app.py

import streamlit as st
import requests
import pandas as pd
import json
import io
import datetime
import pandas as pd
import numpy as np
import requests
import base64
import json
from calendar import monthrange
import pymongo
from mongoengine import StringField, ListField, DateTimeField, DictField
import matplotlib.pyplot as plt
from matplotlib.dates import MonthLocator



def mongo_unavs_call(user_input_start_date, user_input_end_date, user_input_past_date):
    print("Starting mongo_unavs_call")
    # Connect to the MongoDB database
    user = "dmarroquin"
    passw = "tN9XpCCQM2MtYDme"
    host = "nucmonitordata.xxcwx9k.mongodb.net"
    client = pymongo.MongoClient(
        f"mongodb+srv://{user}:{passw}@{host}/?retryWrites=true&w=majority&connectTimeoutMS=100000"
    )

    db = client["data"]
    collection_past_unavs = db["unavs"]
    collection_unavs = db["unavs_update"]

    start_date = f"{user_input_start_date}T00:00:00"
    end_date = f"{user_input_end_date}T23:59:59"
    past_date = f"{user_input_past_date}T23:59:59"


    pipeline_v4 = [
        # 1) Expand each results element into its own doc
        { "$unwind": "$results" },

        # 2) Expand each generation_unavailabilities element
        { "$unwind": "$results.generation_unavailabilities" },

        # 3) Keep only those that match your fuel_type + date criteria
        {
            "$match": {
                "results.generation_unavailabilities.production_type": "NUCLEAR",
                "results.generation_unavailabilities.updated_date": { "$lte": past_date },
                "results.generation_unavailabilities.start_date": { "$lte": end_date },
                "results.generation_unavailabilities.start_date": { "$gte": start_date },
                "results.generation_unavailabilities.end_date": { "$gte": start_date },
                "results.generation_unavailabilities.end_date": { "$lte": end_date }

            }
        },

        # 4) Replace the entire document with just that sub-doc
        {
            "$replaceRoot": {
                "newRoot": "$results.generation_unavailabilities"
            }
        }
    ]


    pipeline_v6 = [
        # 1) Expand each results element into its own doc
        { "$unwind": "$results" },

        # 2) Expand each generation_unavailabilities element
        { "$unwind": "$results.generation_unavailabilities" },

        # 3) Keep only those that match your fuel_type + date criteria
        {
            "$match": {
                "results.generation_unavailabilities.fuel_type": "NUCLEAR",
                "results.generation_unavailabilities.publication_date": { "$lte": past_date },
                "results.generation_unavailabilities.start_date": { "$lte": end_date },
                # "results.generation_unavailabilities.start_date": { "$gte": start_date },
                "results.generation_unavailabilities.end_date": { "$gte": start_date },
                # "results.generation_unavailabilities.end_date": { "$lte": end_date }
            }
        },

        # 4) Replace the entire document with just that sub-doc
        {
            "$replaceRoot": {
                "newRoot": "$results.generation_unavailabilities"
            }
        }
    ]

    result1 = list(collection_past_unavs.aggregate(pipeline_v4))
    result2 = list(collection_unavs.aggregate(pipeline_v4))
    result_v6 = list(collection_unavs.aggregate(pipeline_v6))
    merge_results = result1 + result2 + result_v6
    
    return merge_results

# --------------------------------------------------------------------------------------- #

# Convert the dictionary of dictionaries to JSON
def convert_to_json(item):
    if isinstance(item, dict):
        return {str(k): convert_to_json(v) for k, v in item.items()}
    elif isinstance(item, list):
        return [convert_to_json(i) for i in item]
    elif isinstance(item, ObjectId):
        return str(item)
    else:
        return item
# --------------------------------------------------------------------------------------- #

# Function gives the total of the data. When printed as dataframe/excel,
# Will give a final row with the total for each plant and the total overall
def add_total(data):
    total_values = {}
    for key in data:
        daily_values = data[key]
        total = sum(daily_values.values())
        daily_values["Total"] = total
        for date, value in daily_values.items():
            if date not in total_values:
                total_values[date] = value
            else:
                total_values[date] += value
        
    data["Total"] = total_values

# --------------------------------------------------------------------------------------- #

def nuc_monitor(usr_start_date, usr_end_date, past_date, mongo_db_data):
    # # Slightly changed metadata to fit the data from the RTE API: ST-LAURENT B 2 --> ST LAURENT 2, ....

