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RFI

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# Importing necessary libraries
import streamlit as st

st.set_page_config(
    page_title="Saved Scenarios",
    page_icon="⚖️",
    layout="wide",
    initial_sidebar_state="collapsed",
)

import io
import os
import json
import pickle
import sqlite3
import zipfile
import numpy as np
import pandas as pd
from classes import numerize
from openpyxl import Workbook
from collections import OrderedDict
from utilities import (
    project_selection,
    initialize_data,
    set_header,
    load_local_css,
    name_formating,
)

# Styling
load_local_css("styles.css")
set_header()

# Create project_dct
if "project_dct" not in st.session_state:

    project_selection()
    st.stop()

    database_file = r"DB\User.db"

    conn = sqlite3.connect(
        database_file, check_same_thread=False
    )  # connection with sql db
    c = conn.cursor()

# Display project info
col_project_data = st.columns([2, 1])
with col_project_data[0]:
    st.markdown(f"**Welcome {st.session_state['username']}**")
with col_project_data[1]:
    st.markdown(f"**Current Project: {st.session_state['project_name']}**")

# Page Title
st.title("Saved Scenarios")
scenarios_name_placeholder = st.empty()


# Function to get saved scenarios dictionary
def get_saved_scenarios_dict():
    # Path to the saved scenarios file
    saved_scenarios_dict_path = os.path.join(
        st.session_state["project_path"], "saved_scenarios.pkl"
    )

    # Load existing scenarios if the file exists
    if os.path.exists(saved_scenarios_dict_path):
        with open(saved_scenarios_dict_path, "rb") as f:
            saved_scenarios_dict = pickle.load(f)
    else:
        saved_scenarios_dict = OrderedDict()

    return saved_scenarios_dict


# Function to format values based on their size
def format_value(value):
    return round(value, 4) if value < 1 else round(value, 1)


# Function to recursively convert non-serializable types to serializable ones
def convert_to_serializable(obj):
    if isinstance(obj, np.ndarray):
        return obj.tolist()
    elif isinstance(obj, dict):
        return {key: convert_to_serializable(value) for key, value in obj.items()}
    elif isinstance(obj, list):
        return [convert_to_serializable(element) for element in obj]
    elif isinstance(obj, (int, float, str, bool, type(None))):
        return obj
    else:
        # Fallback: convert the object to a string
        return str(obj)


# Function to generate zip file of current scenario
@st.cache_data(show_spinner=False)
def download_as_zip(
    df,
    scenario_data,
    excel_name="optimization_results.xlsx",
    json_name="scenario_params.json",
):
    # Create an in-memory bytes buffer for the ZIP file
    buffer = io.BytesIO()

    # Create a ZipFile object in memory
    with zipfile.ZipFile(buffer, "w") as zip_file:
        # Save the DataFrame to an Excel file in the zip using openpyxl
        excel_buffer = io.BytesIO()
        workbook = Workbook()
        sheet = workbook.active
        sheet.title = "Results"

        # Write DataFrame headers
        for col_num, column_title in enumerate(df.columns, 1):
            sheet.cell(row=1, column=col_num, value=column_title)

        # Write DataFrame data
        for row_num, row_data in enumerate(df.values, 2):
            for col_num, cell_value in enumerate(row_data, 1):
                sheet.cell(row=row_num, column=col_num, value=cell_value)

        # Save the workbook to the in-memory buffer
        workbook.save(excel_buffer)
        excel_buffer.seek(0)  # Rewind the buffer to the beginning
        zip_file.writestr(excel_name, excel_buffer.getvalue())

        # Save the dictionary to a JSON file in the zip
        json_buffer = io.BytesIO()
        json_buffer.write(
            json.dumps(convert_to_serializable(scenario_data), indent=4).encode("utf-8")
        )
        json_buffer.seek(0)  # Rewind the buffer to the beginning
        zip_file.writestr(json_name, json_buffer.getvalue())

    buffer.seek(0)  # Rewind the buffer to the beginning

    return buffer


# Function to delete the selected scenario from the saved scenarios dictionary
def delete_selected_scenarios(selected_scenario):
    # Path to the saved scenarios file
    saved_scenarios_dict_path = os.path.join(
        st.session_state["project_path"], "saved_scenarios.pkl"
    )

    # Load existing scenarios if the file exists
    if os.path.exists(saved_scenarios_dict_path):
        with open(saved_scenarios_dict_path, "rb") as f:
            saved_scenarios_dict = pickle.load(f)
    else:
        saved_scenarios_dict = OrderedDict()

    try:
        # Attempt to delete the selected scenario
        del saved_scenarios_dict[selected_scenario]

