app3.txt

import uvicorn
import base64
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import random
from statsmodels.stats.power import TTestIndPower
from collections import Counter
from fastapi import FastAPI
from simulation import simulation
#from power_analysis import power_analysis
from fastapi.responses import StreamingResponse,HTMLResponse
from fastapi import FastAPI, UploadFile,File
from pydantic import BaseModel
import io 
import requests
#from flask import Flask, request
app = FastAPI()


@app.get('/')
def index():
    return {'message': 'Hello, World'}

@app.post('/simulate_dataset')
def create_dataset2(data:simulation):
  
  """
  User Defined Inputs:
  1.size=Population Sample size
  2.percentage_alcoholism/depression/tobacco=Percentage of the the respective condition in the population
  3.percentage_alcoholism_depression(and other dual variable overlaps)= Percentage of the overlap of respective two condtions in the populations
  4.percentage_tobacco_alcoholism_depression= Percentage of the overlap of all 3 cases in the population
  5.age, gender, bmi, edu: A list consisting of the percentages of distribution in the various categorical buckets for the following variables:
  eg; age=[18,60,35,15]:min,max,mean,std dev 
  age:4 elements; gender: 2 buckets; bmi: 3 buckets; edu: 4 buckets
  6.Treatment_noth/treatment_1_conditions=The percentage of good treatment outcomes for the population with the following number of conditions
  """
  print(type(data.dict))
  try:
    data= data.dict()
    size = data['size']
    percentage_alc_only = data['percentage_alc_only']
    percentage_dep_only = data['percentage_dep_only'] 
    percentage_tobacco_only = data['percentage_tobacco_only']
    percentage_alc_dep = data['percentage_alc_dep']
    percentage_alc_tobacco = data['percentage_alc_tobacco']
    percentage_dep_tobacco = data['percentage_dep_tobacco'] 
    percentage_tobacco_alcoholism_depression = data['percentage_tobacco_alcoholism_depression']
    treatment_noth = data['treatment_noth']
    treatment_1_conditions = data['treatment_1_conditions']
    treatment_2_conditions = data['treatment_2_conditions']
    treatment_3_conditions = data['treatment_3_conditions']
    male= data['male']
    female=data['female'] 
    low_bmi=data['low_bmi']
    normal_bmi=data['normal_bmi'] 
    high_bmi=data['high_bmi'] 
    l1_edu=data['l1_edu']
    l2_edu=data['l2_edu']
    l3_edu=data['l3_edu']
    l4_edu=data['l4_edu']
    min_age=data['min_age']
    max_age=data['max_age']
    mean_age=data['mean_age']
    sd_age=data['sd_age']
    seed=data['seed']
    gender=list()
    bmi=list()
    age=list()
    edu=list()
    gender.extend([male,female])
    bmi.extend([low_bmi,normal_bmi,high_bmi])
    edu.extend([l1_edu,l2_edu,l3_edu,l4_edu])
    age.extend([min_age,max_age,mean_age,sd_age])
    zeros=1-(percentage_alc_only + percentage_dep_only  + percentage_tobacco_only + percentage_alc_dep + percentage_alc_tobacco + percentage_dep_tobacco + percentage_tobacco_alcoholism_depression)
    
    print("zeros:{}".format(zeros))
    np.random.seed(2020)
    condition=np.random.choice(8,size,p=[zeros, percentage_alc_only, percentage_dep_only,percentage_alc_dep,percentage_tobacco_only,percentage_alc_tobacco, percentage_dep_tobacco, percentage_tobacco_alcoholism_depression])
    alcoholism=np.where((condition == 1) | (condition==3) | (condition==5) | (condition==7),1,0)
    depression=np.where((condition == 2) | (condition==3) | (condition==6) | (condition==7),1,0)
    
    alc_only=np.where(condition == 1,1,0)
    dep_only=np.where(condition == 2,1,0)
    tobacco_only=np.where(condition == 4,1,0)

    alc_dep=np.where(condition == 3,1,0)
    alc_tobacco=np.where(condition == 5,1,0)
    dep_tobacco=np.where(condition == 6,1,0)

