Initial commit

.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml

.idea/food_recomend_mechinelearning.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.8 (food_recomend_mechinelearning)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="pytest" />
+  </component>
+</module>
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.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
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.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (food_recomend_mechinelearning)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/food_recomend_mechinelearning.iml" filepath="$PROJECT_DIR$/.idea/food_recomend_mechinelearning.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

helthyfoodplaning.py

+import os
+import psutil
+import time
+import subprocess
+import fnmatch
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+from tkinter import *
+from PIL import ImageFilter,Image
+from tkinter import filedialog, messagebox
+from sklearn.cluster import KMeans
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+
+data=pd.read_csv('normal_food.csv')
+Breakfastdata=data['Breakfast']
+BreakfastdataNumpy=Breakfastdata.to_numpy()
+    
+Lunchdata=data['Lunch']
+LunchdataNumpy=Lunchdata.to_numpy()
+    
+Dinnerdata=data['Dinner']
+DinnerdataNumpy=Dinnerdata.to_numpy()
+Food_itemsdata=data['Food_items']
+
+
+
+def show_entry_fields():
+    print("\n Age: %s\n Veg-NonVeg: %s\n Weight: %s kg\n Hight: %s cm\n" % (e1.get(), e2.get(),e3.get(), e4.get()))
+
+
+def Weight_Loss():
+    show_entry_fields()
+    
+    breakfastfoodseparated=[]
+    Lunchfoodseparated=[]
+    Dinnerfoodseparated=[]
+        
+    breakfastfoodseparatedID=[]
+    LunchfoodseparatedID=[]
+    DinnerfoodseparatedID=[]
+        
+    for i in range(len(Breakfastdata)):
+        if BreakfastdataNumpy[i]==1:
+            breakfastfoodseparated.append( Food_itemsdata[i] )
+            breakfastfoodseparatedID.append(i)
+        if LunchdataNumpy[i]==1:
+            Lunchfoodseparated.append(Food_itemsdata[i])
+            LunchfoodseparatedID.append(i)
+        if DinnerdataNumpy[i]==1:
+            Dinnerfoodseparated.append(Food_itemsdata[i])
+            DinnerfoodseparatedID.append(i)
+        
+    # retrieving Lunch data rows by loc method |
+    LunchfoodseparatedIDdata = data.iloc[LunchfoodseparatedID]
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.T
+    #print(LunchfoodseparatedIDdata)
+    val=list(np.arange(5,15))
+    Valapnd=[0]+val
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.iloc[Valapnd]
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.T
+    #print(LunchfoodseparatedIDdata)
+
+    # retrieving Breafast data rows by loc method 
+    breakfastfoodseparatedIDdata = data.iloc[breakfastfoodseparatedID]
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.T
+    val=list(np.arange(5,15))
+    Valapnd=[0]+val
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.iloc[Valapnd]
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.T
+        
+        
+    # retrieving Dinner Data rows by loc method 
+    DinnerfoodseparatedIDdata = data.iloc[DinnerfoodseparatedID]
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.T
+    val=list(np.arange(5,15))
+    Valapnd=[0]+val
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.iloc[Valapnd]
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.T
+        
+    #calculating BMI
+    age=int(e1.get())
+    veg=float(e2.get())
+    weight=float(e3.get())
+    height=float(e4.get())
+    bmi = weight/((height/100)**2) 
+    agewiseinp=0
+        
+    for lp in range (0,80,20):
+        test_list=np.arange(lp,lp+20)
+        for i in test_list: 
+            if(i == age):
+                tr=round(lp/20)  
+                agecl=round(lp/20)    
+
+        
+    #conditions
+    print("Your body mass index is: ", bmi)
+    if ( bmi < 16):
+        print("Acoording to your BMI, you are Severely Underweight")
+        clbmi=4
+    elif ( bmi >= 16 and bmi < 18.5):
+        print("Acoording to your BMI, you are Underweight")
+        clbmi=3
+    elif ( bmi >= 18.5 and bmi < 25):
+        print("Acoording to your BMI, you are Healthy")
+        clbmi=2
+    elif ( bmi >= 25 and bmi < 30):
+        print("Acoording to your BMI, you are Overweight")
+        clbmi=1
+    elif ( bmi >=30):