    plants_metadata = {"BELLEVILLE 1": 1310.0, "BELLEVILLE 2": 1310.0, "BLAYAIS 1": 910.0, "BLAYAIS 2": 910.0, 
                    "BLAYAIS 3": 910.0, "BLAYAIS 4": 910.0, "BUGEY 2": 910.0, "BUGEY 3": 910.0, "BUGEY 4": 880.0, 
                    "BUGEY 5": 880.0, "CATTENOM 1": 1300.0, "CATTENOM 2": 1300.0, "CATTENOM 3": 1300.0, 
                    "CATTENOM 4": 1300.0, "CHINON 1": 905.0, "CHINON 2": 905.0, "CHINON 3": 905.0, 
                    "CHINON 4": 905.0, "CHOOZ 1": 1500.0, "CHOOZ 2": 1500.0, "CIVAUX 1": 1495.0, 
                    "CIVAUX 2": 1495.0, "CRUAS 1": 915.0, "CRUAS 2": 915.0, "CRUAS 3": 915.0, "CRUAS 4": 915.0, 
                    "DAMPIERRE 1": 890.0, "DAMPIERRE 2": 890.0, "DAMPIERRE 3": 890.0, "DAMPIERRE 4": 890.0, 
                    "FLAMANVILLE 1": 1330.0, "FLAMANVILLE 2": 1330.0,  "FLAMANVILLE 3": 1620.0, "GOLFECH 1": 1310.0, "GOLFECH 2": 1310.0, 
                    "GRAVELINES 1": 910.0, "GRAVELINES 2": 910.0, "GRAVELINES 3": 910.0, "GRAVELINES 4": 910.0, 
                    "GRAVELINES 5": 910.0, "GRAVELINES 6": 910.0, "NOGENT 1": 1310.0, "NOGENT 2": 1310.0, 
                    "PALUEL 1": 1330.0, "PALUEL 2": 1330.0, "PALUEL 3": 1330.0, "PALUEL 4": 1330.0, "PENLY 1": 1330.0, 
                    "PENLY 2": 1330.0, "ST ALBAN 1": 1335.0, "ST ALBAN 2": 1335.0, "ST LAURENT 1": 915.0, 
                    "ST LAURENT 2": 915.0, "TRICASTIN 1": 915.0, "TRICASTIN 2": 915.0, "TRICASTIN 3": 915.0, 
                    "TRICASTIN 4": 915.0, "FESSENHEIM 1": 0.0, "FESSENHEIM 2": 0.0}

    # --------------------- INITIAL DATA CLEANING FOR MONGO DATA ------------------------ #    

    # # Create a DataFrame
    # mongo_data = mongo_unavs_call(usr_start_date, usr_end_date, past_date)
    # mongo_data = get_mongodb_data(usr_start_date, usr_end_date, past_date)
    # print(mongo_db_data)
    mongo_df = pd.DataFrame(mongo_db_data)

    # the five columns you care about
    _optional = {
        "updated_date",
        "type",
        "production_type",
        "unit",
        "status",
    }

    # see which of those actually exist
    present = set(mongo_df.columns) & _optional

    if _optional.issubset(mongo_df.columns):
        # 0. first, pick the columns you know exist, plus the always‑present ones
        cols = [
            "identifier", "version", "message_id",
            "values", "publication_date", "unavailability_type", "fuel_type",
            "affected_asset_or_unit_name", "affected_asset_or_unit_installed_capacity",
            "event_status"
        ] + list(_optional)
        mongo_df = mongo_df[cols]

        # 1. normalize “unit”
        unit_expanded = pd.json_normalize(mongo_df["unit"])
        mongo_df_2 = pd.concat([mongo_df.drop(columns=["unit"]), unit_expanded], axis=1)

