(item['Cradle-to-gate process emission factor (kg CO2e/per t-shirt(excluding scopes)']) for item in tshirt_data])
    total_emissions = scope1_and_scope2_emissions + waste_output_emissions + other_upstream_emissions
    st.header(f"Total Emissions for HybridPro Method: {total_emissions} kg CO2e")
    return total_emissions



def main():
    st.title("CO2 Emission Calculator")

    method_options = ["Supplier Specific Method", "Hybrid Method", "HybridPro Method"]
    method = st.selectbox("Select Method", method_options)

    if method == "Supplier Specific Method":
        st.header("Supplier Specific Method")
        num_items = st.number_input("Number of items", min_value=1, step=1)
        purchased_goods_data = []
        for i in range(num_items):
            goods = st.selectbox(f"Purchased Goods {i + 1}", purchased_goods_values, key=f"goods_{i}")
            supplier = st.selectbox(f"Supplier {i + 1}", supplier_values, key=f"supplier_{i}")
            qty = st.number_input(f"Qty Purchased (kg) {i + 1}", min_value=0.0, step=0.01, key=f"qty_{i}")
            emission_factor = st.number_input(f"Supplier-specific Emission Factor (kg CO2e/kg) {i + 1}", min_value=0.0, step=0.01, key=f"emission_factor_{i}")
            purchased_goods_data.append((goods, supplier, qty, emission_factor))
        total_emissions = calculate_emissions_supplier_specific(purchased_goods_data)

    elif method == "Hybrid Method":
        st.header("Hybrid Method")
        scope1_and_scope2_data = dynamic_input_fields_with_dropdown("Scope 1 and Scope 2 data from supplier B relating to production of purchased goods", "Enter scope 1 and scope 2 data", scope_values, ["Category","Amount (kWh)", "Emission factor (kg CO2e/kWh)"])
        material_inputs_data = dynamic_input_fields_with_dropdown("Material inputs of purchased goods", "Enter material input data", material_inputs_values, ["Purchased Goods", "Mass purchased (kg)", "Emission factor (kg CO2e/kg)"])
        transport_data = dynamic_input_fields_with_dropdown("Transport of material inputs to supplier B", "Enter transport data", transport_values, ["Purchased Goods", "Distance of transport (km)", "Vehicle type emission factor (kg CO2e/kg/km)"])
        waste_output_data = dynamic_input_fields_with_emission_factor("Waste outputs by supplier B relating to production of purchased goods", "Enter waste output data", waste_output_values, ["Amount (kg)", "Emission factor (kg CO2e/kg of waste sent to landfill)"])
        total_emissions = calculate_emissions_hybrid(scope1_and_scope2_data, material_inputs_data, transport_data, waste_output_data)

    elif method == "HybridPro Method":
        scope_data = dynamic_input_fields_with_dropdown("Scope 1 and Scope 2 data from supplier B", "Enter scope data", scope_values, ["Category","Amount (kWh)", "Emission factor (kg CO2e/kWh)"])
        tshirt_data = dynamic_input_fields_with_emission_factor("T-shirts", "Enter T-shirt data", purchased_goods_values,
                                                                ["Number of t-shirts purchased",
                                                                 "Cradle-to-gate process emission factor (kg CO2e/per t-shirt)","Cradle-to-gate process emission factor (kg CO2e/per t-shirt(excluding scopes)"])
        waste_output_data = dynamic_input_fields_with_emission_factor("Waste outputs by supplier B", "Enter waste output data", waste_output_values,
                                                                      ["Amount (kg)", "Emission factor (kg CO2e/kg of waste sent to landfill)"])
        total_emissions = calculate_emissions_hybrid_pro(tshirt_data, scope_data, waste_output_data)

def dynamic_input_fields(label, values, headings):
    num_items = st.number_input(f"**Number of {label} items**", min_value=1, step=1, key=f"{label}_num_items")
    input_fields = []
    for i in range(num_items):
        st.subheader(f"{label} {i + 1}")
        input_data = {}
        for value, heading in zip(values, headings):
            input_data[value] = st.number_input(f"{heading} {i + 1}", min_value=0, step=0.01, key=f"{label}_{i}_{value}")
        input_fields.append(input_data)
    return input_fields

def dynamic_input_fields_with_dropdown(label, prompt, values, headings):
    num_items = st.number_input(f"**Number of {label} items**", min_value=1, step=1, key=f"{label}_num_items")
    input_fields = []
    for i in range(num_items):
        st.subheader(f"{label} {i + 1}")
        input_data = {}
        input_data[headings[0]] = st.selectbox(f"{headings[0]} {i + 1}", values, key=f"{label}_{i}_{headings[0]}")
        for heading in headings[1:]:
            input_data[heading] = st.number_input(f"{heading} {i + 1}", min_value=0.0, step=0.01, key=f"{label}_{i}_{heading}")
        input_fields.append(input_data)
    return input_fields

def dynamic_input_fields_with_emission_factor(label, prompt, values, headings):
    num_items = st.number_input(f"**Number of {label} items**", min_value=1, step=1, key=f"{label}_num_items")
    input_fields = []
    for i in range(num_items):
        st.subheader(f"{label} {i + 1}")
        input_data = {}
        for heading in headings:
            input_data[heading] = st.number_input(f"{heading} {i + 1}", min_value=0.0, step=0.01, key=f"{label}_{i}_{heading}")
        input_fields.append(input_data)
    return input_fields

if __name__ == "__main__":
    main()