    tobacco=np.where((condition == 4) | (condition==5) | (condition==6) | (condition==7),1,0)
    all_three=np.where(condition == 7,1,0)
    age_1= np.random.normal(loc=age[2], scale=age[3], size=size)
    age_1=np.clip(age_1, a_min=age[0], a_max=age[1])
    age_1 = np.round(age_1).astype(int)
    sex=np.random.choice(2,size,p=[gender[0],gender[1]])
    body_mass=np.random.choice(3,size,p=[bmi[0],bmi[1],bmi[2]])
    education=np.random.choice(4,size,p=[edu[0],edu[1],edu[2],edu[3]])
    cavitation = np.random.choice(2,size,p=[0.5,0.5])
    ttd = np.random.normal(loc=7, scale=3, size=size)
    df = pd.DataFrame(
      {
          'idx': np.arange(1, size+1),
          'age': age_1,
          'gender': sex,
          'bmi': body_mass,
          'education': education,
          'cavitation': cavitation,
          'TTD': ttd,
          'alcoholism': alcoholism,
          'depression': depression,
          'tobacco': tobacco,
          'alcohol_only':alc_only,
          'depression_only':dep_only,
          'tobacco_only': tobacco_only,
          'alcoholism+depression':alc_dep,
          'alcoholism+tobacco':alc_tobacco,
          'depression+tobacco':dep_tobacco,
          'tobacco+alcohol+smoking':all_three
      }
    )
    intervention_arr=[]
    choices_alc_only=['NAlc','A']
    choices_dep_only=['ND','D']
    choices_tobacco_only=['NT','T']
    choices_alc_dep_only=['NAD','AD']
    choices_alc_tobacco_only=['NAT','AT']
    choices_dep_tobacco_only=['NDT','DT']
    choices_all_3=['NADT','ADT']
    random.seed(seed)
    weights=[0.5,0.5]
    for i in range(size):
      if(df['alcohol_only'][i]==1):
        #intervention_arr.append(np.random.binomial(1,0.5,size=1))
        intervention_arr.append(random.choices(choices_alc_only,weights=weights)[0])
      if(df['depression_only'][i]==1):
        intervention_arr.append(random.choices(choices_dep_only,weights=weights)[0])
      if(df['tobacco_only'][i]==1):
        intervention_arr.append(random.choices(choices_tobacco_only,weights=weights)[0])
      if(df['alcoholism+depression'][i]==1):
        intervention_arr.append(random.choices(choices_alc_dep_only,weights=weights)[0])
      if(df['alcoholism+tobacco'][i]==1):
        intervention_arr.append(random.choices(choices_alc_tobacco_only,weights=weights)[0])
      if(df['depression+tobacco'][i]==1):
        intervention_arr.append(random.choices(choices_dep_tobacco_only,weights=weights)[0])
      if(df['tobacco+alcohol+smoking'][i]==1):
        intervention_arr.append(random.choices(choices_all_3,weights=weights)[0])
      if(df['alcoholism'][i]==0 and df['depression'][i]==0 and df['tobacco'][i]==0):
        intervention_arr.append('UNAFFECTED')
    df['Intervention'] = intervention_arr
    #print(np.unique(intervention_arr,return_councentage_ts=True))
    df['treatment_outcomes'] = " "
    treatment_outcomes_single_ni = []
    treatment_outcomes_two_ni = []
    treatment_outcomes_three_ni = []
    treatment_outcomes_i = []
    #treatment_outcomes_noth = []

    list_noth = list(np.where(df['Intervention'] == 'UNAFFECTED')[0])
    values_noth = np.random.choice(2,len(list_noth),p=[1-treatment_noth,treatment_noth])
    for i in range(len(list_noth)):
      df.loc[list_noth[i],"treatment_outcomes"] = values_noth[i]
    
    list_single_ni = list(np.where((df['Intervention'] == 'NAlc') | (df['Intervention'] == 'ND') | (df['Intervention'] == 'NT'))[0])
    values_single_ni = np.random.choice(2,len(list_single_ni),p=[1-treatment_1_conditions,treatment_1_conditions])
    for i in range(len(list_single_ni)):
      df.loc[list_single_ni[i],"treatment_outcomes"] = values_single_ni[i]

    list_two_ni = list(np.where((df['Intervention'] == 'NAD') | (df['Intervention'] == 'NDT') | (df['Intervention'] == 'NAT'))[0])
    values_two_ni = np.random.choice(2,len(list_two_ni),p=[1-treatment_2_conditions,treatment_2_conditions])
    for i in range(len(list_two_ni)):
      df.loc[list_two_ni[i],"treatment_outcomes"] = values_two_ni[i]

    list_three_ni = list(np.where(df['Intervention'] == 'NADT')[0])
    values_three_ni = np.random.choice(2,len(list_three_ni),p=[1-treatment_3_conditions,treatment_3_conditions])
    for i in range(len(list_three_ni)):
      df.loc[list_three_ni[i],"treatment_outcomes"] = values_three_ni[i]    
    list_single_inter=list(np.where((df['Intervention'] == 'A') | (df['Intervention'] == 'D') | (df['Intervention'] == 'T'))[0])
    s_int=(1-treatment_1_conditions)/2
    values_single_inter = np.random.choice(2,len(list_single_inter),p=[s_int,treatment_1_conditions+s_int])
    for i in range(len(list_single_inter)):
      df.loc[list_single_inter[i],"treatment_outcomes"] = values_single_inter[i]

    list_double_inter=list(np.where((df['Intervention'] == 'AD') | (df['Intervention'] == 'DT') | (df['Intervention'] == 'AT'))[0])
    d_int=(1-treatment_2_conditions)/2
    values_double_inter = np.random.choice(2,len(list_double_inter),p=[d_int,treatment_2_conditions + d_int])
    for i in range(len(list_double_inter)):
      df.loc[list_double_inter[i],"treatment_outcomes"] = values_double_inter[i]

    list_triple_inter=list(np.where((df['Intervention'] == 'ADT'))[0])
    t_int = (1-treatment_3_conditions)/2
    values_triple_inter = np.random.choice(2,len(list_triple_inter),p=[t_int,treatment_3_conditions+ t_int])
    for i in range(len(list_triple_inter)):
      df.loc[list_triple_inter[i],"treatment_outcomes"] = values_triple_inter[i]


    return StreamingResponse(
        iter([df.to_csv(index=False)]),
        media_type="text/csv",
        headers={"Content-Disposition": f"attachment; filename=init_dataset.csv"})
  except Exception as ex:
    print(ex)

