pages/2_Scenario_Planner.py

import streamlit as st
from numerize.numerize import numerize
import numpy as np
from functools import partial
from collections import OrderedDict
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from utilities import (
    format_numbers,format_numbers_f,
    load_local_css,
    set_header,
    initialize_data,
    load_authenticator,
    send_email,
    channel_name_formating,
)
from classes import class_from_dict, class_to_dict
import pickle
import streamlit_authenticator as stauth
import yaml
from yaml import SafeLoader
import re
import pandas as pd
import plotly.express as px
import response_curves_model_quality as rc

st.set_page_config(layout="wide")
load_local_css("styles.css")
set_header()


for k, v in st.session_state.items():
    if k not in ["logout", "login", "config"] and not k.startswith("FormSubmitter"):
        st.session_state[k] = v
# ======================================================== #
# ======================= Functions ====================== #
# ======================================================== #


def optimize(key, status_placeholder):
    """
    Optimize the spends for the sales
    """

    channel_list = [
        key for key, value in st.session_state["optimization_channels"].items() if value
    ]

    if len(channel_list) > 0:
        scenario = st.session_state["scenario"]
        if key.lower() == "media spends":
            with status_placeholder:
                with st.spinner("Optimizing"):
                    result = st.session_state["scenario"].optimize(
                        st.session_state["total_spends_change"], channel_list
                    )
        # elif key.lower() == "revenue":
        else:
            with status_placeholder:
                with st.spinner("Optimizing"):

                    result = st.session_state["scenario"].optimize_spends(
                        st.session_state["total_sales_change"], channel_list
                    )
        for channel_name, modified_spends in result:

            st.session_state[channel_name] = numerize(
                modified_spends * scenario.channels[channel_name].conversion_rate,
                1,
            )
            prev_spends = (
                st.session_state["scenario"].channels[channel_name].actual_total_spends
            )
            st.session_state[f"{channel_name}_change"] = round(
                100 * (modified_spends - prev_spends) / prev_spends, 2
            )


def save_scenario(scenario_name):
    """
    Save the current scenario with the mentioned name in the session state

    Parameters
    ----------
    scenario_name
        Name of the scenario to be saved
    """
    if "saved_scenarios" not in st.session_state:
        st.session_state = OrderedDict()

    # st.session_state['saved_scenarios'][scenario_name] = st.session_state['scenario'].save()
    st.session_state["saved_scenarios"][scenario_name] = class_to_dict(
        st.session_state["scenario"]
    )
    st.session_state["scenario_input"] = ""
    # print(type(st.session_state['saved_scenarios']))
    with open("../saved_scenarios.pkl", "wb") as f:
        pickle.dump(st.session_state["saved_scenarios"], f)


if "allow_spends_update" not in st.session_state:
    st.session_state["allow_spends_update"] = True

if "allow_sales_update" not in st.session_state:
    st.session_state["allow_sales_update"] = True


def update_sales_abs_slider():
    actual_sales = _scenario.actual_total_sales
    if validate_input(st.session_state["total_sales_change_abs_slider"]):
        modified_sales = extract_number_for_string(
            st.session_state["total_sales_change_abs_slider"]
        )
        st.session_state["total_sales_change"] = round(
            ((modified_sales / actual_sales) - 1) * 100
        )
        st.session_state["total_sales_change_abs"] = numerize(modified_sales, 1)


def update_sales_abs():
    if (
        st.session_state["total_sales_change_abs"]
        in st.session_state["total_sales_change_abs_slider_options"]
    ):
        st.session_state["allow_sales_update"] = True
    else:
        st.session_state["allow_sales_update"] = False

    actual_sales = _scenario.actual_total_sales
    if (
        validate_input(st.session_state["total_sales_change_abs"])
        and st.session_state["allow_sales_update"]
    ):
        modified_sales = extract_number_for_string(
            st.session_state["total_sales_change_abs"]
        )
        st.session_state["total_sales_change"] = round(
            ((modified_sales / actual_sales) - 1) * 100
        )
        st.session_state["total_sales_change_abs_slider"] = numerize(modified_sales, 1)


def update_sales():
    st.session_state["total_sales_change_abs"] = numerize(
        (1 + st.session_state["total_sales_change"] / 100)
        * _scenario.actual_total_sales,
        1,
    )
    st.session_state["total_sales_change_abs_slider"] = numerize(
        (1 + st.session_state["total_sales_change"] / 100)
        * _scenario.actual_total_sales,
        1,
    )


def update_all_spends_abs_slider():
    actual_spends = _scenario.actual_total_spends
    if validate_input(st.session_state["total_spends_change_abs_slider"]):
        modified_spends = extract_number_for_string(
            st.session_state["total_spends_change_abs_slider"]
        )
        st.session_state["total_spends_change"] = round(
            ((modified_spends / actual_spends) - 1) * 100
        )
        st.session_state["total_spends_change_abs"] = numerize(modified_spends, 1)

        update_all_spends()


# def update_all_spends_abs_slider():
#     actual_spends = _scenario.actual_total_spends
#     if validate_input(st.session_state["total_spends_change_abs_slider"]):
#         print("#" * 100)
#         print(st.session_state["total_spends_change_abs_slider"])C:\Users\PragyaJatav\Downloads\Untitled Folder 2\simulatorAldi\pages\8_Scenario_Planner.py
#         print("#" * 100)

#         modified_spends = extract_number_for_string(
#             st.session_state["total_spends_change_abs_slider"]
#         )
#         st.session_state["total_spends_change"] = (
#             (modified_spends / actual_spends) - 1
#         ) * 100
#         st.session_state["total_spends_change_abs"] = st.session_state[
#             "total_spends_change_abs_slider"
#         ]