        # 2. normalize the first element of “values”
        mongo_df_2["values_first"] = (
            mongo_df_2["values"]
            .apply(lambda lst: lst[0] if isinstance(lst, list) and lst else {})
        )
        values_expanded = pd.json_normalize(mongo_df_2["values_first"])
        mongo_df_2 = pd.concat(
            [mongo_df_2.drop(columns=["values", "values_first"]), values_expanded],
            axis=1
        )

        # 3. coalesce and drop old cols
        mongo_df_2["fuel_type"] = mongo_df_2["fuel_type"].combine_first(mongo_df_2["production_type"])
        mongo_df_2["publication_date"] = mongo_df_2["publication_date"].combine_first(mongo_df_2["updated_date"])
        mongo_df_2["event_status"] = mongo_df_2["event_status"].combine_first(mongo_df_2["status"])
        mongo_df_2["affected_asset_or_unit_installed_capacity"] = (
            mongo_df_2["affected_asset_or_unit_installed_capacity"]
            .combine_first(mongo_df_2["installed_capacity"])
        )
        mongo_df_2["affected_asset_or_unit_name"] = (
            mongo_df_2["affected_asset_or_unit_name"]
            .combine_first(mongo_df_2["name"])
        )
        mongo_df_2["unavailability_type"] = (
            mongo_df_2["unavailability_type"]
            .combine_first(mongo_df_2["type"].iloc[:, 0])
        )

        drop_cols = [
            "production_type",
            "updated_date",
            "status",
            "installed_capacity",
            "name",
            "type",
            "eic_code",
        ]
        # only drop those that are actually there
        drop_cols = [c for c in drop_cols if c in mongo_df_2.columns]
        mongo_df_2 = mongo_df_2.drop(columns=drop_cols)

        # now mongo_df_2 is your final
    else:
        # at least one of the required columns is missing:
        # present contains the ones you did find
        missing = _optional - present
        print(f"Skipping normalize process because these columns are missing: {missing}")
        mongo_df_2 = mongo_df.copy()  # or however you want to proceed

        mongo_df_2["values_first"] = mongo_df_2["values"].apply(
            lambda lst: lst[0] if isinstance(lst, list) and len(lst) > 0 else {}
        )

        # 2. Normalize that dict into separate columns
        values_expanded = pd.json_normalize(mongo_df_2["values_first"])
        #    e.g. this produces columns like “start_date”, “end_date”, etc.

        # 3. Concatenate back and drop the originals
        mongo_df_2 = pd.concat(
            [
                mongo_df_2.drop(columns=["values", "values_first", "start_date", "end_date"]),
                values_expanded
            ],
            axis=1
        )

    # Convert the date columns to datetime objects
    for col in ["publication_date", "start_date", "end_date"]:
        mongo_df_2[col] = pd.to_datetime(mongo_df_2[col], utc=True)

    # # Now convert everything to French time (CET/CEST):
    # for col in ["publication_date", "start_date", "end_date"]:
    #     mongo_df_2[col] = mongo_df_2[col].dt.tz_convert("Europe/Paris")

    # mongo_df_2 = mongo_df_2.drop_duplicates(subset='identifier', keep='first')
    mongo_df_2['version'] = mongo_df_2['version'].astype(float)
    # Sort by identifier and version to ensure the latest version is at the top
    # Method 1: Use groupby + idxmax to pick the row with the largest version per identifier
    idx = mongo_df_2.groupby("identifier")["version"].idxmax()
    mongo_df_2 = mongo_df_2.loc[idx].reset_index(drop=True)

    mongo_df_2 = mongo_df_2[mongo_df_2['event_status'] != 'DISMISSED']


    # Create the final dataframe
    final_df = pd.DataFrame()

    # Create the date column, with date range from start_date to end_date in daily granularity
    final_df['Date'] = pd.date_range(start=usr_start_date, end=usr_end_date, freq='D')
    final_df['Date'] = [ts.strftime("%Y-%m-%d") for ts in final_df['Date']]

    # For each plant create a new column with the plant name
    for plant, capacity in plants_metadata.items():
        # Create a new column for each plant
        final_df[plant] = np.nan  # Initialize with zeros

    mongo_df_3 = mongo_df_2.copy()

    dates_of_interest = list(pd.date_range(start=usr_start_date, end=usr_end_date, freq="D"))

