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app.py
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app.py
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from flask import Flask, render_template, request, session, url_for, redirect, jsonify
import pymysql
#===============================================
import pickle
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.neural_network import MLPClassifier
#import matplotlib.pyplot as plt
#import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
# %matplotlib inline
df=pd.read_csv('lymphography.csv', delimiter=',',nrows=25,skiprows=[1])
df_cancer =df
# Pick the best combination of parameters
X = df_cancer.drop(['class'], axis = 1) # We drop our "target" feature and use all the remaining features in our dataframe to train the model.
X.head()
y = df_cancer['class']
y.head()
scaler = StandardScaler()
#X_std = scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state =20)
ann_clf = MLPClassifier()
acc_scorer = make_scorer(accuracy_score)
# Run grid search
parameters = {'solver': ['lbfgs'],
'alpha':[1e-2],
'hidden_layer_sizes':(9,14,14,2), # 9 input, 14-14 neuron in 2 layers,1 output layer
'random_state': [1]}
grid_obj = GridSearchCV(ann_clf, parameters, scoring=acc_scorer)
grid_obj = grid_obj.fit(X_train, y_train)
ann_clf = grid_obj.best_estimator_
ann_clf.fit(X_train, y_train)
y_pred_ann = ann_clf.predict(X_test)
filename1 = 'ann.sav'
pickle.dump(ann_clf, open(filename1, 'wb'))
svclassifierlinear = SVC(kernel='linear')
svclassifierlinear.fit(X_train,y_train)
y_pred=svclassifierlinear.predict(X_test)
accuracy_score(y_test,y_pred)
filename = 'linear_svm_model.sav'
pickle.dump(svclassifierlinear, open(filename, 'wb'))
class_obt={2:'Metastates',3:'malign lymph'}
loaded_model = pickle.load(open(filename, 'rb'))
y_gotdata=loaded_model.predict(np.array(X_test.iloc[4:5]))
y_gotdata
#class_obt.get(y_gotdata[0])
#=================================================
app = Flask(__name__)
app.secret_key = 'random string'
#Database Connection
def dbConnection():
connection = pymysql.connect(host="localhost", user="root", password="root", database="9cancerdiseaseprediction")
return connection
#close DB connection
def dbClose():
dbConnection().close()
return
#default welcome page
@app.route('/')
@app.route('/index')
def index():
return render_template('index.html')
@app.route('/home')
def home():
if 'user' in session:
return render_template('home.html',user=session['user'])
return redirect(url_for('index'))
#logout code
@app.route('/logout')
def logout():
session.pop('user')
return redirect(url_for('index'))
#login code
@app.route('/login', methods=["GET","POST"])
def login():
msg = ''
if request.method == "POST":
#session.pop('user',None)
mobno = request.form.get("mobno")
password = request.form.get("pas")
#print(mobno+password)
con = dbConnection()
cursor = con.cursor()
result_count = cursor.execute('SELECT * FROM userdetails WHERE mobile = %s AND password = %s', (mobno, password))
#a= 'SELECT * FROM userdetails WHERE mobile ='+mobno+' AND password = '+ password
#print(a)
#result_count=cursor.execute(a)
# result = cursor.fetchone()
if result_count>0:
print(result_count)
session['user'] = mobno
return redirect(url_for('home'))
else:
print(result_count)
msg = 'Incorrect username/password!'
return msg
#dbClose()
return redirect(url_for('index'))
#user register code
@app.route('/userRegister', methods=["GET","POST"])
def userRegister():
if request.method == "POST":
try:
status=""
name = request.form.get("name")
address = request.form.get("address")
mailid = request.form.get("mailid")
mobile = request.form.get("mobile")
pass1 = request.form.get("pass1")
con = dbConnection()
cursor = con.cursor()
cursor.execute('SELECT * FROM userdetails WHERE mobile = %s', (mobile))
res = cursor.fetchone()
if not res:
sql = "INSERT INTO userdetails (name, address, email, mobile, password) VALUES (%s, %s, %s, %s, %s)"
val = (name, address, mailid, mobile, pass1)
cursor.execute(sql, val)
con.commit()
status= "success"
return redirect(url_for('index'))
else:
status = "Already available"
return status
except:
print("Exception occured at user registration")
return redirect(url_for('index'))
finally:
dbClose()
return redirect(url_for('index'))
@app.route('/questions', methods=["GET","POST"])
def questions():
if 'user' in session:
if request.method == "POST":
v1 = request.form.get("para1")
v2 = request.form.get("para2")
v3 = request.form.get("para3")
v4 = request.form.get("para4")
v5 = request.form.get("para5")
v6 = request.form.get("para6")
v7 = request.form.get("para7")
v8 = request.form.get("para8")
v9 = request.form.get("para9")
v10 = request.form.get("para10")
v11 = request.form.get("para11")
v12 = request.form.get("para12")
v13 = request.form.get("para13")
v14 = request.form.get("para14")
v15 = request.form.get("para15")
v16 = request.form.get("para16")
v17 = request.form.get("para17")
v18 = request.form.get("para18")
test_list = []
valofall = v1 + ',' + v2 + ',' + v3 + ',' + v4 + ',' + v5 + ',' + v6 + ',' + v7 + ',' + v8 + ',' + v9 + ',' + v10 + ',' + v11 + ',' + v12 + ',' + v13 + ',' + v14 + ',' + v15 + ',' + v16 + ',' + v17 + ',' + v18
print(valofall)
valofsplit = valofall.split(",")
print(valofsplit)
for i in range(0, len(valofsplit)):
test_list.append(int(valofsplit[i]))
# print(test_list)
# X_std = scaler.fit_transform(X)
print(test_list)
print(np.array([test_list]))
loaded_model = pickle.load(open(filename1, 'rb'))
y_gotdata = loaded_model.predict(np.array([test_list]))
print(y_gotdata[0])
print("predicted cancer is " + class_obt.get(y_gotdata[0]))
result = class_obt.get(y_gotdata[0])
# l.config(text="predicted cancer is " + class_obt.get(y_gotdata[0]))
return render_template('predictresult.html',user=session['user'],result=result)
return render_template('questions.html',user=session['user'])
return redirect(url_for('index'))
if __name__ == '__main__':
app.run("0.0.0.0")