#         update_all_spends()


def update_all_spends_abs():
    if (
        st.session_state["total_spends_change_abs"]
        in st.session_state["total_spends_change_abs_slider_options"]
    ):
        st.session_state["allow_spends_update"] = True
    else:
        st.session_state["allow_spends_update"] = False

    actual_spends = _scenario.actual_total_spends
    if (
        validate_input(st.session_state["total_spends_change_abs"])
        and st.session_state["allow_spends_update"]
    ):
        modified_spends = extract_number_for_string(
            st.session_state["total_spends_change_abs"]
        )
        st.session_state["total_spends_change"] = (
            (modified_spends / actual_spends) - 1
        ) * 100
        st.session_state["total_spends_change_abs_slider"] = st.session_state[
            "total_spends_change_abs"
        ]

        update_all_spends()


def update_spends():
    st.session_state["total_spends_change_abs"] = numerize(
        (1 + st.session_state["total_spends_change"] / 100)
        * _scenario.actual_total_spends,
        1,
    )
    st.session_state["total_spends_change_abs_slider"] = numerize(
        (1 + st.session_state["total_spends_change"] / 100)
        * _scenario.actual_total_spends,
        1,
    )

    update_all_spends()


def update_all_spends():
    """
    Updates spends for all the channels with the given overall spends change
    """
    percent_change = st.session_state["total_spends_change"]

    for channel_name in st.session_state["channels_list"]:
        channel = st.session_state["scenario"].channels[channel_name]
        current_spends = channel.actual_total_spends
        modified_spends = (1 + percent_change / 100) * current_spends
        st.session_state["scenario"].update(channel_name, modified_spends)
        st.session_state[channel_name] = numerize(
            modified_spends * channel.conversion_rate, 1
        )
        st.session_state[f"{channel_name}_change"] = percent_change


def extract_number_for_string(string_input):
    string_input = string_input.upper()
    if string_input.endswith("K"):
        return float(string_input[:-1]) * 10**3
    elif string_input.endswith("M"):
        return float(string_input[:-1]) * 10**6
    elif string_input.endswith("B"):
        return float(string_input[:-1]) * 10**9


def validate_input(string_input):
    pattern = r"\d+\.?\d*[K|M|B]$"
    match = re.match(pattern, string_input)
    if match is None:
        return False
    return True


def update_data_by_percent(channel_name):
    prev_spends = (
        st.session_state["scenario"].channels[channel_name].actual_total_spends
        * st.session_state["scenario"].channels[channel_name].conversion_rate
    )
    modified_spends = prev_spends * (
        1 + st.session_state[f"{channel_name}_change"] / 100
    )
    st.session_state[channel_name] = numerize(modified_spends, 1)
    st.session_state["scenario"].update(
        channel_name,
        modified_spends
        / st.session_state["scenario"].channels[channel_name].conversion_rate,
    )


def update_data(channel_name):
    """
    Updates the spends for the given channel
    """

    if validate_input(st.session_state[channel_name]):
        modified_spends = extract_number_for_string(st.session_state[channel_name])
        prev_spends = (
            st.session_state["scenario"].channels[channel_name].actual_total_spends
            * st.session_state["scenario"].channels[channel_name].conversion_rate
        )
        st.session_state[f"{channel_name}_change"] = round(
            100 * (modified_spends - prev_spends) / prev_spends, 2
        )
        st.session_state["scenario"].update(
            channel_name,
            modified_spends
            / st.session_state["scenario"].channels[channel_name].conversion_rate,
        )
    # st.session_state['scenario'].update(channel_name, modified_spends)
    # else:
    #     try:
    #         modified_spends = float(st.session_state[channel_name])
    #         prev_spends = st.session_state['scenario'].channels[channel_name].actual_total_spends * st.session_state['scenario'].channels[channel_name].conversion_rate
    #         st.session_state[f'{channel_name}_change'] = round(100*(modified_spends - prev_spends) / prev_spends,2)
    #         st.session_state['scenario'].update(channel_name, modified_spends/st.session_state['scenario'].channels[channel_name].conversion_rate)
    #         st.session_state[f'{channel_name}'] = numerize(modified_spends,1)
    #     except ValueError:
    #         st.write('Invalid input')


def select_channel_for_optimization(channel_name):
    """
    Marks the given channel for optimization
    """
    st.session_state["optimization_channels"][channel_name] = st.session_state[
        f"{channel_name}_selected"
    ]


def select_all_channels_for_optimization():
    """
    Marks all the channel for optimization
    """
    for channel_name in st.session_state["optimization_channels"].keys():
        st.session_state[f"{channel_name}_selected"] = st.session_state[
            "optimze_all_channels"
        ]
        st.session_state["optimization_channels"][channel_name] = st.session_state[
            "optimze_all_channels"
        ]


def update_penalty():
    """
    Updates the penalty flag for sales calculation
    """
    st.session_state["scenario"].update_penalty(st.session_state["apply_penalty"])


def reset_scenario(panel_selected, file_selected, updated_rcs):
    # #print(st.session_state['default_scenario_dict'])
    # st.session_state['scenario']  = class_from_dict(st.session_state['default_scenario_dict'])
    # for channel in st.session_state['scenario'].channels.values():
    #     st.session_state[channel.name] = float(channel.actual_total_spends * channel.conversion_rate)
    # initialize_data()

    if panel_selected == "Total Market":
        initialize_data(
            panel=panel_selected,
            target_file=file_selected,
            updated_rcs=updated_rcs,
            metrics=metrics_selected,
        )
        panel = None
    else:
        initialize_data(
            panel=panel_selected,
            target_file=file_selected,
            updated_rcs=updated_rcs,
            metrics=metrics_selected,
        )

    for channel_name in st.session_state["channels_list"]:
        st.session_state[f"{channel_name}_selected"] = False
        st.session_state[f"{channel_name}_change"] = 0
    st.session_state["optimze_all_channels"] = False

    st.session_state["total_sales_change"] = 0

    update_spends()
    update_sales()

    reset_inputs()

    # st.rerun()


def format_number(num):
    if num >= 1_000_000:
        return f"{num / 1_000_000:.2f}M"
    elif num >= 1_000:
        return f"{num / 1_000:.0f}K"
    else:
        return f"{num:.2f}"


def summary_plot(data, x, y, title, text_column):
    fig = px.bar(
        data,
        x=x,
        y=y,
        orientation="h",
        title=title,
        text=text_column,
        color="Channel_name",
    )

    # Convert text_column to numeric values
    data[text_column] = pd.to_numeric(data[text_column], errors="coerce")

    # Update the format of the displayed text based on magnitude
    fig.update_traces(
        texttemplate="%{text:.2s}",
        textposition="outside",
        hovertemplate="%{x:.2s}",
    )

    fig.update_layout(xaxis_title=x, yaxis_title="Channel Name", showlegend=False)
    return fig


def s_curve(x, K, b, a, x0):
    return K / (1 + b * np.exp(-a * (x - x0)))


def find_segment_value(x, roi, mroi):
    start_value = x[0]
    end_value = x[len(x) - 1]