def create_dataset(size,percentage_alc_only, percentage_dep_only, percentage_tobacco_only,percentage_alc_dep, percentage_alc_tobacco,percentage_dep_tobacco, 
                    percentage_tobacco_alcoholism_depression,treatment_noth,treatment_1_conditions,treatment_2_conditions,treatment_3_conditions,
                    gender,bmi,edu,seed,age):
  """
  User Defined Inputs:
  1.size=Population Sample size
  2.percentage_alcoholism/depression/tobacco=Percentage of the the respective condition in the population
  3.percentage_alcoholism_depression(and other dual variable overlaps)= Percentage of the overlap of respective two condtions in the populations
  4.percentage_tobacco_alcoholism_depression= Percentage of the overlap of all 3 cases in the population
  5.gender, bmi, edu: A list consisting of the percentages of distribution in the various categorical buckets for the following variables:
  eg;  
  buckets; gender: 2 buckets; bmi: 3 buckets; edu: 4 buckets
  6.age has 4 parameters - a_min, a_max, mean, std dev
  6.Treatment_noth/treatment_1_conditions=The percentage of good treatment outcomes for the population with the following number of conditions
  """
  zeros=1-(percentage_alc_only + percentage_dep_only  + percentage_tobacco_only + percentage_alc_dep + percentage_alc_tobacco + percentage_dep_tobacco + percentage_tobacco_alcoholism_depression)
  
  print("zeros:{}".format(zeros))
  np.random.seed(2020)
  condition=np.random.choice(8,size,p=[zeros, percentage_alc_only, percentage_dep_only,percentage_alc_dep,percentage_tobacco_only,percentage_alc_tobacco, percentage_dep_tobacco, percentage_tobacco_alcoholism_depression])
  alcoholism=np.where((condition == 1) | (condition==3) | (condition==5) | (condition==7),1,0)
  depression=np.where((condition == 2) | (condition==3) | (condition==6) | (condition==7),1,0)
  
  alc_only=np.where(condition == 1,1,0)
  dep_only=np.where(condition == 2,1,0)
  tobacco_only=np.where(condition == 4,1,0)

  alc_dep=np.where(condition == 3,1,0)
  alc_tobacco=np.where(condition == 5,1,0)
  dep_tobacco=np.where(condition == 6,1,0)

  tobacco=np.where((condition == 4) | (condition==5) | (condition==6) | (condition==7),1,0)
  all_three=np.where(condition == 7,1,0)
  age_1= np.random.normal(loc=age[2], scale=age[3], size=size)
  age_1=np.clip(age_1, a_min=age[0], a_max=age[1])
  age_1 = np.round(age_1).astype(int)
  sex=np.random.choice(2,size,p=[gender[0],gender[1]])
  body_mass=np.random.choice(3,size,p=[bmi[0],bmi[1],bmi[2]])
  education=np.random.choice(4,size,p=[edu[0],edu[1],edu[2],edu[3]])
  cavitation = np.random.choice(2,size,p=[0.5,0.5])
  ttd = np.random.normal(loc=7, scale=3, size=size)
  df = pd.DataFrame(
    {
        'idx': np.arange(1, size+1),
        'age': age_1,
        'gender': sex,
        'bmi': body_mass,
        'education': education,
        'cavitation': cavitation,
      	'TTD': ttd,
        'alcoholism': alcoholism,
        'depression': depression,
        'tobacco': tobacco,
        'alcohol_only':alc_only,
        'depression_only':dep_only,
        'tobacco_only': tobacco_only,
        'alcoholism+depression':alc_dep,
        'alcoholism+tobacco':alc_tobacco,
        'depression+tobacco':dep_tobacco,
        'tobacco+alcohol+smoking':all_three
     }
  )
  intervention_arr=[]
  choices_alc_only=['NAlc','A']
  choices_dep_only=['ND','D']
  choices_tobacco_only=['NT','T']
  choices_alc_dep_only=['NAD','AD']
  choices_alc_tobacco_only=['NAT','AT']
  choices_dep_tobacco_only=['NDT','DT']
  choices_all_3=['NADT','ADT']
  random.seed(seed)
  weights=[0.5,0.5]
  for i in range(size):
    if(df['alcohol_only'][i]==1):
      #intervention_arr.append(np.random.binomial(1,0.5,size=1))
      intervention_arr.append(random.choices(choices_alc_only,weights=weights)[0])
    if(df['depression_only'][i]==1):
      intervention_arr.append(random.choices(choices_dep_only,weights=weights)[0])
    if(df['tobacco_only'][i]==1):
      intervention_arr.append(random.choices(choices_tobacco_only,weights=weights)[0])
    if(df['alcoholism+depression'][i]==1):
      intervention_arr.append(random.choices(choices_alc_dep_only,weights=weights)[0])
    if(df['alcoholism+tobacco'][i]==1):
      intervention_arr.append(random.choices(choices_alc_tobacco_only,weights=weights)[0])
    if(df['depression+tobacco'][i]==1):
      intervention_arr.append(random.choices(choices_dep_tobacco_only,weights=weights)[0])
    if(df['tobacco+alcohol+smoking'][i]==1):
      intervention_arr.append(random.choices(choices_all_3,weights=weights)[0])
    if(df['alcoholism'][i]==0 and df['depression'][i]==0 and df['tobacco'][i]==0):
      intervention_arr.append('UNAFFECTED')
  df['Intervention'] = intervention_arr
  #print(np.unique(intervention_arr,return_councentage_ts=True))
  df['treatment_outcomes'] = " "
  treatment_outcomes_single_ni = []
  treatment_outcomes_two_ni = []
  treatment_outcomes_three_ni = []
  treatment_outcomes_i = []
  #treatment_outcomes_noth = []

