import streamlit as st
import joblib
import pandas as pd
from scipy.stats import yeojohnson
import inv_transform



# Title of the app
st.title("Event Budget Estimate")

# Add custom CSS for spacing between columns
st.markdown(
    """
    <style>
    .stColumn > div {
        padding: 10px; /* Adjust the value as needed to increase spacing */
    }
    h2 {
        font-size: 20px;
    }
    </style>
    """, unsafe_allow_html=True)

# Create columns with adjusted spacing
col1, col2 = st.columns([1, 1], gap="large")  # Adjust gap as needed

with col1:
    # Smaller column title
    #st.subheader("Event Data")
    st.markdown("### Event Data")
    
    countries = pd.read_csv('ISO-3166-Countries-with-Regional-Codes.csv')
    countries_a2 = list (countries['alpha-2'])
    countries_names = list (countries['name'])
    countries = [f"{a2} - {name}" for a2, name in zip(countries_a2, countries_names)]
        
    us_index = countries_a2.index('US')
    #cn_index = countries_a2.index('CN')
    in_index = countries_a2.index('IN')
    
    states = pd.read_csv('USA States.csv', index_col=0)
    states = list(states.Abbreviation)
    ca_index = states.index('CA')
    
    # Input fields
    atnd_num = st.number_input("Estimate Number of Attendees", min_value=0, value=180)
    ppr_num = st.number_input("Estimate Paper Number", min_value=0, value=100)
    exh_num = st.number_input("Estimate Number of Exhibits", min_value=0, value=0)
    long = st.number_input("Conference Duration, Days", min_value=0, value=3)
    
    # Dropdown menus
    event_type = st.selectbox("Event Type", ['Colloquium', 'Conference', 'Forum', 'Seminar', 'Symposium', 'Workshop' , 'Other'], index = 1)
    cntry = st.selectbox("Conference Location Country", countries, index = us_index)
    chosen_country_index = countries.index(cntry) 
    if chosen_country_index == us_index:
        loc_state = st.selectbox("Conference Location: State Code", states, index=ca_index)
    else:
        loc_state = "Other"

    # Checkboxes
    st.markdown("<h4>Keywords</h4>", unsafe_allow_html=True)
    kw_comp = st.checkbox("Computer(s) / Computing / Computation / Computational")
    kw_sys = st.checkbox("System(s)")
    kw_app = st.checkbox("Application(s)")
    #kw_ntwk = st.checkbox("Network(s) /Networking")
    kw_wless = st.checkbox("Wireless")
    #kw_mdl = st.checkbox("Model / Modeling")
    #kw_arch = st.checkbox("Architecture(s)")
    kw_img = st.checkbox("Image / Imaging")
#    kw_adv = st.checkbox("Advanced")
#    kw_dist = st.checkbox("Distributed")
    
    st.write("**Please only use the exact keywords listed and avoid including any variations or additional words!**")
submit = st.button("Submit")

# If the submit button is clicked, show output in the second column (col2)
with col2:
    if submit:
        if chosen_country_index != us_index and loc_state != "Other":
            st.error("Correct Event Location: Country and State!")
        else:
            
            try:
                p = True
                regressor = joblib.load("budget_prediction_model.joblib")
            except:
                p = False
              
            if not p:
                st.write('Check the model path')
                st.error("Model doesn't Exist!")
            else:
               
                atnd_num = atnd_num / 1.22
                ppr_num = ppr_num / 1.26
                
                
                data = pd.DataFrame([{
                                        'act_atnd_tot_atnd_num': atnd_num,
                                        'long_atnd_ratio':long/atnd_num, 
                                        'act_paprs_num': ppr_num, 
                                        'longevity': long,
                                        'papr_atnd_ratio':ppr_num/atnd_num, 
                                        'exh_num': exh_num,
                                        'conf_loc_cntry_nm_India': int(chosen_country_index == in_index),
                                        'conf_loc_cntry_nm_USA': int(chosen_country_index == us_index),
                                        'comput': int(kw_comp),
                                        'conf_loc_state_nm_CA': int(loc_state == "CA"),
                                        'system': int(kw_sys),
                                        'conf_evnt_typ_nm_Conference': int(event_type == "Conference"),
                                        'applic': int(kw_app),
                                        'conf_evnt_typ_nm_Workshop': int(event_type == "Workshop"),
                                        'imag': int(kw_img),
                                        'wireless': int(kw_wless), 
                                    