    # Condition for green region: Both MROI and ROI > 1
    green_condition = (roi > 1) & (mroi > 1)
    left_indices = np.where(green_condition)[0]
    left_value = x[left_indices[0]] if left_indices.size > 0 else x[0]

    right_indices = np.where(green_condition)[0]
    right_value = x[right_indices[-1]] if right_indices.size > 0 else x[0]

    return start_value, end_value, left_value, right_value


def calculate_rgba(
    start_value, end_value, left_value, right_value, current_channel_spends
):
    # Initialize alpha to None for clarity
    alpha = None

    # Determine the color and calculate relative_position and alpha based on the point's position
    if start_value <= current_channel_spends <= left_value:
        color = "yellow"
        relative_position = (current_channel_spends - start_value) / (
            left_value - start_value
        )
        alpha = 0.8 - (0.6 * relative_position)  # Alpha decreases from start to end

    elif left_value < current_channel_spends <= right_value:
        color = "green"
        relative_position = (current_channel_spends - left_value) / (
            right_value - left_value
        )
        alpha = 0.8 - (0.6 * relative_position)  # Alpha decreases from start to end

    elif right_value < current_channel_spends <= end_value:
        color = "red"
        relative_position = (current_channel_spends - right_value) / (
            end_value - right_value
        )
        alpha = 0.2 + (0.6 * relative_position)  # Alpha increases from start to end

    else:
        # Default case, if the spends are outside the defined ranges
        return "rgba(136, 136, 136, 0.5)"  # Grey for values outside the range

    # Ensure alpha is within the intended range in case of any calculation overshoot
    alpha = max(0.2, min(alpha, 0.8))

    # Define color codes for RGBA
    color_codes = {
        "yellow": "255, 255, 0",  # RGB for yellow
        "green": "0, 128, 0",  # RGB for green
        "red": "255, 0, 0",  # RGB for red
    }

    rgba = f"rgba({color_codes[color]}, {alpha})"
    return rgba


def debug_temp(x_test, power, K, b, a, x0):
    print("*" * 100)
    # Calculate the count of bins
    count_lower_bin = sum(1 for x in x_test if x <= 2524)
    count_center_bin = sum(1 for x in x_test if x > 2524 and x <= 3377)
    count_ = sum(1 for x in x_test if x > 3377)

    print(
        f"""
            lower : {count_lower_bin}
            center : {count_center_bin}
            upper : {count_}
          """
    )


# @st.cache
def plot_response_curves(summary_df_sorted):
    # rows = (
    #     len(channels_list) // cols
    #     if len(channels_list) % cols == 0
    #     else len(channels_list) // cols + 1
    # )
    # rcs = st.session_state["rcs"]
    # shapes = []
    # fig = make_subplots(rows=rows, cols=cols, subplot_titles=channels_list)
    channel_cols = [
    'BroadcastTV',
    'CableTV',
    'Connected&OTTTV',
    'DisplayProspecting',
    'DisplayRetargeting',
        'Video',
    'SocialProspecting',
    'SocialRetargeting',
    'SearchBrand',
    'SearchNon-brand',
    'DigitalPartners',
    'Audio',
    'Email']
    summary_df_sorted.index = summary_df_sorted["Channel_name"]
    figures = [rc.response_curves(channels_list[i], summary_df_sorted["Optimized_spend"][channels_list[i]]/104, summary_df_sorted["New_sales"][channels_list[i]]/104) for i in range(13)]
    
    # Display figures in a grid layout
    cols = st.columns(3)  # 4 columns for the grid

    for idx, fig in enumerate(figures):
        col = cols[idx % 3]
        with col:
            st.plotly_chart(fig, use_container_width=True)

    # cols = st.columns(3)
    # for i in range(0, len(channels_list)):

    #     col = channels_list[i]
    #     if col == "Panel":
    #         continue
    #     st.write(col)
    #     x_modified = summary_df_sorted["Optimized_spend"][col]/104
    #     y_modified = summary_df_sorted["New_sales"][col]/104
    #     st.plotly_chart(rc.response_curves(col,x_modified,y_modified))
        

# @st.cache
# def plot_response_curves():
#     cols = 4
#     rcs = st.session_state["rcs"]
#     shapes = []
#     fig = make_subplots(rows=6, cols=cols, subplot_titles=channels_list)
#     for i in range(0, len(channels_list)):
#         col = channels_list[i]
#         x = st.session_state["actual_df"][col].values
#         spends = x.sum()
#         power = np.ceil(np.log(x.max()) / np.log(10)) - 3
#         x = np.linspace(0, 3 * x.max(), 200)

#         K = rcs[col]["K"]
#         b = rcs[col]["b"]
#         a = rcs[col]["a"]
#         x0 = rcs[col]["x0"]

#         y = s_curve(x / 10**power, K, b, a, x0)
#         roi = y / x
#         marginal_roi = a * (y) * (1 - y / K)
#         fig.add_trace(
#             go.Scatter(
#                 x=52
#                 * x
#                 * st.session_state["scenario"].channels[col].conversion_rate,
#                 y=52 * y,
#                 name=col,
#                 customdata=np.stack((roi, marginal_roi), axis=-1),
#                 hovertemplate="Spend:%{x:$.2s}<br>Sale:%{y:$.2s}<br>ROI:%{customdata[0]:.3f}<br>MROI:%{customdata[1]:.3f}",
#             ),
#             row=1 + (i) // cols,
#             col=i % cols + 1,
#         )

#         fig.add_trace(
#             go.Scatter(
#                 x=[
#                     spends
#                     * st.session_state["scenario"]
#                     .channels[col]
#                     .conversion_rate
#                 ],
#                 y=[52 * s_curve(spends / (10**power * 52), K, b, a, x0)],
#                 name=col,
#                 legendgroup=col,
#                 showlegend=False,
#                 marker=dict(color=["black"]),
#             ),
#             row=1 + (i) // cols,
#             col=i % cols + 1,
#         )

#         shapes.append(
#             go.layout.Shape(
#                 type="line",
#                 x0=0,
#                 y0=52 * s_curve(spends / (10**power * 52), K, b, a, x0),
#                 x1=spends
#                 * st.session_state["scenario"].channels[col].conversion_rate,
#                 y1=52 * s_curve(spends / (10**power * 52), K, b, a, x0),
#                 line_width=1,
#                 line_dash="dash",
#                 line_color="black",
#                 xref=f"x{i+1}",
#                 yref=f"y{i+1}",
#             )
#         )