  list_noth = list(np.where(df['Intervention'] == 'UNAFFECTED')[0])
  values_noth = np.random.choice(2,len(list_noth),p=[1-treatment_noth,treatment_noth])
  for i in range(len(list_noth)):
    df.loc[list_noth[i],"treatment_outcomes"] = values_noth[i]
  
  list_single_ni = list(np.where((df['Intervention'] == 'NAlc') | (df['Intervention'] == 'ND') | (df['Intervention'] == 'NT'))[0])
  values_single_ni = np.random.choice(2,len(list_single_ni),p=[1-treatment_1_conditions,treatment_1_conditions])
  for i in range(len(list_single_ni)):
    df.loc[list_single_ni[i],"treatment_outcomes"] = values_single_ni[i]

  list_two_ni = list(np.where((df['Intervention'] == 'NAD') | (df['Intervention'] == 'NDT') | (df['Intervention'] == 'NAT'))[0])
  values_two_ni = np.random.choice(2,len(list_two_ni),p=[1-treatment_2_conditions,treatment_2_conditions])
  for i in range(len(list_two_ni)):
    df.loc[list_two_ni[i],"treatment_outcomes"] = values_two_ni[i]

  list_three_ni = list(np.where(df['Intervention'] == 'NADT')[0])
  values_three_ni = np.random.choice(2,len(list_three_ni),p=[1-treatment_3_conditions,treatment_3_conditions])
  for i in range(len(list_three_ni)):
    df.loc[list_three_ni[i],"treatment_outcomes"] = values_three_ni[i]

  #list_i = list(np.where((df['Intervention'] == 'A') | (df['Intervention'] == 'D') | (df['Intervention'] == 'T') | (df['Intervention'] == 'AD') | (df['Intervention'] == 'AT') | (df['Intervention'] == 'DT') | (df['Intervention'] == 'ADT'))[0])
  #values_i = np.random.choice(2,len(list_i),p=[1-treatment_intervention,treatment_intervention])
  #for i in range(len(list_i)):
  # df.loc[list_i[i],"treatment_outcomes"] = values_i[i]
  
  list_single_inter=list(np.where((df['Intervention'] == 'A') | (df['Intervention'] == 'D') | (df['Intervention'] == 'T'))[0])
  s_int=(1-treatment_1_conditions)/2
  values_single_inter = np.random.choice(2,len(list_single_inter),p=[s_int,treatment_1_conditions+s_int])
  for i in range(len(list_single_inter)):
    df.loc[list_single_inter[i],"treatment_outcomes"] = values_single_inter[i]

  list_double_inter=list(np.where((df['Intervention'] == 'AD') | (df['Intervention'] == 'DT') | (df['Intervention'] == 'AT'))[0])
  d_int=(1-treatment_2_conditions)/2
  values_double_inter = np.random.choice(2,len(list_double_inter),p=[d_int,treatment_2_conditions + d_int])
  for i in range(len(list_double_inter)):
    df.loc[list_double_inter[i],"treatment_outcomes"] = values_double_inter[i]

  list_triple_inter=list(np.where((df['Intervention'] == 'ADT'))[0])
  t_int = (1-treatment_3_conditions)/2
  values_triple_inter = np.random.choice(2,len(list_triple_inter),p=[t_int,treatment_3_conditions+ t_int])
  for i in range(len(list_triple_inter)):
    df.loc[list_triple_inter[i],"treatment_outcomes"] = values_triple_inter[i]

  return df


#@app.post('/calc_power')
def calculate_power(x:str,y:str,file:UploadFile=File(...)):
    """ x = value of treatment variable (gives us idea of the population - NAlc, a, nt, t, adt, etc)
        y = value of control variable """
    try:
        df = pd.read_csv(file.file)
        file.file.close
        #print(df.head())
        power_analysis=TTestIndPower()
        treatment_arr=[]
        control_arr=[]
        treatment_locs=np.where((df['Intervention']==x))
        control_locs=np.where((df['Intervention']==y))
        for i in treatment_locs:
            treatment_arr.append(df['treatment_outcomes'].iloc[i])
        for j in control_locs:
            control_arr.append(df['treatment_outcomes'].iloc[j])
        l1 = len(treatment_arr[0])
        l2 = len(control_arr[0])
        index_treatment=np.arange(0,l1)
        index_control=np.arange(0,l2)
        treatment_df=pd.DataFrame({'idx':index_treatment,"Treatment":treatment_arr[0]})
        control_df=pd.DataFrame({'idx':index_control,"Control":control_arr[0]}) 
        mu1=treatment_df['Treatment'].mean()
        mu2=control_df['Control'].mean()
        std1=treatment_df['Treatment'].std()
        std2=control_df['Control'].std()
        s = np.sqrt(((l1 - 1) * std1 + (l2 - 1) * std2) / (l1 + l2 - 2))
        d = (mu1 - mu2) / s #cohen's effect size
        eff = round(d,2)
        p = power_analysis.power(effect_size=eff,alpha=0.05,nobs1=l1,ratio=(l1/l2),alternative='two-sided')
        return p
    except Exception as ex:
      print(ex) 

