--- a
+++ b/Predict.py
@@ -0,0 +1,64 @@
+import os
+import pandas as pd
+import numpy as np
+import csv
+import glob
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn import preprocessing
+from xgboost import XGBClassifier
+
+def process(path,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10):
+    data=pd.read_csv(path)
+    label_encoder = preprocessing.LabelEncoder()
+    data['Diagnosis']= label_encoder.fit_transform(data['Diagnosis'])
+    data['Gen']= label_encoder.fit_transform(data['Genero'])
+    X=data[['Age', 'Weight (Kg)', 'Height (cms)', 'Gen','Heart Rate', 'oxygen saturation', 'Respiratory Rate','Systolic Blood Pressure', 'Diastolic Blood Pressure','Mean Blood Pressure']]
+    y=data['Diagnosis']
+    l=[]
+    #l.append("eswar")
+    l.append(a1)
+    l.append(a2)
+    l.append(a3)
+    l.append(a4)
+    l.append(a5)
+    l.append(a6)
+    l.append(a7)
+    l.append(a8)
+    l.append(a9)
+    l.append(a10)
+    
+    #l.append(a11)
+    
+    
+    
+     
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+    model2=XGBClassifier(objective='multi:softprob')
+    X_test =pd.DataFrame([l])
+    print("Testing data",X_test)
+    model2.fit(X_train, y_train)
+    y_pred = model2.predict(X_test)
+    print("predicted")
+    print(y_pred)
+    result=""
+    treat=""
+    if y_pred[0]==0:
+        result="Stage Normal"
+        treat="dexrazoxane is no longer contraindicated"
+    elif y_pred[0]==1:
+        result="Stage Mild"
+        treat="Adeno-associated virus gene therapy"
+        
+    elif y_pred[0]==2:
+        result="Stage Moderate"
+        treat="anti–interleukin-6 receptor antagonist such as tocilizumab "
+    elif y_pred[0]==3:
+        result="Stage Severe"
+        treat="Immediate surgey need to given"
+    else:
+        result="No Disease"
+    return result,treat
+
+
+