#         shapes.append(
#             go.layout.Shape(
#                 type="line",
#                 x0=spends
#                 * st.session_state["scenario"].channels[col].conversion_rate,
#                 y0=0,
#                 x1=spends
#                 * st.session_state["scenario"].channels[col].conversion_rate,
#                 y1=52 * s_curve(spends / (10**power * 52), K, b, a, x0),
#                 line_width=1,
#                 line_dash="dash",
#                 line_color="black",
#                 xref=f"x{i+1}",
#                 yref=f"y{i+1}",
#             )
#         )

#     fig.update_layout(
#         height=1500,
#         width=1000,
#         title_text="Response Curves",
#         showlegend=False,
#         shapes=shapes,
#     )
#     fig.update_annotations(font_size=10)
#     fig.update_xaxes(title="Spends")
#     fig.update_yaxes(title=target)
#     return fig


# ======================================================== #
# ==================== HTML Components =================== #
# ======================================================== #


def generate_spending_header(heading):
    return st.markdown(
        f"""<h2 class="spends-header">{heading}</h2>""", unsafe_allow_html=True
    )


# ======================================================== #
# =================== Session variables ================== #
# ======================================================== #

with open("config.yaml") as file:
    config = yaml.load(file, Loader=SafeLoader)
    st.session_state["config"] = config

authenticator = stauth.Authenticate(
    config["credentials"],
    config["cookie"]["name"],
    config["cookie"]["key"],
    config["cookie"]["expiry_days"],
    config["preauthorized"],
)
st.session_state["authenticator"] = authenticator
name, authentication_status, username = authenticator.login("Login", "main")
auth_status = st.session_state.get("authentication_status")

import os
import glob


def get_excel_names(directory):
    # Create a list to hold the final parts of the filenames
    last_portions = []

    # Patterns to match Excel files (.xlsx and .xls) that contain @#
    patterns = [
        os.path.join(directory, "*@#*.xlsx"),
        os.path.join(directory, "*@#*.xls"),
    ]

    # Process each pattern
    for pattern in patterns:
        files = glob.glob(pattern)

        # Extracting the last portion after @# for each file
        for file in files:
            base_name = os.path.basename(file)
            last_portion = base_name.split("@#")[-1]
            last_portion = last_portion.replace(".xlsx", "").replace(
                ".xls", ""
            )  # Removing extensions
            last_portions.append(last_portion)

    return last_portions


def name_formating(channel_name):
    # Replace underscores with spaces
    name_mod = channel_name.replace("_", " ")

    # Capitalize the first letter of each word
    name_mod = name_mod.title()

    return name_mod


@st.cache_data(show_spinner=False)
def panel_fetch(file_selected):
    raw_data_mmm_df = pd.read_excel(file_selected, sheet_name="RAW DATA MMM")

    # if "Panel" in raw_data_mmm_df.columns:
    #     panel = list(set(raw_data_mmm_df["Panel"]))
    # else:
    #     raw_data_mmm_df = None
    #     panel = None
    # raw_data_mmm_df = None
    panel = None
    return panel


def reset_inputs():
    if "total_spends_change_abs" in st.session_state:
        del st.session_state.total_spends_change_abs
    if "total_spends_change" in st.session_state:
        del st.session_state.total_spends_change
    if "total_spends_change_abs_slider" in st.session_state:
        del st.session_state.total_spends_change_abs_slider

    if "total_sales_change_abs" in st.session_state:
        del st.session_state.total_sales_change_abs
    if "total_sales_change" in st.session_state:
        del st.session_state.total_sales_change
    if "total_sales_change_abs_slider" in st.session_state:
        del st.session_state.total_sales_change_abs_slider

    st.session_state["initialized"] = False


if auth_status == True:
    authenticator.logout("Logout", "main")
    st.header("Scenario Planner")
    def scenario_planner_plots():
        
        with st.expander('Optimized Spends Overview'):
            # if st.button('Refresh'):
            #     st.experimental_rerun()

            import plotly.graph_objects as go
            from plotly.subplots import make_subplots

            # Define light colors for bars
            import plotly.graph_objects as go
            from plotly.subplots import make_subplots

            st.empty()
            #st.header('Model Result Analysis')
            spends_data=pd.read_excel('Overview_data_test.xlsx')

            with open('summary_df.pkl', 'rb') as file:
                summary_df_sorted = pickle.load(file)
            #st.write(summary_df_sorted)

            # selected_scenario= st.selectbox('Select Saved Scenarios',['S1','S2']) 
            summary_df_sorted=summary_df_sorted.sort_values(by=['Optimized_spend'],ascending=False)
            summary_df_sorted['old_efficiency']=(summary_df_sorted['Old_sales']/summary_df_sorted['Old_sales'].sum())/(summary_df_sorted['Actual_spend']/summary_df_sorted['Actual_spend'].sum())
            summary_df_sorted['new_efficiency']=(summary_df_sorted['New_sales']/summary_df_sorted['New_sales'].sum())/(summary_df_sorted['Optimized_spend']/summary_df_sorted['Optimized_spend'].sum())

            summary_df_sorted['old_roi']=summary_df_sorted['Old_sales']/summary_df_sorted['Actual_spend']
            summary_df_sorted['new_roi']=summary_df_sorted['New_sales']/summary_df_sorted['Optimized_spend']

            total_actual_spend = summary_df_sorted['Actual_spend'].sum()
            total_optimized_spend = summary_df_sorted['Optimized_spend'].sum()

            actual_spend_percentage = (summary_df_sorted['Actual_spend'] / total_actual_spend) * 100
            optimized_spend_percentage = (summary_df_sorted['Optimized_spend'] / total_optimized_spend) * 100



            light_blue = 'rgba(0, 31, 120, 0.7)'
            light_orange = 'rgba(0, 181, 219, 0.7)'
            light_green = 'rgba(240, 61, 20, 0.7)'
            light_red = 'rgba(250, 110, 10, 0.7)'
            light_purple = 'rgba(255, 191, 69, 0.7)'