def calculate_power1(x, y, df):
    power_analysis=TTestIndPower()
    treatment_arr=[]
    control_arr=[]
    treatment_locs=np.where((df['Intervention']==x))
    control_locs=np.where((df['Intervention']==y))
    for i in treatment_locs:
        treatment_arr.append(df['treatment_outcomes'].iloc[i])
    for j in control_locs:
        control_arr.append(df['treatment_outcomes'].iloc[j])
    l1 = len(treatment_arr[0])
    l2 = len(control_arr[0])
    index_treatment=np.arange(0,l1)
    index_control=np.arange(0,l2)
    treatment_df=pd.DataFrame({'idx':index_treatment,"Treatment":treatment_arr[0]})
    control_df=pd.DataFrame({'idx':index_control,"Control":control_arr[0]}) 
    mu1=treatment_df['Treatment'].mean()
    mu2=control_df['Control'].mean()
    std1=treatment_df['Treatment'].std()
    std2=control_df['Control'].std()
    s = np.sqrt(((l1 - 1) * std1 + (l2 - 1) * std2) / (l1 + l2 - 2))
    d = (mu1 - mu2) / s #cohen's effect size
    eff = round(d,2)
    p = power_analysis.power(effect_size=eff,alpha=0.05,nobs1=l1,ratio=(l1/l2),alternative='two-sided')
    return p

@app.post('/check_power')
async def checks_power(file:UploadFile=File(...)):
    ''' Calculates the power for each risk factor group in the population '''
    try:
        dfx = pd.read_csv(file.file)
        file.file.close
        sample_sizes={}
        sample_sizes['Alcohol']=calculate_power1('A','NAlc',dfx)
        sample_sizes['Depression']=calculate_power1('D','ND',dfx)  
        sample_sizes['Tobacco']=calculate_power1('T','NT',dfx)  
        sample_sizes['Alcohol-Depression']=calculate_power1('AD','NAD',dfx)  
        sample_sizes['Alcohol-Tobacco']=calculate_power1('AT','NAT',dfx)  
        sample_sizes['Depression-Tobacco']=calculate_power1('DT','NDT',dfx) 
        sample_sizes['Alcohol-Depression-Tobacco']=calculate_power1('ADT','NADT',dfx) 
        #for i in sample_sizes.values():
        all_above_threshold = all(value >= 0.8 for value in sample_sizes.values())
        print(all_above_threshold)
        print(sample_sizes.values())
        return {'Current Power for groups':list(sample_sizes.items())}
    except Exception as ex:
      print(ex) 
# ch
# eck_sample_sizes(df_2)

@app.get("/find_ideal_samples", response_class=HTMLResponse)
async def get_links():
    urls = [
    "https://colab.research.google.com/drive/1CUXdWtmOlpdMTCxPU-l-p80N2H4K-KFh?usp=sharing",
    "https://drive.google.com/drive/folders/1AsRdkL3UpydRKNGkUqwth0yZia4P9_1K?usp=sharing",
    ]
    website_contents = ""
    for url in urls:
        website_contents += f"<a href='{url}' style='color: blue;'>{url}</a><br><br>"
    return website_contents

def try_sample_sizes3(dfx):
    sample_sizes = {}
    sample_sizes['Alcohol'] = calculate_power1('A', 'NAlc', dfx)
    sample_sizes['Depression'] = calculate_power1('D', 'ND', dfx)
    sample_sizes['Tobacco'] = calculate_power1('T', 'NT', dfx)
    sample_sizes['Alcohol-Depression'] = calculate_power1('AD', 'NAD', dfx)
    sample_sizes['Alcohol-Tobacco'] = calculate_power1('AT', 'NAT', dfx)
    sample_sizes['Depression-Tobacco'] = calculate_power1('DT', 'NDT', dfx)
    sample_sizes['Alcohol-Depression-Tobacco'] = calculate_power1('ADT', 'NADT', dfx)

    size = dfx.shape[0]
    #total_power = sum(sample_sizes.values())
    print(sample_sizes.values())
    all_above_threshold = all(value >= 0.79 for value in sample_sizes.values())
    print(all_above_threshold)
    #print(type(dfx))
    print(type(dfx.shape[0]))
    if not all_above_threshold:
        if sample_sizes['Alcohol'] < 0.79:
            size += 100
        elif sample_sizes['Depression'] < 0.79:
            size += 100
        elif sample_sizes['Tobacco'] < 0.79:
            size += 100
        elif sample_sizes['Alcohol-Depression'] < 0.79:
            size += 80
        elif sample_sizes['Alcohol-Tobacco'] < 0.79:
            size += 80
        elif sample_sizes['Depression-Tobacco'] < 0.79:
            size += 80
        elif sample_sizes['Alcohol-Depression-Tobacco'] < 0.79:
            size += 20
          
        ratio_alc_only = round(dfx['Intervention'].value_counts(normalize=True)['A']+dfx['Intervention'].value_counts(normalize=True)['NAlc'],2)
        ratio_dep_only = round(dfx['Intervention'].value_counts(normalize=True)['D']+dfx['Intervention'].value_counts(normalize=True)['ND'],2)
        ratio_tob_only = round(dfx['Intervention'].value_counts(normalize=True)['T']+dfx['Intervention'].value_counts(normalize=True)['NT'],2)
        ratio_at = round(dfx['Intervention'].value_counts(normalize=True)['AT']+dfx['Intervention'].value_counts(normalize=True)['NAT'],2)
        ratio_ad = round(dfx['Intervention'].value_counts(normalize=True)['AD']+dfx['Intervention'].value_counts(normalize=True)['NAD'],2)
        ratio_dt = round(dfx['Intervention'].value_counts(normalize=True)['DT']+dfx['Intervention'].value_counts(normalize=True)['NDT'],2)
        ratio_adt = round(dfx['Intervention'].value_counts(normalize=True)['ADT']+dfx['Intervention'].value_counts(normalize=True)['NADT'],2)
        
