model.py

import pandas as pd 
import numpy as np
import streamlit as st
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
# from sklearn.externals import joblib
import joblib
from urllib.request import urlopen
import MySQLdb
from pymysql import *
from pymysql import cursors


def fetchData():
    try:
        conn = MySQLdb.connect(host="localhost", user="root", passwd="password", db="sih", port=3307)
        c = conn.cursor()
    except:
        print('Cant establish connecetion, Database Server Down!')
        
    dfquiz = pd.read_sql_query("select * from quiz;", conn)
    dfsurvey = pd.read_sql_query("select * from survey;", conn)
    return dfquiz,dfsurvey


def result(user_prime_key=None):
    dfquiz,dfsurvey = fetchData()
    result = pd.merge(dfquiz, dfsurvey, on=['pk', 'name'], suffixes=['_quiz', '_survey']) #this will have complete reuslt
    temp = result[result.pk == user_prime_key] #user specific results in the session
    return temp

def domainmarks(dataframe=None):
    temp1 = dataframe.drop(['name','pk'],axis=1)
    temp10 = dataframe.drop(['name','pk'],axis=1)
    marksdf = pd.DataFrame(columns=(['Language_vocab', 'Memory', 'Speed','Visual_discrimination','Audio_Discrimination', 'Survey_Score']) )
    marksdf.Language_vocab = (temp1.mone_quiz+temp1.mtwo_quiz+temp1.mthree_quiz+temp1.mfour_quiz+temp1.mfive_quiz+temp1.msix_quiz+temp1.meight_quiz)/24
    marksdf.Visual_discrimination = (temp1.mone_quiz+temp1.mthree_quiz+temp1.mfour_quiz+temp1.msix_quiz)/16
    marksdf.Memory = (temp1.mtwo_quiz+temp1.mnine_quiz)/8
    marksdf.Audio_Discrimination = (temp1.mseven_quiz+temp1.mten_quiz)/8
    
    marksdf.Speed = np.random.uniform(0,0.5)
    
    surevy_col =['mone_survey', 'mtwo_survey', 'mthree_survey', 'mfour_survey',
       'mfive_survey', 'msix_survey', 'mseven_survey', 'meight_survey',
       'mnine_survey', 'mten_survey', 'meleven', 'mtwelve', 'mthirteen',
       'mfourteen', 'mfifteen', 'msixteen', 'mseventeen', 'meighteen',
       'mnineteen', 'mtwenty']
    marksdf.Survey_Score = temp1[surevy_col].sum(axis=1)/80
    data = marksdf.copy()
    del temp1
    del temp10
    del marksdf
    return data
 
 
def prediction(user_prime_key=None):
    res = result(user_prime_key = user_prime_key)
    
    data = domainmarks(res)
    
     
    print("loading model")
    pipeline = joblib.load('C:\\Users\\admin\\OneDrive\\Desktop\\DyslexiaML-master\\pages\\my_model.joblib') #url
    print('Done')
    
    predictions = pipeline.predict(data)
    print(predictions[0])
    return predictions[0]

# def main():
#     user_prime_key=1
#     print(model(user_prime_key= user_prime_key)) 

# if __name__ == "__main__":
#     main()