            # # Create subplots with one row and two columns
            # fig = make_subplots(rows=3, cols=1, subplot_titles=("Actual vs. Optimized Spend", "Actual vs. Optimized Contribution", "Actual vs. Optimized ROI"))

            # # Add actual vs optimized spend bars

            
            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['Actual_spend'], name='Actual',
            #                      text=summary_df_sorted['Actual_spend'].apply(format_number) + ' '+' (' + actual_spend_percentage.round(2).astype(str) + '%)',
            #                        marker_color=light_blue, orientation='h'), 
            #                      row=1,
            #                        col=1)
            
            
            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['Optimized_spend'], name='Optimized',
            #                      text=summary_df_sorted['Optimized_spend'].apply(format_number) + ' (' + optimized_spend_percentage.round(2).astype(str) + '%)',
            #                        marker_color=light_orange, 
            #                        orientation='h'),
            #                          row=1, 
            #                          col=1)
            
            # fig.update_xaxes(title_text="Amount", row=1, col=1)

            # # Add actual vs optimized Contribution
            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['New_sales'],
            #                       name='Optimized Contribution',text=summary_df_sorted['New_sales'].apply(format_number), 
            #                       marker_color=light_orange, orientation='h',showlegend=False), row=2, col=1)
            
            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['Old_sales'], 
            #                      name='Actual Contribution',text=summary_df_sorted['Old_sales'].apply(format_number), 
            #                      marker_color=light_blue, orientation='h',showlegend=False), row=2, col=1)
            
    
            # fig.update_xaxes(title_text="Contribution", row=2, col=1) 

            # # Add actual vs optimized ROI bars
                    
            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['new_roi'], 
            #                      name='Optimized ROI',text=summary_df_sorted['new_roi'].apply(format_number) , 
            #                      marker_color=light_orange, orientation='h',showlegend=False), row=3, col=1)
            
            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['old_roi'], 
            #                      name='Actual ROI', text=summary_df_sorted['old_roi'].apply(format_number) ,
            #                        marker_color=light_blue, orientation='h',showlegend=False), row=3, col=1)

            # fig.update_xaxes(title_text="ROI", row=3, col=1)

            # # Update layout
            # fig.update_layout(title_text="Actual vs. Optimized Metrics for Media Channels",
            #                    showlegend=True, yaxis=dict(title='Media Channels', autorange="reversed"))

            # st.plotly_chart(fig,use_container_width=True)

            # Create subplots with one row and two columns
            fig = go.Figure()
            # Add actual vs optimized spend bars

            
            fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['Actual_spend'], name='Actual',
                                text=summary_df_sorted['Actual_spend'].apply(format_number) + ' '
                                #  + 
                                #  ' '+
                                # '</br> (' + actual_spend_percentage.astype(int).astype(str) + '%)'
                                ,textposition='outside',#textfont=dict(size=30), 
                                marker_color=light_blue))
            
            
            fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['Optimized_spend'], name='Optimized',
                                text=summary_df_sorted['Optimized_spend'].apply(format_number) + ' '
                                #    + 
                                #  '</br> (' + optimized_spend_percentage.astype(int).astype(str) + '%)'
                                ,textposition='outside',#textfont=dict(size=30), 
                                marker_color=light_orange))
            
            fig.update_xaxes(title_text="Channels")
            fig.update_yaxes(title_text="Spends ($)")
            fig.update_layout(
                title = "Actual vs. Optimized Spends",
                        margin=dict(t=40, b=40, l=40, r=40)
                    )
            
            st.plotly_chart(fig,use_container_width=True)

            # Add actual vs optimized Contribution
            fig = go.Figure()
            fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['Old_sales'], 
                                name='Actual Contribution',text=summary_df_sorted['Old_sales'].apply(format_number),textposition='outside', 
                                marker_color=light_blue,showlegend=True))
            
            fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['New_sales'],
                                name='Optimized Contribution',text=summary_df_sorted['New_sales'].apply(format_number),textposition='outside', 
                                marker_color=light_orange, showlegend=True))
            
            
    
            fig.update_yaxes(title_text="Contribution")
            fig.update_xaxes(title_text="Channels") 
            fig.update_layout(
                title = "Actual vs. Optimized Contributions",
                        margin=dict(t=40, b=40, l=40, r=40)
                        # yaxis=dict(range=[0, 0.002]),
                    )
            st.plotly_chart(fig,use_container_width=True)

            # Add actual vs optimized Efficiency bars
            fig = go.Figure()
            summary_df_sorted_p =  summary_df_sorted[summary_df_sorted['Channel_name']!="Panel"]
            fig.add_trace(go.Bar(x=summary_df_sorted_p['Channel_name'], y=summary_df_sorted_p['old_efficiency'], 
                                name='Actual Efficiency', text=summary_df_sorted_p['old_efficiency'].apply(format_number) ,textposition='outside',
                                marker_color=light_blue,showlegend=True))
            fig.add_trace(go.Bar(x=summary_df_sorted_p['Channel_name'], y=summary_df_sorted_p['new_efficiency'], 
                                name='Optimized Efficiency',text=summary_df_sorted_p['new_efficiency'].apply(format_number),textposition='outside' , 
                                marker_color=light_orange,showlegend=True))
            
            fig.update_xaxes(title_text="Channels")
            fig.update_yaxes(title_text="ROI")
            fig.update_layout(
                title = "Actual vs. Optimized ROI",
                        margin=dict(t=40, b=40, l=40, r=40),
                        # yaxis=dict(range=[0, 0.002]),
                    )
                
            st.plotly_chart(fig,use_container_width=True)


    # Response Metrics
    directory = "metrics_level_data"
    metrics_list = get_excel_names(directory)
    
    # metrics_selected = col1.selectbox(
    #     "Response Metrics",
    #     metrics_list,
    #     format_func=name_formating,
    #     index=0,
    #     on_change=reset_inputs,
    # )

    metrics_selected='prospects'
    # Target
    target = name_formating(metrics_selected)

    file_selected = (
        f"Overview_data_test_panel@#{metrics_selected}.xlsx"
    )

    # Panel List
    panel_list = panel_fetch(file_selected)

    # # Panel Selected
    # panel_selected = st.selectbox(
    #     "Markets",
    #     ["Total Market"] + panel_list,
    #     index=0,
    #     on_change=reset_inputs,
    # )
    