        #print(dfx['Intervention'].value_counts(normalize=True))
        print(dfx.shape[0])
        #dfx = create_dataset2(size, 0.08, 0.08, 0.08, 0.04, 0.04, 0.04, 0.03, 0.9, 0.80, 0.70, 0.60, gender=[0.5, 0.5], bmi=[0.2,0.5,0.3],edu=[0.1,0.2,0.2,0.5],seed=52)
        print('inside growing')
        dfx = create_dataset(size, ratio_alc_only, ratio_dep_only, ratio_tob_only, ratio_ad, ratio_at, ratio_dt, ratio_adt, 0.95, 0.90, 0.85, 0.80, gender=[0.5, 0.5], bmi=[0.2,0.5,0.3],edu=[0.1,0.2,0.2,0.5],age=[18,60,35,15],seed=52)
        dfx = try_sample_sizes3(dfx)
    while all(value > 0.85 for value in sample_sizes.values()):
            print('inside shrinking')
                #for group in sample_sizes.keys:
                    #if sample_sizes[group] > 0.95:
                    #   size -= 1500
            if sample_sizes['Alcohol'] > 0.95:
                size -= 1500 
            elif sample_sizes['Tobacco'] > 0.95:
                size -= 1500
            elif sample_sizes['Depression'] > 0.95:
                size -= 1500
            ratio_alc_only = round(dfx['Intervention'].value_counts(normalize=True)['A']+dfx['Intervention'].value_counts(normalize=True)['NAlc'],2)
            ratio_dep_only = round(dfx['Intervention'].value_counts(normalize=True)['D']+dfx['Intervention'].value_counts(normalize=True)['ND'],2)
            ratio_tob_only = round(dfx['Intervention'].value_counts(normalize=True)['T']+dfx['Intervention'].value_counts(normalize=True)['NT'],2)
            ratio_at = round(dfx['Intervention'].value_counts(normalize=True)['AT']+dfx['Intervention'].value_counts(normalize=True)['NAT'],2)
            ratio_ad = round(dfx['Intervention'].value_counts(normalize=True)['AD']+dfx['Intervention'].value_counts(normalize=True)['NAD'],2)
            ratio_dt = round(dfx['Intervention'].value_counts(normalize=True)['DT']+dfx['Intervention'].value_counts(normalize=True)['NDT'],2)
            ratio_adt = round(dfx['Intervention'].value_counts(normalize=True)['ADT']+dfx['Intervention'].value_counts(normalize=True)['NADT'],2)
                
            dfx = create_dataset(size, ratio_alc_only, ratio_dep_only, ratio_tob_only, ratio_ad, ratio_at, ratio_dt, ratio_adt, 0.95, 0.90, 0.85, 0.80, gender=[0.5, 0.5], bmi=[0.2,0.5,0.3],edu=[0.1,0.2,0.2,0.5],age=[18,60,35,15],seed=52)
            #dfx = create_dataset2(size, 0.08, 0.08, 0.08, 0.04, 0.04, 0.04, 0.03, 0.9, 0.80, 0.70, 0.60, gender=[0.5, 0.5], bmi=[0.2,0.5,0.3],edu=[0.1,0.2,0.2,0.5],seed=52)
            dfx = try_sample_sizes3(dfx)
    return dfx

#@app.post('/try_samp_sizes')
def process_file(file:UploadFile = File(...)):
  try:
      dfx=pd.read_csv(file.file)
      dfx1 = try_sample_sizes3(dfx)
      #csvx = dfx.to_csv(index = False)    
      return StreamingResponse(
                    iter([dfx1.to_csv(index=False)]),
                    media_type="text/csv",
                    headers={"Content-Disposition": f"attachment; filename=resim_screening.csv"})
  except Exception as ex:
      print(ex)

def try_sample_sizes4(file:UploadFile=File(...)):
    try:
        dfx = pd.read_csv(file.file)
        file.file.close
        #dfx = pd.read_csv(io.BytesIO(contents), encoding='utf-8')
        #file.file.close
        sample_sizes = {}
        sample_sizes['Alcohol'] = calculate_power1('A', 'NAlc', dfx)
        sample_sizes['Depression'] = calculate_power1('D', 'ND', dfx)
        sample_sizes['Tobacco'] = calculate_power1('T', 'NT', dfx)
        sample_sizes['Alcohol-Depression'] = calculate_power1('AD', 'NAD', dfx)
        sample_sizes['Alcohol-Tobacco'] = calculate_power1('AT', 'NAT', dfx)
        sample_sizes['Depression-Tobacco'] = calculate_power1('DT', 'NDT', dfx)
        sample_sizes['Alcohol-Depression-Tobacco'] = calculate_power1('ADT', 'NADT', dfx)