+        print("Acoording to your BMI, you are Severely Overweight")
+        clbmi=0
+
+    #converting into numpy array
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.to_numpy()
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.to_numpy()
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.to_numpy()
+    ti=(clbmi+agecl)/2
+    
+    ## K-Means Based  Dinner Food
+    Datacalorie=DinnerfoodseparatedIDdata[1:,1:len(DinnerfoodseparatedIDdata)]
+
+    X = np.array(Datacalorie)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
+
+    XValu=np.arange(0,len(kmeans.labels_))
+    
+    # retrieving the labels for dinner food
+    dnrlbl=kmeans.labels_
+
+    ## K-Means Based  lunch Food
+    Datacalorie=LunchfoodseparatedIDdata[1:,1:len(LunchfoodseparatedIDdata)]
+    
+    X = np.array(Datacalorie)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
+    
+    XValu=np.arange(0,len(kmeans.labels_))
+    
+    # retrieving the labels for lunch food
+    lnchlbl=kmeans.labels_
+    
+    ## K-Means Based  lunch Food
+    Datacalorie=breakfastfoodseparatedIDdata[1:,1:len(breakfastfoodseparatedIDdata)]
+    
+    X = np.array(Datacalorie)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
+    
+    XValu=np.arange(0,len(kmeans.labels_))
+    
+    # retrieving the labels for breakfast food
+    brklbl=kmeans.labels_
+    
+    inp=[]
+    ## Reading of the Dataet
+    datafin=pd.read_csv('nutrition_container.csv')
+
+    ## train set
+    dataTog=datafin.T
+    bmicls=[0,1,2,3,4]
+    agecls=[0,1,2,3,4]
+    weightlosscat = dataTog.iloc[[1,2,7,8]]
+    weightlosscat=weightlosscat.T
+    weightgaincat= dataTog.iloc[[0,1,2,3,4,7,9,10]]
+    weightgaincat=weightgaincat.T
+    healthycat = dataTog.iloc[[1,2,3,4,6,7,9]]
+    healthycat=healthycat.T
+    weightlosscatDdata=weightlosscat.to_numpy()
+    weightgaincatDdata=weightgaincat.to_numpy()
+    healthycatDdata=healthycat.to_numpy()
+    weightlosscat=weightlosscatDdata[1:,0:len(weightlosscatDdata)]
+    weightgaincat=weightgaincatDdata[1:,0:len(weightgaincatDdata)]
+    healthycat=healthycatDdata[1:,0:len(healthycatDdata)]
+    
+    
+    weightlossfin=np.zeros((len(weightlosscat)*5,6),dtype=np.float32)
+    weightgainfin=np.zeros((len(weightgaincat)*5,10),dtype=np.float32)
+    healthycatfin=np.zeros((len(healthycat)*5,9),dtype=np.float32)
+    t=0
+    r=0
+    s=0
+    yt=[]
+    yr=[]
+    ys=[]
+    for zz in range(5):
+        for jj in range(len(weightlosscat)):
+            valloc=list(weightlosscat[jj])
+            valloc.append(bmicls[zz])
+            valloc.append(agecls[zz])
+            weightlossfin[t]=np.array(valloc)
+            yt.append(brklbl[jj])
+            t+=1
+        for jj in range(len(weightgaincat)):
+            valloc=list(weightgaincat[jj])
+            valloc.append(bmicls[zz])
+            valloc.append(agecls[zz])
+            weightgainfin[r]=np.array(valloc)
+            yr.append(lnchlbl[jj])
+            r+=1
+        for jj in range(len(healthycat)):
+            valloc=list(healthycat[jj])
+            valloc.append(bmicls[zz])
+            valloc.append(agecls[zz])
+            healthycatfin[s]=np.array(valloc)
+            ys.append(dnrlbl[jj])
+            s+=1
+
+    
+    X_test=np.zeros((len(weightlosscat),6),dtype=np.float32)
+
+    print('####################')
+    
+    #randomforest
+    for jj in range(len(weightlosscat)):
+        valloc=list(weightlosscat[jj])
+        valloc.append(agecl)
+        valloc.append(clbmi)
+        X_test[jj]=np.array(valloc)*ti
+    
+    
+    
+    X_train=weightlossfin# Features
+    y_train=yt # Labels
+
+    #Create a Gaussian Classifier
+    clf=RandomForestClassifier(n_estimators=100)
+    
+    #Train the model using the training sets y_pred=clf.predict(X_test)
+    clf.fit(X_train,y_train)
+    
+    #print (X_test[1])
+    X_test2=X_test
+    y_pred=clf.predict(X_test)
+    
+    
+    print ('SUGGESTED FOOD  ::')
+    for ii in range(len(y_pred)):
+        if y_pred[ii]==2:     #weightloss
+            print (Food_itemsdata[ii])
+            findata=Food_itemsdata[ii]
+            if int(veg)==1:
+                datanv=['Chicken Burger']
+                for it in range(len(datanv)):
+                    if findata==datanv[it]:
+                        print('VegNovVeg')
+