    # st.write(panel_selected)
    panel_selected = "Total Market"
    st.session_state['selected_markets']=panel_selected

    if "update_rcs" in st.session_state:
        updated_rcs = st.session_state["update_rcs"]
    else:
        updated_rcs = None

    if "first_time" not in st.session_state:
        st.session_state["first_time"] = True

    # Check if state is initiaized
    is_state_initiaized = st.session_state.get("initialized", False)
    if not is_state_initiaized or st.session_state["first_time"]:
        # initialize_data()
        if panel_selected == "Total Market":
            initialize_data(
                panel=panel_selected,
                target_file=file_selected,
                updated_rcs=updated_rcs,
                metrics=metrics_selected,
            )
            panel = None
        else:
            initialize_data(
                panel=panel_selected,
                target_file=file_selected,
                updated_rcs=updated_rcs,
                metrics=metrics_selected,
            )
        st.session_state["initialized"] = True
        st.session_state["first_time"] = False

    # initialize_data(
    #             panel=panel_selected,
    #             target_file=file_selected,
    #             updated_rcs=updated_rcs,
    #             metrics=metrics_selected,
    #         )
    # st.session_state["initialized"] = True
    # st.session_state["first_time"] = False

    # Channels List
    channels_list = st.session_state["channels_list"]

    # ======================================================== #
    # ========================== UI ========================== #
    # ======================================================== #

    # print(list(st.session_state.keys()))
    main_header = st.columns((2, 2))
    sub_header = st.columns((1, 1, 1, 1))
    _scenario = st.session_state["scenario"]

    if "total_spends_change" not in st.session_state:
        st.session_state.total_spends_change = 0

    if "total_sales_change" not in st.session_state:
        st.session_state.total_sales_change = 0

    if "total_spends_change_abs" not in st.session_state:
        st.session_state["total_spends_change_abs"] = numerize(
            _scenario.actual_total_spends, 1
        )

    if "total_sales_change_abs" not in st.session_state:
        st.session_state["total_sales_change_abs"] = numerize(
            _scenario.actual_total_sales, 1
        )

    if "total_spends_change_abs_slider" not in st.session_state:
        st.session_state.total_spends_change_abs_slider = numerize(
            _scenario.actual_total_spends, 1
        )

    if "total_sales_change_abs_slider" not in st.session_state:
        st.session_state.total_sales_change_abs_slider = numerize(
            _scenario.actual_total_sales, 1
        )

    with main_header[0]:
        st.subheader("Actual")

    with main_header[-1]:
        st.subheader("Simulated")

    with sub_header[0]:
        st.metric(label="Spends", value=format_numbers(_scenario.actual_total_spends))

    with sub_header[1]:
        st.metric(
            label=target,
            value=format_numbers_f(
                float(_scenario.actual_total_sales)
            ),
        )

    with sub_header[2]:
        st.metric(
            label="Spends",
            value=format_numbers(_scenario.modified_total_spends),
            delta=numerize(_scenario.delta_spends, 1),
        )

    with sub_header[3]:
        st.metric(
            label=target,
            value=format_numbers_f(
                float(_scenario.modified_total_sales)
            ),
            delta=numerize(_scenario.delta_sales, 1),
        )

    with st.expander("Channel Spends Simulator", expanded=True):
        _columns1 = st.columns((2, 2, 1, 1))
        with _columns1[0]:
            optimization_selection = st.selectbox(
                "Optimize", options=["Media Spends", target], key="optimization_key"
            )

        with _columns1[1]:
            st.markdown("#")
            # if st.checkbox(
            #     label="Optimize all Channels",
            #     key="optimze_all_channels",
            #     value=False,
            #     # on_change=select_all_channels_for_optimization,
            # ):
            #     select_all_channels_for_optimization()

            st.checkbox(
                label="Optimize all Channels",
                key="optimze_all_channels",
                value=False,
                on_change=select_all_channels_for_optimization,
            )

        with _columns1[2]:
            st.markdown("#")
            # st.button(
            #     "Optimize",
            #     on_click=optimize,
            #     args=(st.session_state["optimization_key"]),
            #     use_container_width=True,
            # )

            optimize_placeholder = st.empty()

        with _columns1[3]:
            st.markdown("#")
            st.button(
                "Reset",
                on_click=reset_scenario,
                args=(panel_selected, file_selected, updated_rcs),
                # use_container_width=True,
            )
            # st.write(target)


        _columns2 = st.columns((2, 2, 2))
        if st.session_state["optimization_key"] == "Media Spends":
            with _columns2[0]:
                spend_input = st.text_input(
                    "Absolute",
                    key="total_spends_change_abs",
                    # label_visibility="collapsed",
                    on_change=update_all_spends_abs,
                )

            with _columns2[1]:
                st.number_input(
                    "Percent Change",
                    key="total_spends_change",
                    min_value=-50,
                    max_value=50,
                    step=1,
                    on_change=update_spends,
                )

            with _columns2[2]:
                min_value = round(_scenario.actual_total_spends * 0.5)
                max_value = round(_scenario.actual_total_spends * 1.5)
                st.session_state["total_spends_change_abs_slider_options"] = [
                    numerize(value, 1)
                    for value in range(min_value, max_value + 1, int(1e4))
                ]

                # st.select_slider(
                #     "Absolute Slider",
                #     options=st.session_state["total_spends_change_abs_slider_options"],
                #     key="total_spends_change_abs_slider",
                #     on_change=update_all_spends_abs_slider,
                # )
        
        elif st.session_state["optimization_key"] == target:
            # st.write(target)
            with _columns2[0]:
                sales_input = st.text_input(
                    "Absolute",
                    key="total_sales_change_abs",
                    on_change=update_sales_abs,
                )

            with _columns2[1]:
                st.number_input(
                    "Percent Change",
                    key="total_sales_change",
                    min_value=-50,
                    max_value=50,
                    step=1,
                    on_change=update_sales,
                )
            with _columns2[2]:
                min_value = round(_scenario.actual_total_sales * 0.5)
                max_value = round(_scenario.actual_total_sales * 1.5)
                st.write(min_value)
                st.write(max_value)
                # for value in range(min_value, max_value + 1, int(100)):
                #     st.write(numerize(value, 1))
                st.session_state["total_sales_change_abs_slider_options"] = [
                    numerize(value, 1)
                    for value in range(min_value, max_value + 1, int(100))
                ]

                st.select_slider(
                    "Absolute Slider",
                    options=st.session_state["total_sales_change_abs_slider_options"],
                    key="total_sales_change_abs_slider",
                    on_change=update_sales_abs_slider,
                    # value=numerize(min_value, 1)
                )

        if (
            not st.session_state["allow_sales_update"]
            and optimization_selection == target
        ):
            st.warning("Invalid Input")

        if (
            not st.session_state["allow_spends_update"]
            and optimization_selection == "Media Spends"
        ):
            st.warning("Invalid Input")

        status_placeholder = st.empty()

