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xy.py
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from flask import Flask, render_template, request
import numpy as np
import pandas as pd
from alter import alter
app = Flask(__name__)
l1=['None','back_pain','constipation','abdominal_pain','diarrhoea','mild_fever','yellow_urine',
'yellowing_of_eyes','acute_liver_failure','fluid_overload','swelling_of_stomach',
'swelled_lymph_nodes','malaise','blurred_and_distorted_vision','phlegm','throat_irritation',
'redness_of_eyes','sinus_pressure','runny_nose','congestion','chest_pain','weakness_in_limbs',
'fast_heart_rate','pain_during_bowel_movements','pain_in_anal_region','bloody_stool',
'irritation_in_anus','neck_pain','dizziness','cramps','bruising','obesity','swollen_legs',
'swollen_blood_vessels','puffy_face_and_eyes','enlarged_thyroid','brittle_nails',
'swollen_extremeties','excessive_hunger','extra_marital_contacts','drying_and_tingling_lips',
'slurred_speech','knee_pain','hip_joint_pain','muscle_weakness','stiff_neck','swelling_joints',
'movement_stiffness','spinning_movements','loss_of_balance','unsteadiness',
'weakness_of_one_body_side','loss_of_smell','bladder_discomfort','foul_smell_of urine',
'continuous_feel_of_urine','passage_of_gases','internal_itching','toxic_look_(typhos)',
'depression','irritability','muscle_pain','altered_sensorium','red_spots_over_body','belly_pain',
'abnormal_menstruation','dischromic _patches','watering_from_eyes','increased_appetite','polyuria','family_history','mucoid_sputum',
'rusty_sputum','lack_of_concentration','visual_disturbances','receiving_blood_transfusion',
'receiving_unsterile_injections','coma','stomach_bleeding','distention_of_abdomen',
'history_of_alcohol_consumption','fluid_overload','blood_in_sputum','prominent_veins_on_calf',
'palpitations','painful_walking','pus_filled_pimples','blackheads','scurring','skin_peeling',
'silver_like_dusting','small_dents_in_nails','inflammatory_nails','blister','red_sore_around_nose',
'yellow_crust_ooze']
disease=['Fungal infection','Allergy','GERD','Chronic cholestasis','Drug Reaction',
'Peptic ulcer diseae','AIDS','Diabetes','Gastroenteritis','Bronchial Asthma','Hypertension',
' Migraine','Cervical spondylosis',
'Paralysis (brain hemorrhage)','Jaundice','Malaria','Chicken pox','Dengue','Typhoid','hepatitis A',
'Hepatitis B','Hepatitis C','Hepatitis D','Hepatitis E','Alcoholic hepatitis','Tuberculosis',
'Common Cold','Pneumonia','Dimorphic hemmorhoids(piles)',
'Heartattack','Varicoseveins','Hypothyroidism','Hyperthyroidism','Hypoglycemia','Osteoarthristis',
'Arthritis','(vertigo) Paroymsal Positional Vertigo','Acne','Urinary tract infection','Psoriasis',
'Impetigo']
# TESTING DATA df -------------------------------------------------------------------------------------
df=pd.read_csv("Training.csv")
df.replace({'prognosis':{'Fungal infection':0,'Allergy':1,'GERD':2,'Chronic cholestasis':3,'Drug Reaction':4,
'Peptic ulcer diseae':5,'AIDS':6,'Diabetes ':7,'Gastroenteritis':8,'Bronchial Asthma':9,'Hypertension ':10,
'Migraine':11,'Cervical spondylosis':12,
'Paralysis (brain hemorrhage)':13,'Jaundice':14,'Malaria':15,'Chicken pox':16,'Dengue':17,'Typhoid':18,'hepatitis A':19,
'Hepatitis B':20,'Hepatitis C':21,'Hepatitis D':22,'Hepatitis E':23,'Alcoholic hepatitis':24,'Tuberculosis':25,
'Common Cold':26,'Pneumonia':27,'Dimorphic hemmorhoids(piles)':28,'Heart attack':29,'Varicose veins':30,'Hypothyroidism':31,
'Hyperthyroidism':32,'Hypoglycemia':33,'Osteoarthristis':34,'Arthritis':35,
'(vertigo) Paroymsal Positional Vertigo':36,'Acne':37,'Urinary tract infection':38,'Psoriasis':39,
'Impetigo':40}},inplace=True)
# print(df.head())
X= df[l1]
y = df[["prognosis"]]
np.ravel(y)
# print(y)
# TRAINING DATA tr --------------------------------------------------------------------------------
tr=pd.read_csv("Testing.csv")