        # Save the updated dictionary back to the file
        with open(saved_scenarios_dict_path, "wb") as f:
            pickle.dump(saved_scenarios_dict, f)

    except KeyError:
        # If the scenario is not found in the dictionary, ignore the error
        pass


# Get saved scenarios dictionary and scenario name list
saved_scenarios_dict = get_saved_scenarios_dict()
scenarios_list = list(saved_scenarios_dict.keys())

# Check if the list of saved scenarios is empty
if len(scenarios_list) == 0:
    # Display a warning message if no scenarios are saved
    st.warning("No scenarios saved. Please save a scenario to load.", icon="⚠️")
    st.stop()

# Display a dropdown saved scenario list
selected_scenario = st.selectbox(
    "Pick a Scenario", sorted(scenarios_list), key="selected_scenario"
)
selected_scenario_data = saved_scenarios_dict[selected_scenario]

# Scenarios Name
metrics_name = selected_scenario_data["metrics_selected"]
panel_name = selected_scenario_data["panel_selected"]
optimization_name = selected_scenario_data["optimization"]

# Display the scenario details with bold "Metric," "Panel," and "Optimization"
scenarios_name_placeholder.markdown(
    f"**Metric**: {name_formating(metrics_name)}; **Panel**: {name_formating(panel_name)}; **Optimization**: {name_formating(optimization_name)}"
)

# Create columns for download and delete buttons
download_col, delete_col = st.columns(2)


channels_list = list(selected_scenario_data["channels"].keys())

# List to hold data for all channels
channels_data = []

# Iterate through each channel and gather required data
for channel in channels_list:
    channel_conversion_rate = selected_scenario_data["channels"][channel][
        "conversion_rate"
    ]
    channel_actual_spends = (
        selected_scenario_data["channels"][channel]["actual_total_spends"]
        * channel_conversion_rate
    )
    channel_optimized_spends = (
        selected_scenario_data["channels"][channel]["modified_total_spends"]
        * channel_conversion_rate
    )

    channel_actual_metrics = selected_scenario_data["channels"][channel][
        "actual_total_sales"
    ]
    channel_optimized_metrics = selected_scenario_data["channels"][channel][
        "modified_total_sales"
    ]

    channel_roi_mroi_data = selected_scenario_data["channel_roi_mroi"][channel]

    # Extract the ROI and MROI data
    actual_roi = channel_roi_mroi_data["actual_roi"]
    optimized_roi = channel_roi_mroi_data["optimized_roi"]
    actual_mroi = channel_roi_mroi_data["actual_mroi"]
    optimized_mroi = channel_roi_mroi_data["optimized_mroi"]

    # Calculate spends per metric
    spends_per_metrics_actual = channel_actual_spends / channel_actual_metrics
    spends_per_metrics_optimized = channel_optimized_spends / channel_optimized_metrics

    # Append the collected data as a dictionary to the list
    channels_data.append(
        {
            "Channel Name": channel,
            "Spends Actual": numerize(channel_actual_spends),
            "Spends Optimized": numerize(channel_optimized_spends),
            f"{name_formating(metrics_name)} Actual": numerize(channel_actual_metrics),
            f"{name_formating(metrics_name)} Optimized": numerize(
                channel_optimized_metrics
            ),
            "ROI Actual": format_value(actual_roi),
            "ROI Optimized": format_value(optimized_roi),
            "MROI Actual": format_value(actual_mroi),
            "MROI Optimized": format_value(optimized_mroi),
            f"Spends per {name_formating(metrics_name)} Actual": round(
                spends_per_metrics_actual, 2
            ),
            f"Spends per {name_formating(metrics_name)} Optimized": round(
                spends_per_metrics_optimized, 2
            ),
        }
    )

# Create a DataFrame from the collected data
df = pd.DataFrame(channels_data)

# Display the DataFrame
st.dataframe(df, hide_index=True)

# Generate download able data for selected scenario
buffer = download_as_zip(
    df,
    selected_scenario_data,
    excel_name="optimization_results.xlsx",
    json_name="scenario_params.json",
)

# Provide the buffer as a downloadable ZIP file in Streamlit
download_col.download_button(
    label="Download",
    data=buffer,
    file_name=f"{selected_scenario}_scenario_data.zip",
    mime="application/zip",
    use_container_width=True,
)

# Button to trigger the deletion of the selected scenario
delete_col.button(
    "Delete",
    use_container_width=True,
    on_click=delete_selected_scenarios,
    args=(selected_scenario,),
)