        size = dfx.shape[0]
        #total_power = sum(sample_sizes.values())
        all_above_threshold = all(value >= 0.79 for value in sample_sizes.values())
        if not all_above_threshold:
            if sample_sizes['Alcohol'] < 0.79:
                size += 100
            elif sample_sizes['Depression'] < 0.79:
                size += 100
            elif sample_sizes['Tobacco'] < 0.79:
                size += 100
            elif sample_sizes['Alcohol-Depression'] < 0.79:
                size += 80
            elif sample_sizes['Alcohol-Tobacco'] < 0.79:
                size += 80
            elif sample_sizes['Depression-Tobacco'] < 0.79:
                size += 80
            elif sample_sizes['Alcohol-Depression-Tobacco'] < 0.79:
                size += 20
              
            ratio_alc_only = round(dfx['Intervention'].value_counts(normalize=True)['A']+dfx['Intervention'].value_counts(normalize=True)['NAlc'],2)
            ratio_dep_only = round(dfx['Intervention'].value_counts(normalize=True)['D']+dfx['Intervention'].value_counts(normalize=True)['ND'],2)
            ratio_tob_only = round(dfx['Intervention'].value_counts(normalize=True)['T']+dfx['Intervention'].value_counts(normalize=True)['NT'],2)
            ratio_at = round(dfx['Intervention'].value_counts(normalize=True)['AT']+dfx['Intervention'].value_counts(normalize=True)['NAT'],2)
            ratio_ad = round(dfx['Intervention'].value_counts(normalize=True)['AD']+dfx['Intervention'].value_counts(normalize=True)['NAD'],2)
            ratio_dt = round(dfx['Intervention'].value_counts(normalize=True)['DT']+dfx['Intervention'].value_counts(normalize=True)['NDT'],2)
            ratio_adt = round(dfx['Intervention'].value_counts(normalize=True)['ADT']+dfx['Intervention'].value_counts(normalize=True)['NADT'],2)
            
            #dfx = create_dataset2(size, 0.08, 0.08, 0.08, 0.04, 0.04, 0.04, 0.03, 0.9, 0.80, 0.70, 0.60, gender=[0.5, 0.5], bmi=[0.2,0.5,0.3],edu=[0.1,0.2,0.2,0.5],seed=52)
            dfx = create_dataset(size, ratio_alc_only, ratio_dep_only, ratio_tob_only, ratio_ad, ratio_at, ratio_dt, ratio_adt, 0.95, 0.90, 0.85, 0.80, gender=[0.5, 0.5], bmi=[0.2,0.5,0.3],edu=[0.1,0.2,0.2,0.5],seed=52,age=[18,60,35,15])
            return try_sample_sizes3(dfx)
        while all(value > 0.85 for value in sample_sizes.values()):
                print('inside shrinking')
                #for group in sample_sizes.keys:
                    #if sample_sizes[group] > 0.95:
                    #   size -= 1500
                if sample_sizes['Alcohol'] > 0.95:
                  size -= 1500 
                elif sample_sizes['Tobacco'] > 0.95:
                  size -= 1500
                elif sample_sizes['Depression'] > 0.95:
                  size -= 1500
                    #if any(value < 0.80 for value in sample_sizes.values()):
                
                ratio_alc_only = round(dfx['Intervention'].value_counts(normalize=True)['A']+dfx['Intervention'].value_counts(normalize=True)['NAlc'],2)
                ratio_dep_only = round(dfx['Intervention'].value_counts(normalize=True)['D']+dfx['Intervention'].value_counts(normalize=True)['ND'],2)
                ratio_tob_only = round(dfx['Intervention'].value_counts(normalize=True)['T']+dfx['Intervention'].value_counts(normalize=True)['NT'],2)
                ratio_at = round(dfx['Intervention'].value_counts(normalize=True)['AT']+dfx['Intervention'].value_counts(normalize=True)['NAT'],2)
                ratio_ad = round(dfx['Intervention'].value_counts(normalize=True)['AD']+dfx['Intervention'].value_counts(normalize=True)['NAD'],2)
                ratio_dt = round(dfx['Intervention'].value_counts(normalize=True)['DT']+dfx['Intervention'].value_counts(normalize=True)['NDT'],2)
                ratio_adt = round(dfx['Intervention'].value_counts(normalize=True)['ADT']+dfx['Intervention'].value_counts(normalize=True)['NADT'],2)
                    
                dfx = create_dataset(size, ratio_alc_only, ratio_dep_only, ratio_tob_only, ratio_ad, ratio_at, ratio_dt, ratio_adt, 0.95, 0.90, 0.85, 0.80, gender=[0.5, 0.5], bmi=[0.2,0.5,0.3],edu=[0.1,0.2,0.2,0.5],seed=52,age=[18,60,35,15])
                #dfx = create_dataset2(size, 0.08, 0.08, 0.08, 0.04, 0.04, 0.04, 0.03, 0.9, 0.80, 0.70, 0.60, gender=[0.5, 0.5], bmi=[0.2,0.5,0.3],edu=[0.1,0.2,0.2,0.5],seed=52)
                return try_sample_sizes3(dfx)
        #return dfx
        return StreamingResponse(
                    iter([dfx.to_csv(index=False)]),
                    media_type="text/csv",
                    headers={"Content-Disposition": f"attachment; filename=resim_screening.csv"})
    except Exception as ex:
      print(ex) 