    # Now convert each Timestamp → “YYYY-MM-DD” string:
    dates_of_interest = [ts.strftime("%Y-%m-%d") for ts in dates_of_interest]

    mongo_df_3['start_day'] = mongo_df_3['start_date'].dt.day
    mongo_df_3['start_hour'] = mongo_df_3['start_date'].dt.hour
    mongo_df_3['start_minute'] = mongo_df_3['start_date'].dt.minute
    mongo_df_3['end_day'] = mongo_df_3['end_date'].dt.day
    mongo_df_3['end_hour'] = mongo_df_3['end_date'].dt.hour
    mongo_df_3['end_minute'] = mongo_df_3['end_date'].dt.minute

    # mongo_df_3 = mongo_df_3.sort_values(by=['publication_date'], ascending=False)
    mongo_df_3 = mongo_df_3.sort_values(by=['publication_date'])

    # results_plants = {plant_name: {date: {"available_capacity": power, "publication_date": pd.to_datetime("1970-01-01", utc=True)} for date in dates_of_interest}
    #                 for plant_name, power in plants_metadata.items()}

    results_plants = {plant_name: {date: {"available_capacity": power, "publication_date": pd.to_datetime("1970-01-01", utc=True)} 
                                for date in dates_of_interest}
                                    for plant_name, power in plants_metadata.items()}

    for row in mongo_df_3.itertuples():
        # Get the start and end dates for the unavailability
        row_start_date = str(row.start_date.date())
        row_end_date = str(row.end_date.date())
        
        # Get the plant name and capacity
        plant_name = row.affected_asset_or_unit_name
        plant_capacity = plants_metadata.get(plant_name, 0)  # Default to 0 if not found

        results_current_plant = results_plants[plant_name]

        power_unavailability = row.available_capacity
        publication_date_unav = row.publication_date
        
        for day in dates_of_interest:
            # percentage_of_day = results_current_plant[day]["percentage_of_day"]


            if row_start_date <= day <= row_end_date:
                
                # Check if the day is already updated with a later (more recent) update_date; by sorting the DataFrame by publication_date, 
                # we ensure that the latest unavailability is applied
                # Get the percentage of day that the plant is unavailable

                # if day in final_df['Date'] and pd.notna(final_df.loc[final_df['Date'] == day, plant_name]).any():
                if (day in results_current_plant) and (publication_date_unav <= results_current_plant[day]["publication_date"]):
                    # If the plant's capacity for that day is already set, skip to the next day
                    continue

                # The unavailability starts and ends on the same day
                if row_start_date == day and day == row_end_date:
                    percentage_of_day = (row.end_hour * 60 + row.end_minute - row.start_hour * 60 - row.start_minute) / (24 * 60)
                    # results_current_plant[day]["percentage_of_day"] += percentage_of_day
                    # power_of_day = percentage_of_day * row.available_capacity + (1 - percentage_of_day) * plant_capacity
                    # final_df.loc[final_df['Date'] == day, plant_name] = power_of_day

                # The unavailability starts on the current day but ends on a later day
                elif row_start_date == day and day < row_end_date:
                    percentage_of_day = (24 * 60 - (row.start_hour * 60 + row.start_minute)) / (24 * 60)
                    # results_current_plant[day]["percentage_of_day"] += percentage_of_day

                    # power_of_day = percentage_of_day * row.available_capacity + (1 - percentage_of_day) * plant_capacity
                    # final_df.loc[final_df['Date'] == day, plant_name] = power_of_day

                # # The unavailability starts on a previous day and ends on the current day
                elif row_end_date == day and row_start_date < day:
                    percentage_of_day = (row.end_hour * 60 + row.end_minute) / (24 * 60)
                    # results_current_plant[day]["percentage_of_day"] += percentage_of_day

                    # power_of_day = percentage_of_day * row.available_capacity + (1 - percentage_of_day) * plant_capacity
                    # final_df.loc[final_df['Date'] == day, plant_name] = power_of_day
                
                else:
                    # The unavailability starts on a previous day and ends on a later day
                    percentage_of_day = 1
                    # power_of_day = percentage_of_day * row.available_capacity + (1 - percentage_of_day) * plant_capacity
                    # final_df.loc[final_df['Date'] == day, plant_name] = power_of_day

                power_of_day = percentage_of_day * power_unavailability + (1 - percentage_of_day) * plant_capacity