                }])
            
                
                
                lambdas = pd.read_csv('lambdas_yeojohnson.csv', header=None, index_col=0)
                lambdas = lambdas.to_dict()[1]
                
                for n in ['act_atnd_tot_atnd_num', 'exh_num', 'act_paprs_num']:
                    data[n] = yeojohnson(data[n], lambdas[n])
                data.longevity -= 1
        
                # Predict income
                income = regressor.predict(data)[0]
                
                if exh_num > 0:
                    reg_fees_inc = 0.995 * income
                else:
                    reg_fees_inc = income
                 
                income = inv_transform.inv_yeojohnson(income, lambdas['fin_inc_tot_amt'])
                reg_fees_inc = inv_transform.inv_yeojohnson(reg_fees_inc, lambdas['fin_inc_tot_amt'])
                print(reg_fees_inc)
                income = round(income / 1000) * 1000
                reg_fees_inc = round(reg_fees_inc / 1000) * 1000
                
                exh_inc = 0
                sponsor_inc = 0
                
                if exh_num < 10:
                    exh_inc = min(exh_num * 1000, income - reg_fees_inc)     
                elif exh_num < 30:
                    exh_inc = min(10*1000 + (exh_num-10) * 1500, income - reg_fees_inc)
                elif exh_num < 50:
                    exh_inc = min(10*1000 + 20 * 1500 + (exh_num-30) * 3500, income - reg_fees_inc)
                else:
                    exh_inc = min(10*1000 + 20 * 1500 + 20 * 3500  + (exh_num-50) * 2900, income - reg_fees_inc)
                    
                    
                # repeat piecewise function for grants and donations
                if exh_num < 50:
                    sponsor_inc = min(exh_num * 3900, income - reg_fees_inc - exh_inc)
                else:
                    sponsor_inc = min(50 * 3900, income - reg_fees_inc - exh_inc)
                
                sponsor_inc = round(sponsor_inc / 1000) * 1000
                exh_inc = round(exh_inc / 1000) * 1000
                other_inc = income - reg_fees_inc - exh_inc - sponsor_inc
                reg_fees_inc = reg_fees_inc + other_inc
                
                
                expenses =  round(income * 0.85 / 1000) * 1000
                socl_funcs_exp = round(expenses * 0.54 / 1000) * 1000
                local_arr_exp = round(expenses * 0.30 / 1000) * 1000
                admintn_exp = round(expenses * 0.07 / 1000) * 1000
                promo_exp =round(expenses * 0.04 / 1000) * 1000
                audit_exp = min(expenses*0.006, 6000)
                audit_exp = round(audit_exp / 1000) * 1000
                other_exp = expenses - socl_funcs_exp - local_arr_exp - admintn_exp - promo_exp - audit_exp

                # Display results
                st.markdown("### Predicted Budget*")
                st.markdown("<h4>Income Structure</h4>", unsafe_allow_html=True)
                st.write(f"**Total Income: ${income}**")
                st.write(f"Registration Fees Income: ${reg_fees_inc}")
                st.write(f"Exhibit Income: ${exh_inc}")
                st.write(f"Sponsorship Income: ${sponsor_inc}")
                #st.write(f"Other Income: ${other_inc}")
                st.markdown("<h4>Expenses Structure</h4>", unsafe_allow_html=True)
                st.write(f"**Total Expenses: ${round(round(expenses, -3))}**")
                st.write(f"Social Functions Expenses: ${round(round(socl_funcs_exp, -3))}")
                st.write(f"Local Arrangement Expenses: ${round(round(local_arr_exp, -3))}")
                st.write(f"Administration Expenses: ${round(round(admintn_exp, -3))}")
                st.write(f"Promotion Expenses Amount: ${round(round(promo_exp, -3))}")
                st.write(f"Audit Fees Expenses: ${audit_exp}")
                st.write(f"Other Expenses: ${round(round(other_exp, -3))}")
                st.write("")
                st.write("***The numbers are approximate and should be adjusted according to event needs**")
                
                # Add button save those numbers to Excel