Updated Code

app.py

@@ -66,7 +66,7 @@ from sklearn import preprocessing
 le= preprocessing.LabelEncoder()
 df["gender"]=le.fit_transform(df["gender"])
 df.head()
-
+df.insert(44,"epw_total",target)
 
 # In[22]:
 
@@ -88,11 +88,37 @@ y_pred = model.predict(X_test)
 print('Coefficients: \n', model.coef_)
 print("Mean squared error: %.2f" % np.mean((model.predict(X_test) - y_test) ** 2))
 
+#epw
+X=df[["age","gender","height","weight","phq1","phq2","phq3","phq4","phq5","phq6","phq7","phq8","phq9","reported_sleep_in_hours","nap_duration_hours"]]
+y=df["epw_total"]
+
+X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.15,random_state=21)
+
+modelepw=LinearRegression()
+modelepw.fit(X_train,y_train)
+print("Training complete.")
+
+
+# In[79]:
+
+
+r2_score=modelepw.score(X_test,y_test)
+print(r2_score*100,"%")
+
+
+# In[80]:
+
+
+y_pred = modelepw.predict(X_test)
+print('Coefficients: \n', modelepw.coef_)
+# The mean squared error
+print("Mean squared error: %.2f" % np.mean((modelepw.predict(X_test) - y_test) ** 2))
+
 def greet(input):
     temp =np.array(input.split(","), dtype=float) 
     y = model.predict([[temp[0],temp[1],temp[2],temp[3],temp[4],temp[5],temp[6],temp[7],temp[8],temp[9],temp[10],temp[11],temp[12],temp[13],temp[14]]])
-    y = str(y)
-    return y
+    z = modelepw.predict([[temp[0],temp[1],temp[2],temp[3],temp[4],temp[5],temp[6],temp[7],temp[8],temp[9],temp[10],temp[11],temp[12],temp[13],temp[14]]])
+    return str(y,",",z)
 
 textbox_x = gr.Textbox(label="Variable X:", placeholder="10", lines=1)
 textbox_y = gr.Textbox(label="Variable Y:", placeholder ="11", lines=1)