                # Update the available_capacity for the day only if it's not already updated with a later update_date
                if (day not in results_current_plant):
                    results_current_plant[day] = {"available_capacity": power_of_day, "publication_date": publication_date_unav}
                
                elif (day in results_current_plant) and (publication_date_unav > results_current_plant[day]["publication_date"]) \
                        and (power_of_day < results_current_plant[day]['available_capacity']):
                    # results_current_plant[day]["available_capacity"] *= power_of_day
                    # results_current_plant[day]["publication_date"] = publication_date_unav

                    results_current_plant[day] = {"available_capacity": power_of_day, "publication_date": publication_date_unav}

                else:
                    continue

    output_results = {}
    for plant, plant_data in results_plants.items():
        available_capacity_per_day = {str(date): data["available_capacity"] for date, data in plant_data.items()}
        output_results[plant] = available_capacity_per_day

    add_total(output_results)

    output_results = {plant: {str(date): power for date, power in plant_data.items()} for plant, plant_data in output_results.items()}
    output_results = pd.DataFrame(output_results)

    # -------------------------------------------------
    # Calculate the average of each column excluding the last row
    averages = output_results.iloc[:-1, :].mean()

    # Replace the last row with the calculated averages
    output_results.iloc[-1, :] = averages

    output_results = output_results.to_dict()

    def turn_total_row_to_avg(data):
        # Replace the last key of each dictionary with 'Averages'
        for key, value in data.items():
            last_key = list(value.keys())[-1]
            value['Averages'] = value.pop(last_key)

    turn_total_row_to_avg(output_results)

    json_data = json.dumps(output_results)
    # print(json_data)
    return json_data
    # -------------------------------------------------


# @st.cache_data
def get_mongodb_data(start_date, end_date, past_date):
    database_data = mongo_unavs_call(start_date, end_date, past_date)
    return database_data

# @st.cache_data
def get_nucmonitor_data(start_date, end_date, past_date):
    mongo = get_mongodb_data(start_date, end_date, past_date)
    response_nucmonitor = nuc_monitor(start_date, end_date, past_date, mongo)
    # nucmonitor_data = response_nucmonitor.json()
    # nucmonitor_json = json.loads(nucmonitor_data)
    # print(response_nucmonitor)
    df = pd.read_json(response_nucmonitor)
    return df

# @st.cache_data
def get_photodate_data(start_date, end_date, past_date):
    mongo = get_mongodb_data(start_date, end_date, past_date)
    response_nucmonitor = nuc_monitor(start_date, end_date, past_date, mongo)
    # nucmonitor_data = response_nucmonitor.json()
    # nucmonitor_json = json.loads(nucmonitor_data)
    # print(response_nucmonitor)
    df = pd.read_json(response_nucmonitor)
    return df

def run_app():

    st.title("Nucmonitor App")

    # Get user input (e.g., dates)
    start_date = st.date_input("Start Date")
    end_date = st.date_input("End Date")
    past_date = st.date_input("Cutoff Date")
    # winter_date = st.date_input("Winter Cutoff Date")

    current_date = datetime.datetime.now()
    
    with st.form("nucmonitor_form"):
        submitted = st.form_submit_button("Get Nucmonitor")

    if not submitted:
        st.write("Form not submitted")
    
    else:
        st.write("Data received from Flask:")
        df_nucmonitor = get_nucmonitor_data(start_date, end_date, current_date)
        df_photo_date = get_photodate_data(start_date, end_date, past_date)
        # df_winter_date = get_nucmonitor_data(start_date, end_date, winter_date)
        current_date_str = str(current_date.strftime('%Y-%m-%d'))
        past_date_str = str(past_date.strftime('%Y-%m-%d'))
        st.write(f"Current View Forecast at {current_date_str} (MW)")
        st.write(df_nucmonitor)  # Display DataFrame
        
        st.write(f"Past View Forecast at {past_date_str}")
        st.write(df_photo_date)

