import os

import json

from dotenv import load_dotenv

import pymongo

import pandas as pd

import plotly.express as px

import matplotlib.pyplot as plt

load_dotenv()

username = os.environ.get("USERNAME")

password = os.environ.get("PASSWORD")

client = pymongo.MongoClient(f"mongodb+srv://{username}:{password}@cluster0.lxsbb.mongodb.net/?retryWrites=true&w=majority")

db = client.medical_record

collection = db["report"]

a = collection.find({})

b = list(a)

patient = {

    k:[] for k in b[0]["demographics"].keys()

}

patient["patient_id"] = []

for d in b:

    patient["patient_id"].append(d["_id"]),

    for k, v in d["demographics"].items():

        patient[k].append(v)

df_patient = pd.DataFrame(patient)

fig_patient_gender = px.bar(

    x=df_patient["gender"].value_counts().index,

    y=df_patient["gender"].value_counts(),

)

fig_patient_gender.update_layout(

    title="Distribution of Gender",

    xaxis_title="Gender",

    yaxis_title="Frequency",

)

fig_patient_race = px.bar(

    x=df_patient["race"].value_counts().index,

    y=df_patient["race"].value_counts()

)

fig_patient_race.update_layout(

    title="Distribution of race",

    xaxis_title="Race",

    yaxis_title="Frequency",

)

allergies = {

    k:[] for k in b[0]["allergies"][0].keys()

}

allergies["patient_id"] = []

for d in b:

    if d["allergies"]:

        for allergy in d["allergies"]:

            allergies["patient_id"].append(d["_id"])

            for k, v in allergy.items():

                allergies[k].append(v)

    else:

        allergies["patient_id"].append(d["_id"])

        keys = list(allergies.keys())[:-1]

        for k in keys:

            allergies[k].append(None)

df_allergy = pd.DataFrame(allergies)

allergic = df_allergy.groupby("patient_id")["to"].count()

allergic = allergic.gt(0).replace({True:"allergic", False:"not allergic"})

fig_allergy = px.pie(

    values=allergic.value_counts(),

    names=allergic.value_counts().index,

)

fig_allergy.update_layout(

    title="Percentage of Allergic and Non-allergic patients Patients",

)

fig_allergy_type = px.bar(

    x=df_allergy["type"].value_counts().index,

    y=df_allergy["type"].value_counts(),

)

fig_allergy_type.update_layout(

    title="Different types of allergies",

    xaxis_title="Types of allergies",

    yaxis_title="Number of patients",

)

conditions = {

     k:[] for k in b[0]["conditions"][0].keys()

}

conditions["patient_id"] = []

for d in b:

    if d["conditions"]:

        for condition in d["conditions"]:

            conditions["patient_id"].append(d["_id"])

            for k, v in condition.items():

                conditions[k].append(v)

    else:

        conditions["patient_id"].append(d["_id"])

        keys = list(conditions.keys())[:-1]

        for k in keys:

            conditions[k].append(None)

df_conditions = pd.DataFrame(conditions)

top_10_conditions = df_conditions["condition"].value_counts().head(10)

# top_10_conditions.plot(

#     kind="bar",

#     title="Top ten conditions of patients",

#     xlabel="Conditions",

#     ylabel="Number of patients"

# );

fig_top_conditions = px.bar(

    x=top_10_conditions.index,

    y=top_10_conditions,

)

fig_top_conditions.update_layout(

    title="Top ten conditions of patients",

    xaxis_title="Different Conditions",

    yaxis_title="Number of patients",

)

df_demographic_condition = pd.merge(

    df_patient, df_conditions, how='inner', on=["patient_id"]

)[["gender", "condition"]]

top_conds = top_10_conditions.index

df_demographic_condition["gender"] = df_demographic_condition["gender"].replace({"M": "male", "F": "female"})

cond_gender = {k:{"male":0, "female":0} for k in top_conds}

for index, row in df_demographic_condition.iterrows():

    if row["condition"] in top_conds:

        cond_gender[row["condition"]][row["gender"]] += 1

df_cond_gender = pd.DataFrame(cond_gender).T

fig_cond_gender = px.bar(

    df_cond_gender,

    barmode='group'

)

fig_cond_gender.update_layout(

    title="Top ten conditions of patients distributed among genders",

    xaxis_title="Different Conditions",

    yaxis_title="Number of patients",

)

immunizations = {

    k:[] for k in b[0]["immunization"][0].keys()

}

immunizations["patient_id"] = []

for d in b:

    if d["immunization"]:

        for imm in d["immunization"]:

            immunizations["patient_id"].append(d["_id"])

            for k, v in imm.items():

                immunizations[k].append(v)

    else:

        immunizations["patient_id"].append(d["_id"])

        keys = list(immunizations.keys())[:-1]

        for k in keys:

            immunizations[k].append(None)

df_imm = pd.DataFrame(immunizations)

df_imm["date"] = pd.to_datetime(df_imm['date'])

df_imm["immunization"].value_counts()

df_imm_covid = df_imm[df_imm["immunization"].str.contains("COVID")==True].sort_values(by='date')

df_imm["month_year"] = df_imm_covid["date"].dt.to_period('M')

covid_21_22 = df_imm.groupby("month_year")["immunization"].count()

fig_covid_21_22 = px.line(

    x=covid_21_22.index.strftime("%Y-%m"),

    y=covid_21_22

)

fig_covid_21_22.update_layout(

    title="Time plot of COVID vaccination from Jan 2021 to June 2022",

    xaxis_title="Month and Year of vaccination",

    yaxis_title="Number of patients",

)

# covid_21_22.plot(

#     title="Time plot of COVID vaccination from Jan 2021 to June 2022",

#     xlabel="Time",

#     ylabel="Frequency of vaccination"

# );