        # if optimize_placeholder.button("Optimize", use_container_width=True):
        #     optimize(st.session_state["optimization_key"], status_placeholder)
        #     st.rerun()

        optimize_placeholder.button(
            "Optimize",
            on_click=optimize,
            args=(st.session_state["optimization_key"], status_placeholder),
            # use_container_width=True,
        )

        st.markdown("""<hr class="spends-heading-seperator">""", unsafe_allow_html=True)
        _columns = st.columns((2.5, 2, 1.5, 1.5, 1))
        with _columns[0]:
            generate_spending_header("Channel")
        with _columns[1]:
            generate_spending_header("Spends Input")
        with _columns[2]:
            generate_spending_header("Spends")
        with _columns[3]:
            generate_spending_header(target)
        with _columns[4]:
            generate_spending_header("Optimize")

        st.markdown("""<hr class="spends-heading-seperator">""", unsafe_allow_html=True)

        if "acutual_predicted" not in st.session_state:
            st.session_state["acutual_predicted"] = {
                "Channel_name": [],
                "Actual_spend": [],
                "Optimized_spend": [],
                "Delta": [],
                "New_sales":[],
                "Old_sales":[]
            }
        for i, channel_name in enumerate(channels_list):
            # st.write(channel_name)
            _channel_class = st.session_state["scenario"].channels[channel_name]
            _columns = st.columns((2.5, 1.5, 1.5, 1.5, 1))
            with _columns[0]:
                st.write(channel_name_formating(channel_name))
                bin_placeholder = st.container()

            with _columns[1]:
                channel_bounds = _channel_class.bounds
                channel_spends = float(_channel_class.actual_total_spends)
                min_value = float((1 + channel_bounds[0] / 100) * channel_spends)
                max_value = float((1 + channel_bounds[1] / 100) * channel_spends)
                ##print(st.session_state[channel_name])
                spend_input = st.text_input(
                    channel_name,
                    key=channel_name,
                    label_visibility="collapsed",
                    on_change=partial(update_data, channel_name),
                )
                if not validate_input(spend_input):
                    st.error("Invalid input")

                channel_name_current = f"{channel_name}_change"

                st.number_input(
                    "Percent Change",
                    key=channel_name_current,
                    step=1,
                    on_change=partial(update_data_by_percent, channel_name),
                )

            with _columns[2]:
                # spends
                current_channel_spends = float(
                    _channel_class.modified_total_spends
                    * _channel_class.conversion_rate
                )
                actual_channel_spends = float(
                    _channel_class.actual_total_spends * _channel_class.conversion_rate
                )
                spends_delta = float(
                    _channel_class.delta_spends * _channel_class.conversion_rate
                )
                st.session_state["acutual_predicted"]["Channel_name"].append(
                    channel_name
                )
                st.session_state["acutual_predicted"]["Actual_spend"].append(
                    actual_channel_spends
                )
                st.session_state["acutual_predicted"]["Optimized_spend"].append(
                    current_channel_spends
                )
                st.session_state["acutual_predicted"]["Delta"].append(spends_delta)
                ## REMOVE
                st.metric(
                    "Spends",
                    format_numbers(current_channel_spends),
                    delta=numerize(spends_delta, 1),
                    label_visibility="collapsed",
                )

            with _columns[3]:
                # sales
                current_channel_sales = float(_channel_class.modified_total_sales)
                actual_channel_sales = float(_channel_class.actual_total_sales)
                sales_delta = float(_channel_class.delta_sales)
                st.session_state["acutual_predicted"]["Old_sales"].append(actual_channel_sales)
                st.session_state["acutual_predicted"]["New_sales"].append(current_channel_sales)
                #st.write(actual_channel_sales)

                st.metric(
                    target,
                    format_numbers_f(current_channel_sales),
                    delta=numerize(sales_delta, 1),
                    label_visibility="collapsed",
                )

            with _columns[4]:

                # if st.checkbox(
                #     label="select for optimization",
                #     key=f"{channel_name}_selected",
                #     value=False,
                #     # on_change=partial(select_channel_for_optimization, channel_name),
                #     label_visibility="collapsed",
                # ):
                #     select_channel_for_optimization(channel_name)

                st.checkbox(
                    label="select for optimization",
                    key=f"{channel_name}_selected",
                    value=False,
                    on_change=partial(select_channel_for_optimization, channel_name),
                    label_visibility="collapsed",
                )

            st.markdown(
                """<hr class="spends-child-seperator">""",
                unsafe_allow_html=True,
            )

            # Bins
            col = channels_list[i]
            x_actual = st.session_state["scenario"].channels[col].actual_spends
            x_modified = st.session_state["scenario"].channels[col].modified_spends
            # x_modified_total = 0 
            # for c in channels_list:
            #     # st.write(c)
            #     # st.write(st.session_state["scenario"].channels[c].modified_spends)
            #     x_modified_total = x_modified_total + st.session_state["scenario"].channels[c].modified_spends.sum()
            # st.write(x_modified_total)
            
            x_total = x_modified.sum()
            power = np.ceil(np.log(x_actual.max()) / np.log(10)) - 3

            updated_rcs_key = f"{metrics_selected}#@{panel_selected}#@{channel_name}"

            if updated_rcs and updated_rcs_key in list(updated_rcs.keys()):
                K = updated_rcs[updated_rcs_key]["K"]
                b = updated_rcs[updated_rcs_key]["b"]
                a = updated_rcs[updated_rcs_key]["a"]
                x0 = updated_rcs[updated_rcs_key]["x0"]
            else:
                K = st.session_state["rcs"][col]["K"]
                b = st.session_state["rcs"][col]["b"]
                a = st.session_state["rcs"][col]["a"]
                x0 = st.session_state["rcs"][col]["x0"]

            x_plot = np.linspace(0, 5 * x_actual.sum(), 200)