        # Get info on current forecast Nucmonitor
        st.write(f"Total Energy per Day at Current View Forecast {current_date_str} (MW)")
        
        # Remove the final row 'Total'
        df_nucmonitor_2 = df_nucmonitor.iloc[:-1, :]
        # Get the last column
        df_nucmonitor_2 = df_nucmonitor_2.iloc[:, -1]
        
        # print(df_nucmonitor_2)

        st.write(df_nucmonitor_2)

        # Get info on past date forecast Nucmonitor
        st.write(f"Total Energy per Day at Past View Forecast {past_date_str} (MW)")
        
        # Remove the final row 'Total'
        df_photo_date_2 = df_photo_date.iloc[:-1, :]
        # Get the last column
        df_photo_date_2 = df_photo_date_2.iloc[:, -1]
        
        # print(df_photo_date_2)

        st.write(df_photo_date_2)

# --------------------------------- AVERAGE EXPECTED AVAILABILITY M-1 M M+1 M+2 PIPELINE --------------------------------- #

        # Create a Table that displays the forecast of each dataframe total for two months before date and two months after
        # Filter dates for two months before and after the current date
        # Define date ranges
        # I am under the impression that I will need to use past_date for the offset
        # two_months_before = (current_date - pd.DateOffset(months=2)).strftime('%Y-%m')
        # one_month_before = (current_date - pd.DateOffset(months=1)).strftime('%Y-%m')
        # one_month_after = (current_date + pd.DateOffset(months=1)).strftime('%Y-%m')
        # two_months_after = (current_date + pd.DateOffset(months=2)).strftime('%Y-%m')

        two_months_before = (current_date - pd.DateOffset(months=2)).strftime('%Y-%m')
        one_month_before = (current_date - pd.DateOffset(months=1)).strftime('%Y-%m')
        one_month_after = (current_date + pd.DateOffset(months=1)).strftime('%Y-%m')
        two_months_after = (current_date + pd.DateOffset(months=2)).strftime('%Y-%m')

        # Assuming df is the DataFrame containing the date index and the 'Total' column

        # # Convert the index to datetime if it's not already
        # df_nucmonitor_2.index = pd.to_datetime(df_nucmonitor_2.index)
        # df_photo_date_2.index = pd.to_datetime(df_photo_date_2.index)

        # # Calculate monthly averages with date in yyyy-mm format
        # monthly_average_nucmonitor = df_nucmonitor_2.resample('M').mean()
        # monthly_average_photo_date = df_photo_date_2.resample('M').mean()

        # Convert the index to datetime if it's not already
        df_nucmonitor_2.index = pd.to_datetime(df_nucmonitor_2.index)
        df_photo_date_2.index = pd.to_datetime(df_photo_date_2.index)

        # Calculate monthly averages with date in yyyy-mm format
        monthly_average_nucmonitor = df_nucmonitor_2.resample('ME').mean()
        monthly_average_nucmonitor.index = monthly_average_nucmonitor.index.strftime('%Y-%m')

        monthly_average_photo_date = df_photo_date_2.resample('ME').mean()
        monthly_average_photo_date.index = monthly_average_photo_date.index.strftime('%Y-%m')


        # print(monthly_average_nucmonitor)
        # print(monthly_average_nucmonitor.index)
        # print(len(monthly_average_nucmonitor.index) < 5)
        if (len(monthly_average_nucmonitor.index) < 5) or (two_months_before not in monthly_average_nucmonitor.index or two_months_after not in monthly_average_nucmonitor.index):
            df_display_normal_bool = False

        else:
            # print(two_months_before, one_month_before, current_date.strftime('%Y-%m'), one_month_after, two_months_after)
            # Filter DataFrames based on date ranges
            df_nucmonitor_filtered = monthly_average_nucmonitor[
                (monthly_average_nucmonitor.index == two_months_before) |
                (monthly_average_nucmonitor.index == one_month_before) |
                (monthly_average_nucmonitor.index == current_date.strftime('%Y-%m')) |
                (monthly_average_nucmonitor.index == one_month_after) |
                (monthly_average_nucmonitor.index == two_months_after)
            ]

            df_photo_date_filtered = monthly_average_photo_date[
                (monthly_average_photo_date.index == two_months_before) |
                (monthly_average_photo_date.index == one_month_before) |
                (monthly_average_photo_date.index == current_date.strftime('%Y-%m')) |
                (monthly_average_photo_date.index == one_month_after) |
                (monthly_average_photo_date.index == two_months_after)
            ]