@app.post('/clinical_dataset')
async def clinical_ss(file:UploadFile=File(...)):
    ''' Brings the power of each subgroup in the dataset close to 80% by tweaking their sizes.'''
    try:
        #def create_clinical_tb(dfx):
    # Get desired counts for each intervention group
        dfx = pd.read_csv(file.file)
        file.file.close
        alc_only = round((dfx["Intervention"].value_counts()['A'] + dfx["Intervention"].value_counts()['NAlc'])/6)
        dep_only = round((dfx["Intervention"].value_counts()['D'] + dfx["Intervention"].value_counts()['ND'])/6)
        tob_only = round((dfx["Intervention"].value_counts()['T'] + dfx["Intervention"].value_counts()['NT'])/6)
        alc_dep_only = round((dfx["Intervention"].value_counts()['AD'] + dfx["Intervention"].value_counts()['NAD'])/6)
        alc_tob_only = round((dfx["Intervention"].value_counts()['AT'] + dfx["Intervention"].value_counts()['NAT'])/6)
        dep_tob_only = round((dfx["Intervention"].value_counts()['DT'] + dfx["Intervention"].value_counts()['NDT'])/6)
        alc_dep_tob_only = round((dfx["Intervention"].value_counts()['ADT'] + dfx["Intervention"].value_counts()['NADT'])/6)
        #unaff = round((dfx["Intervention"].value_counts()['UNAFFECTED'])/3)

        my_count = [alc_only, dep_only, tob_only, alc_dep_only, alc_tob_only, dep_tob_only, alc_dep_tob_only ]
        count  = max(my_count)
        # Get current counts for each intervention group
        # curr_counts = dfx["Intervention"].value_counts()
        curr_alc_only = dfx[dfx['Intervention'] == 'A']
        curr_dep_only = dfx[dfx['Intervention'] == 'D']
        curr_tob_only = dfx[dfx['Intervention'] == 'T']
        curr_alc_dep_only = dfx[dfx['Intervention'] == 'AD']
        curr_alc_tob_only = dfx[dfx['Intervention'] == 'AT']
        curr_dep_tob_only = dfx[dfx['Intervention'] == 'DT']
        curr_alc_dep_tob_only = dfx[dfx['Intervention'] == 'ADT']

        curr_Nalc_only = dfx[dfx['Intervention'] == 'NAlc']
        curr_Ndep_only = dfx[dfx['Intervention'] == 'ND']
        curr_Ntob_only = dfx[dfx['Intervention'] == 'NT']
        curr_Nalc_dep_only = dfx[dfx['Intervention'] == 'NAD']
        curr_Nalc_tob_only = dfx[dfx['Intervention'] == 'NAT']
        curr_Ndep_tob_only = dfx[dfx['Intervention'] == 'NDT']
        curr_Nalc_dep_tob_only = dfx[dfx['Intervention'] == 'NADT']

        curr_unaff = dfx[dfx['Intervention'] == 'UNAFFECTED']


        # Update Alcohol Only
        new = curr_alc_only.sample(n=count)
        dfx = dfx.drop(curr_alc_only.index.difference(new.index))

        new = curr_Nalc_only.sample(n=count)
        dfx = dfx.drop(curr_Nalc_only.index.difference(new.index))


        # Update Depression Only
        new = curr_dep_only.sample(n=count)
        dfx = dfx.drop(curr_dep_only.index.difference(new.index))

        new = curr_Ndep_only.sample(n=count)
        dfx = dfx.drop(curr_Ndep_only.index.difference(new.index))


        # Update Tobacco Only
        new = curr_tob_only.sample(n=count)
        dfx = dfx.drop(curr_tob_only.index.difference(new.index))

        new = curr_Ntob_only.sample(n=count)
        dfx = dfx.drop(curr_Ntob_only.index.difference(new.index))


        # Update Alcohol-Depression Only
        new = curr_alc_dep_only.sample(n=count)
        dfx = dfx.drop(curr_alc_dep_only.index.difference(new.index))

        new = curr_Nalc_dep_only.sample(n=count)
        dfx = dfx.drop(curr_Nalc_dep_only.index.difference(new.index))


        # Update Depression-Tobacco Only
        new = curr_dep_tob_only.sample(n=count)
        dfx = dfx.drop(curr_dep_tob_only.index.difference(new.index))

        new = curr_Ndep_tob_only.sample(n=count)
        dfx = dfx.drop(curr_Ndep_tob_only.index.difference(new.index))


        # Update Alcohol-Tobacco Only
        new = curr_alc_tob_only.sample(n=count)
        dfx = dfx.drop(curr_alc_tob_only.index.difference(new.index))

        new = curr_Nalc_tob_only.sample(n=count)
        dfx = dfx.drop(curr_Nalc_tob_only.index.difference(new.index))

        # Update Alcohol-Depression-Tobacco Only
        new = curr_alc_dep_tob_only.sample(n=count)
        dfx = dfx.drop(curr_alc_dep_tob_only.index.difference(new.index))

        new = curr_Nalc_dep_tob_only.sample(n=count)
        dfx = dfx.drop(curr_Nalc_dep_tob_only.index.difference(new.index))

        # Update Unaffected
        new = curr_unaff.sample(n=count)
        dfx = dfx.drop(curr_unaff.index.difference(new.index))
        size=str(dfx.shape[0])
        print(size)
        return StreamingResponse(iter([dfx.to_csv(index=False)]),
        media_type="text/csv",
        headers={"Content-Disposition": f"attachment; filename=clinical_samples_dataset.csv","Clinical-Dataset-Sample-Size":size})
    except Exception as ex:
        print(ex)

if __name__ == '__main__':
    uvicorn.run(app, host='127.0.0.1', port=8000)