            # Append current_channel_spends to the end of x_plot
            x_plot = np.append(x_plot, current_channel_spends)

            x, y, marginal_roi = [], [], []
            for x_p in x_plot:
                x.append(x_p * x_actual / x_actual.sum())

            for index in range(len(x_plot)):
                y.append(s_curve(x[index] / 10**power, K, b, a, x0))

            for index in range(len(x_plot)):
                marginal_roi.append(
                    a * y[index] * (1 - y[index] / np.maximum(K, np.finfo(float).eps))
                )

            x = (
                np.sum(x, axis=1)
                * st.session_state["scenario"].channels[col].conversion_rate
            )
            y = np.sum(y, axis=1)
            marginal_roi = (
                np.average(marginal_roi, axis=1)
                / st.session_state["scenario"].channels[col].conversion_rate
            )

            roi = y / np.maximum(x, np.finfo(float).eps)
            # roi = (y/np.sum(y))/(x/np.sum(x))
            # st.write(x)
            # st.write(y)
            # st.write(roi)

            # st.write(roi[-1])
            
            roi_current, marginal_roi_current = roi[-1], marginal_roi[-1]
            x, y, roi, marginal_roi = (
                x[:-1],
                y[:-1],
                roi[:-1],
                marginal_roi[:-1],
            )  # Drop data for current spends

            # roi_current = 

            start_value, end_value, left_value, right_value = find_segment_value(
                x,
                roi,
                marginal_roi,
            )

            #st.write(roi_current)

            rgba = calculate_rgba(
                start_value,
                end_value,
                left_value,
                right_value,
                current_channel_spends,
            )

            summary_df = pd.DataFrame(st.session_state["acutual_predicted"])
            # st.dataframe(summary_df)
            summary_df.drop_duplicates(subset="Channel_name", keep="last", inplace=True)
            # st.dataframe(summary_df)

            summary_df_sorted = summary_df.sort_values(by="Delta", ascending=False)
            summary_df_sorted["Delta_percent"] = np.round(
                ((summary_df_sorted["Optimized_spend"] / summary_df_sorted["Actual_spend"]) - 1)
                * 100,
                2,
            )

            summary_df_sorted=summary_df_sorted.sort_values(by=['Optimized_spend'],ascending=False)
            summary_df_sorted['old_efficiency']=(summary_df_sorted['Old_sales']/summary_df_sorted['Old_sales'].sum())/(summary_df_sorted['Actual_spend']/summary_df_sorted['Actual_spend'].sum())
            summary_df_sorted['new_efficiency']=(summary_df_sorted['New_sales']/summary_df_sorted['New_sales'].sum())/(summary_df_sorted['Optimized_spend']/summary_df_sorted['Optimized_spend'].sum())
            
            a = (summary_df_sorted[summary_df_sorted['Channel_name']== col]).reset_index()['new_efficiency'][0]
            # st.write(a)

            with bin_placeholder:
                if a> 1:
                    fill_color_box = "#98fb98"
                elif a <1:
                    fill_color_box = "#ff6868"
                else: 
                    fill_color_box = "#ff6868"
                st.markdown(
                    f"""
                    <div style="
                        border-radius: 12px;
                        background-color: {fill_color_box};
                        padding: 10px;
                        text-align: center;
                        color: {'black'};
                        ">
                        <p style="margin: 0; font-size: 20px;">Efficiency: {round(a,2)}</p>
                        <!--<p style="margin: 0; font-size: 20px;">Marginal ROI: {round(marginal_roi_current,1)}</p>-->
                    </div>
                    """,
                    unsafe_allow_html=True,
                )

    with st.expander("See Response Curves", expanded=True):
        fig = plot_response_curves(summary_df_sorted)
        # st.plotly_chart(rc.response_curves(col))
        # st.plotly_chart(fig, use_container_width=True)

    summary_df = pd.DataFrame(st.session_state["acutual_predicted"])
    # st.dataframe(summary_df)
    summary_df.drop_duplicates(subset="Channel_name", keep="last", inplace=True)
    # st.dataframe(summary_df)

    summary_df_sorted = summary_df.sort_values(by="Delta", ascending=False)
    summary_df_sorted["Delta_percent"] = np.round(
        ((summary_df_sorted["Optimized_spend"] / summary_df_sorted["Actual_spend"]) - 1)
        * 100,
        2,
    )

    

    

    with open("summary_df.pkl", "wb") as f:
        pickle.dump(summary_df_sorted, f)
        # st.dataframe(summary_df_sorted)
        # ___columns=st.columns(3)
        # with ___columns[2]:
        #     fig=summary_plot(summary_df_sorted, x='Delta_percent', y='Channel_name', title='Delta', text_column='Delta_percent')
        #     st.plotly_chart(fig,use_container_width=True)
        # with ___columns[0]:
        #     fig=summary_plot(summary_df_sorted, x='Actual_spend', y='Channel_name', title='Actual Spend', text_column='Actual_spend')
        #     st.plotly_chart(fig,use_container_width=True)
        # with ___columns[1]:
        #     fig=summary_plot(summary_df_sorted, x='Optimized_spend', y='Channel_name', title='Planned Spend', text_column='Optimized_spend')
        #     st.plotly_chart(fig,use_container_width=True)

    scenario_planner_plots()

    _columns = st.columns(2)
    # with _columns[0]:
    st.subheader("Save Scenario")
    scenario_name = st.text_input(
        "Scenario name",
        key="scenario_input",
        placeholder="Scenario name",
        label_visibility="collapsed",
    )
    st.button(
        "Save",
        on_click=lambda: save_scenario(scenario_name),
        disabled=len(st.session_state["scenario_input"]) == 0,#use_container_width=True
    )



elif auth_status == False:
    st.error("Username/Password is incorrect")

if auth_status != True:
    try:
        username_forgot_pw, email_forgot_password, random_password = (
            authenticator.forgot_password("Forgot password")
        )
        if username_forgot_pw:
            st.session_state["config"]["credentials"]["usernames"][username_forgot_pw][
                "password"
            ] = stauth.Hasher([random_password]).generate()[0]
            send_email(email_forgot_password, random_password)
            st.success("New password sent securely")
            # Random password to be transferred to user securely
        elif username_forgot_pw == False:
            st.error("Username not found")
    except Exception as e:
        st.error(e)