            # Display the filtered DataFrames
            st.write(f"Forecast at {current_date_str} (MW)")
            st.write(df_nucmonitor_filtered)
            st.write(f"Forecast at {past_date_str} (MW)")
            st.write(df_photo_date_filtered)

            current_forecast_update = df_nucmonitor_filtered.tolist()
            past_forecast_update = df_photo_date_filtered.tolist()
            delta = [current - past for current, past in zip(current_forecast_update, past_forecast_update)]

            # print('Dates:', [two_months_before, one_month_before, current_date.strftime('%Y-%m'), one_month_after, two_months_after])
            # print(f"Forecast update {current_date_str}", current_forecast_update)
            # print(f"Forecast update {past_date_str}", past_forecast_update,)
            # print('Delta', delta)

            # Create a DataFrame for display
            data_avg_expected_normal = {
                'Dates': [two_months_before, one_month_before, current_date.strftime('%Y-%m'), one_month_after, two_months_after],
                f"Forecast update {current_date_str} (MW)": current_forecast_update,
                f"Forecast update {past_date_str} (MW)": past_forecast_update,
                'Delta': delta
            }
            df_display_normal_bool = True

# --------------------------------- AVERAGE EXPECTED AVAILABILITY M-1 M M+1 M+2 PIPELINE --------------------------------- #

# --------------------------------- AVERAGE EXPECTED AVAILABILITY WINTER PIPELINE --------------------------------- #
        # Create a Table that displays the forecast of each dataframe for the Winter months (Nov, Dec, Jan, Feb, Mar)

        # Create a table that gets the forecast for winter. This involves creating a new dataframe with
        # only the winter months with the total of each day, and another dataframe with the average of each month. Each month
        # included will only be 20xx-11, 12, and 20xx+1-01, 02, 03
        
        # Define date ranges for winter months
        # winter_start_date = current_date.replace(month=11, day=1)
        # winter_end_date = (current_date.replace(year=current_date.year+1, month=3, day=31))
        winter_start = f"{current_date.year}-11"
        winter_end = f"{current_date.year+1}-03"
        winter_start_str = str(winter_start)
        winter_end_str = str(winter_end)
        # print("winter_start_str", winter_start)
        # print("winter_end_str", winter_end)
        # print("monthly_average_nucmonitor.index", monthly_average_nucmonitor.index)
        # print(monthly_average_nucmonitor.index == winter_start)
        # print(monthly_average_nucmonitor.index == winter_end)
        if monthly_average_nucmonitor.index.any() != winter_start or monthly_average_nucmonitor.index.any() != winter_end:
            df_display_winter_bool = False

        else:
            # Filter DataFrames based on winter date range
            df_nucmonitor_winter = monthly_average_nucmonitor[(monthly_average_nucmonitor.index >= winter_start_str) & (monthly_average_nucmonitor.index <= winter_end_str)]

            df_photo_date_winter = monthly_average_photo_date[(monthly_average_photo_date.index >= winter_start_str) & (monthly_average_photo_date.index <= winter_end_str)]

            # Display the forecast DataFrames for winter
            st.title("Forecast for Winter Months")
            st.write(f"Forecast for {current_date.year}-{current_date.year+1} (Nov, Dec, Jan, Feb, Mar)")
            st.write("Nucmonitor Forecast:")
            st.write(df_nucmonitor_winter)
            st.write("Photo Date Forecast:")
            st.write(df_photo_date_winter)
            
            current_winter_forecast_update = df_nucmonitor_winter.tolist()
            past_winter_forecast_update = df_photo_date_winter.tolist()
            winter_delta = [current - past for current, past in zip(current_winter_forecast_update, past_winter_forecast_update)]
            # print("current_winter_forecast_update:", current_winter_forecast_update)
            # print("past_winter_forecast_update:", past_winter_forecast_update)

