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app1.py
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from flask import Flask,request,render_template
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import pickle
#pip freeze > requirements.txt
app=Flask(__name__)
@app.route("/diabetes",methods=["GET","POST"])
def diabetes():
if request.method=='GET':
return render_template("form.html")
else:
ds=pd.read_csv('diabetes1.csv')
X=ds.iloc[:,[0,1,2,5,6]]
y=ds.iloc[::,-1]
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.10,random_state=0)
model=LogisticRegression()
model.fit(X_train,y_train)
Pregnancies=int(request.form['Pregnancies'])
Glucose=int(request.form['Glucose'])
BloodPressure=int(request.form['BloodPressure'])
BMI=float(request.form['BMI'])
DiabetesPedigreeFunction=float(request.form['DiabetesPedigreeFunction'])
new=np.array([[Pregnancies,Glucose,BloodPressure,BMI,DiabetesPedigreeFunction]])
y_pred=model.predict(new)
return render_template("result.html",y_pred=y_pred)
@app.route("/heart",methods=["GET","POST"])
def heart():
if request.method=='GET':
return render_template('form1.html')
else:
ds=pd.read_csv('heart.csv')
X=ds.drop('target',axis=1)
y=ds.iloc[:,-1]
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.10,random_state=0)
reg=LogisticRegression(max_iter=1200000)
reg.fit(X_train,y_train)
Age=int(request.form['Age'])
gender=int(request.form['gender'])
cp=int(request.form['cp'])
trestbps=int(request.form['trestbps'])
chol=int(request.form['chol'])
fbs=int(request.form['fbs'])
restecg=int(request.form['restecg'])
thalach=int(request.form['thalach'])
new=np.array([[Age,gender,cp,trestbps,chol,fbs,restecg,thalach,ds['exang'].mean(),ds['oldpeak'].mean(),ds['slope'].mean(),ds['ca'].mean(),ds['thal'].mean()]])
y_pred=reg.predict(new)
return render_template("result1.html",y_pred=y_pred)
@app.route("/liverprediction",methods=["GET","POST"])
def liver():
if request.method=='GET':
return render_template('form2.html')
else:
df=pd.read_csv('indian_liver_patient.csv')
dummies=pd.get_dummies(df['Gender'])
df['Albumin_and_Globulin_Ratio'].fillna(df['Albumin_and_Globulin_Ratio'].mean(),inplace=True)
df1=pd.concat([df,dummies],axis='columns')
X=df1.drop(['Gender','Female','Dataset'],axis=1)
y=df1.iloc[:,-3]
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.10,random_state=0)
model = LogisticRegression(max_iter=12000000)
model.fit(X_train,y_train)
Age=int(request.form['Age'])
gender=int(request.form['gender'])
Total_Bilirubin=int(request.form['Total_Bilirubin'])
Alkaline_Phosphotase=int(request.form['Alkaline_Phosphotase'])
Alamine_Aminotransferase=int(request.form['Alamine_Aminotransferase'])
Aspartate_Aminotransferase=int(request.form['Aspartate_Aminotransferase'])
Total_Protiens=int(request.form['Total_Protiens'])
Albumin=int(request.form['Albumin'])
new=np.array([[Age,Total_Bilirubin,df1['Direct_Bilirubin'].mean(),Alkaline_Phosphotase,Alamine_Aminotransferase,Aspartate_Aminotransferase,Total_Protiens,Albumin,df1['Albumin_and_Globulin_Ratio'].mean(),gender]])
y_pred=model.predict(new)
return render_template('result2.html',y_pred=y_pred)
@app.route('/kidneydisease',methods=['GET','POST'])
def kidney():
if request.method=='GET':
return render_template('kidneyform.html')
else:
with open('kidney_predict','rb') as f:
model=pickle.load(f)
Age=int(request.form['Age'])
Blood_Pressure=int(request.form['Blood_Pressure'])
Specific_Gravity=float(request.form['Specific_Gravity'])
Albumin=int(request.form['Albumin'])
Sugar=int(request.form['Sugar'])
Red_Blood_Cells=int(request.form['Red_Blood_Cells'])
new=np.array([[Age,Blood_Pressure,Specific_Gravity,Albumin,Sugar,Red_Blood_Cells]])
y_pred=model.predict(new)
return render_template('result3.html',y_pred=y_pred)
@app.route("/backpain",methods=["GET","POST"])
def backpain():
if request.method=="GET":
return render_template("backpainform.html")
else:
pelvic_incidence=float(request.form['pelvic_incidence'])
pelvic_tilt=float(request.form['pelvic_tilt'])
lumbar_lordosis_angle=float(request.form['lumbar_lordosis_angle'])
sacral_slope=float(request.form['sacral_slope'])
pelvic_radius=float(request.form['pelvic_radius'])
restecgdegree_spondylolisthesis=float(request.form['restecgdegree_spondylolisthesis'])
pelvic_slope=float(request.form['pelvic_slope'])
Direct_tilt=float(request.form['Direct_tilt'])
thoracic_slope=float(request.form['thoracic_slope'])
cervical_tilt=float(request.form['cervical_tilt'])
sacrum_angle=float(request.form['sacrum_angle'])
scoliosis_slope=float(request.form['scoliosis_slope'])
df=pd.read_csv('Dataset_spine.csv')
df.columns=['pelvic_incidence','pelvic_tilt','lumbar_lordosis_angle','sacral_slope','pelvic_radius','degree_spondylolisthesis','pelvic_slope','Direct_tilt','thoracic_slope','cervical_tilt','sacrum_angle','scoliosis_slope','Class']
X=df.drop('Class',axis=1)
y=df['Class']
model=LogisticRegression(max_iter=120000000)
model.fit(X,y)
new=np.array([[pelvic_incidence,pelvic_tilt,lumbar_lordosis_angle,sacral_slope,pelvic_radius,restecgdegree_spondylolisthesis,pelvic_slope,Direct_tilt,thoracic_slope,cervical_tilt,sacrum_angle,scoliosis_slope]])
y_pred=model.predict(new)
return render_template("backpainresult.html",y_pred=y_pred)
@app.route("/Tuberculosis",methods=["GET","POST"])
def tb():
if request.method=="GET":
return render_template('tbform.html')
else:
df=pd.read_csv("tb.csv")
X=df.drop(['prognosis','Tuberculosis','id'],axis=1)
y=df['Tuberculosis']
model=LogisticRegression()
model.fit(X,y)
fatigue=int(request.form['fatigue'])
weight_loss=int(request.form['weight_loss'])
loss_of_appetite=int(request.form['loss_of_appetite'])
mild_fever=int(request.form['mild_fever'])
chills=int(request.form['chills'])
sweating=int(request.form['sweating'])
mucoid_sputum=int(request.form['mucoid_sputum'])
cough=int(request.form['cough'])
chest_pain=int(request.form['chest_pain'])
new=np.array([[fatigue,weight_loss,loss_of_appetite,mild_fever,chills,sweating,mucoid_sputum,cough,chest_pain]])
y_pred=model.predict(new)
return f'{y_pred}'
@app.route('/about')
def about():
return render_template('about.html')
@app.route('/')
def home():
return render_template('home.html')
if __name__ == '__main__':
app.run(debug=True)