tr.replace({'prognosis':{'Fungal infection':0,'Allergy':1,'GERD':2,'Chronic cholestasis':3,'Drug Reaction':4,
'Peptic ulcer diseae':5,'AIDS':6,'Diabetes ':7,'Gastroenteritis':8,'Bronchial Asthma':9,'Hypertension ':10,
'Migraine':11,'Cervical spondylosis':12,
'Paralysis (brain hemorrhage)':13,'Jaundice':14,'Malaria':15,'Chicken pox':16,'Dengue':17,'Typhoid':18,'hepatitis A':19,
'Hepatitis B':20,'Hepatitis C':21,'Hepatitis D':22,'Hepatitis E':23,'Alcoholic hepatitis':24,'Tuberculosis':25,
'Common Cold':26,'Pneumonia':27,'Dimorphic hemmorhoids(piles)':28,'Heart attack':29,'Varicose veins':30,'Hypothyroidism':31,
'Hyperthyroidism':32,'Hypoglycemia':33,'Osteoarthristis':34,'Arthritis':35,
'(vertigo) Paroymsal Positional Vertigo':36,'Acne':37,'Urinary tract infection':38,'Psoriasis':39,
'Impetigo':40}},inplace=True)
X_test= tr[l1]
y_test = tr[["prognosis"]]
np.ravel(y_test)
def doMyTask2(data):
from sklearn import tree
l2=[]
for x in range(0,len(l1)):
l2.append(0)
clf3 = tree.DecisionTreeClassifier() # empty model of the decision tree
clf3 = clf3.fit(X,y)
# calculating accuracy-------------------------------------------------------------------
from sklearn.metrics import accuracy_score
y_pred=clf3.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(accuracy_score(y_test, y_pred,normalize=False))
# -----------------------------------------------------
psymptoms = [data[0],data[1],data[2],data[3],data[4]]
for k in range(0,len(l1)):
# print (k,)
for z in psymptoms:
if(z==l1[k]):
l2[k]=1
inputtest = [l2]
predict = clf3.predict(inputtest)
predicted=predict[0]
h='no'
for a in range(0,len(disease)):
if(predicted == a):
h='yes'
break
return disease[predicted]
def doMyTask(data):
from sklearn.ensemble import RandomForestClassifier
l2=[]
for x in range(0,len(l1)):
l2.append(0)
clf4 = RandomForestClassifier()
clf4 = clf4.fit(X,np.ravel(y))
# calculating accuracy-------------------------------------------------------------------
from sklearn.metrics import accuracy_score
y_pred=clf4.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(accuracy_score(y_test, y_pred,normalize=False))
# -----------------------------------------------------
psymptoms = [data[0],data[1],data[2],data[3],data[4]]
for k in range(0,len(l1)):
for z in psymptoms:
if(z==l1[k]):
l2[k]=1
inputtest = [l2]
predict = clf4.predict(inputtest)
predicted=predict[0]
h='no'
for a in range(0,len(disease)):
if(predicted == a):
h='yes'
break
return disease[predicted]
@app.route('/acceptor', methods=['GET'])
def acceptor():
return render_template('prediction.php',data=l1)
@app.route('/map', methods=['GET'])
def map():
return render_template('map.html')
# getting data from user via form
@app.route('/acceptor', methods=['POST'])
def Symptoms():
Symptom_1 = request.form['Symptom1']
Symptom_2 = request.form['Symptom2']
Symptom_3 = request.form['Symptom3']
Symptom_4 = request.form['Symptom4']
Symptom_5 = request.form['Symptom5']
result = doMyTask([Symptom_1,Symptom_2,Symptom_3,Symptom_4,Symptom_5])
result=alter(result)
result2 = doMyTask2([Symptom_1,Symptom_2,Symptom_3,Symptom_4,Symptom_5])
result2=alter(result2)
return render_template('prediction.php', data=l1, requestData=[Symptom_1,Symptom_2,Symptom_3,Symptom_4,Symptom_5], sometext="You gave the following symptoms:",randomText="Prediction using Random Forest Algorithm is "+result, randomText2="Prediction using Decision Tree Algorithm is "+result2,randomText9="Prediction using Decision Tree and Random Forest Algorithms is "+result, randomText3="**Considering the above symptoms both the diseases predicted by the two algorithms are possible**", randomText5='https://en.wikipedia.org/wiki/'+result, randomText6='https://en.wikipedia.org/wiki/'+result2,disease1=result,disease2=result2)
if __name__ == '__main__':
app.run(port=1234, debug=True)