            # Create a DataFrame for display
            data_avg_expected_winter = {
                'Dates': [f'Nov-{current_date.year}', f'Dec-{current_date.year}', f'Jan-{current_date.year+1}', f'Feb-{current_date.year+1}', f'Mar-{current_date.year+1}'],
                f"Forecast update {current_date_str}": current_winter_forecast_update,
                f"Forecast update {past_date_str}": past_winter_forecast_update,
                'Delta': winter_delta
            }
            # print(data_avg_expected_winter)
            df_display_winter_bool = True

# --------------------------------- AVERAGE EXPECTED AVAILABILITY WINTER PIPELINE --------------------------------- #

# --------------------------------- VISUALIZE --------------------------------- #
        if df_display_normal_bool:
            df_display_normal = pd.DataFrame(data_avg_expected_normal)
            # Display the DataFrame as a horizontal table
            st.write("Table 1. Average expected availability on the French nuclear fleet (MW) - M-1, M, M+1, M+2, M+3")
            st.table(df_display_normal)
        
        if df_display_winter_bool:
            df_display_winter = pd.DataFrame(data_avg_expected_winter)
            st.write(f"Table 2. Average expected availability on the French nuclear fleet (MW) - Winter {winter_start}/{winter_end}")
            st.table(df_display_winter)

        # Line charts of the forecasts (need to combine them so they appear in the same chart)
        st.write("Current forecast (MW)")
        st.line_chart(df_nucmonitor_2)

        st.write("Previous forecast (MW)")
        st.line_chart(df_photo_date_2)
        # Create a new dataframe out of df_nucmonitor_2 call real_avail that contains df_nucmonitor_2 up until current_date

        # Slice the DataFrame to include data up until current_date
        real_avail = df_nucmonitor_2.loc[df_nucmonitor_2.index <= current_date_str]

        # Winter forecast still not the correct one, this is just a placeholder
        # winter_forecast = df_nucmonitor_2.loc[(df_nucmonitor_2.index >= winter_start_date) & (df_nucmonitor_2.index <= winter_end_date)]
        
        # Optionally, if you want to reset the index
        # real_avail = real_avail.reset_index()
        # print(real_avail)
        st.write("Observed Historical Availability (MW)")
        st.line_chart(real_avail)

        # Combine dataframes
        # combined_df = pd.concat([df_nucmonitor_2, df_photo_date_2, real_avail, winter_forecast], axis=1)
        combined_df = pd.concat([df_nucmonitor_2, df_photo_date_2, real_avail], axis=1)

        # combined_df.columns = [f'Forecast {current_date_str}', f'Forecast {past_date_str}', 'Observed Historical Availability', f'Winter forecast {winter_start}/{winter_end}']
        combined_df.columns = [f'Forecast {current_date_str} (MW)', f'Forecast {past_date_str} (MW)', 'Observed Historical Availability (MW)']

        # print(combined_df)
        st.write(f"Graph 1. {start_date} to {end_date} (MW)")
        st.line_chart(combined_df)

        # Add a download button
        # Create a BytesIO object to hold the Excel data

        excel_buffer = io.BytesIO()

    
        current_datetime = datetime.datetime.now()
        current_year = current_datetime.strftime('%Y')
        current_month = current_datetime.strftime('%m')
        current_day = current_datetime.strftime('%d')
        current_hour = current_datetime.strftime('%H')
        current_minute = current_datetime.strftime('%M')
        current_second = current_datetime.strftime('%S')


        # Save the DataFrame to the BytesIO object as an Excel file
        df_nucmonitor.to_excel(excel_buffer, index=True)
        # Set the cursor position to the beginning of the BytesIO object
        excel_buffer.seek(0)

        # Provide the BytesIO object to the download button
        download_button = st.download_button(
            label="Download Excel",
            data=excel_buffer,
            file_name=f"nucmonitor_data_{current_year}-{current_month}-{current_day}-h{current_hour}m{current_minute}s{current_second}.xlsx",
            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
        )


if __name__ == '__main__':
    run_app()