+    print('\n Thank You for taking our recommendations. :)')
+
+
+
+
+def Weight_Gain():
+    show_entry_fields()
+
+    breakfastfoodseparated=[]
+    Lunchfoodseparated=[]
+    Dinnerfoodseparated=[]
+        
+    breakfastfoodseparatedID=[]
+    LunchfoodseparatedID=[]
+    DinnerfoodseparatedID=[]
+        
+    for i in range(len(Breakfastdata)):
+        if BreakfastdataNumpy[i]==1:
+            breakfastfoodseparated.append( Food_itemsdata[i] )
+            breakfastfoodseparatedID.append(i)
+        if LunchdataNumpy[i]==1:
+            Lunchfoodseparated.append(Food_itemsdata[i])
+            LunchfoodseparatedID.append(i)
+        if DinnerdataNumpy[i]==1:
+            Dinnerfoodseparated.append(Food_itemsdata[i])
+            DinnerfoodseparatedID.append(i)
+        
+    # retrieving rows by loc method |
+    LunchfoodseparatedIDdata = data.iloc[LunchfoodseparatedID]
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.T
+    val=list(np.arange(5,15))
+    Valapnd=[0]+val
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.iloc[Valapnd]
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.T
+        
+    # retrieving rows by loc method 
+    breakfastfoodseparatedIDdata = data.iloc[breakfastfoodseparatedID]
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.T
+    val=list(np.arange(5,15))
+    Valapnd=[0]+val
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.iloc[Valapnd]
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.T
+        
+        
+    # retrieving rows by loc method 
+    DinnerfoodseparatedIDdata = data.iloc[DinnerfoodseparatedID]
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.T
+    val=list(np.arange(5,15))
+    Valapnd=[0]+val
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.iloc[Valapnd]
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.T
+        
+    #claculating BMI
+    age=int(e1.get())
+    veg=float(e2.get())
+    weight=float(e3.get())
+    height=float(e4.get())
+    bmi = weight/((height/100)**2)        
+
+    for lp in range (0,80,20):
+        test_list=np.arange(lp,lp+20)
+        for i in test_list: 
+            if(i == age):
+                tr=round(lp/20)  
+                agecl=round(lp/20)
+
+    print("Your body mass index is: ", bmi)
+    if ( bmi < 16):
+        print("Acoording to your BMI, you are Severely Underweight")
+        clbmi=4
+    elif ( bmi >= 16 and bmi < 18.5):
+        print("Acoording to your BMI, you are Underweight")
+        clbmi=3
+    elif ( bmi >= 18.5 and bmi < 25):
+        print("Acoording to your BMI, you are Healthy")
+        clbmi=2
+    elif ( bmi >= 25 and bmi < 30):
+        print("Acoording to your BMI, you are Overweight")
+        clbmi=1
+    elif ( bmi >=30):
+        print("Acoording to your BMI, you are Severely Overweight")
+        clbmi=0
+
+
+    
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.to_numpy()
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.to_numpy()
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.to_numpy()
+    ti=(bmi+agecl)/2
+    
+    
+    ## K-Means Based  Dinner Food
+    Datacalorie=DinnerfoodseparatedIDdata[1:,1:len(DinnerfoodseparatedIDdata)]
+    
+    X = np.array(Datacalorie)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
+    
+    XValu=np.arange(0,len(kmeans.labels_))
+    # plt.bar(XValu,kmeans.labels_)
+    dnrlbl=kmeans.labels_
+    # plt.title("Predicted Low-High Weigted Calorie Foods")
+    
+    ## K-Means Based  lunch Food
+    Datacalorie=LunchfoodseparatedIDdata[1:,1:len(LunchfoodseparatedIDdata)]
+    
+    X = np.array(Datacalorie)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
+    
+    XValu=np.arange(0,len(kmeans.labels_))
+    # fig,axs=plt.subplots(1,1,figsize=(15,5))
+    # plt.bar(XValu,kmeans.labels_)
+    lnchlbl=kmeans.labels_
+    # plt.title("Predicted Low-High Weigted Calorie Foods")
+    
+    ## K-Means Based  lunch Food
+    Datacalorie=breakfastfoodseparatedIDdata[1:,1:len(breakfastfoodseparatedIDdata)]
+
+    X = np.array(Datacalorie)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
+    
+    XValu=np.arange(0,len(kmeans.labels_))
+    # fig,axs=plt.subplots(1,1,figsize=(15,5))
+    # plt.bar(XValu,kmeans.labels_)
+    brklbl=kmeans.labels_
+    
+    # plt.title("Predicted Low-High Weigted Calorie Foods")
+    inp=[]
+    ## Reading of the Dataet
+    datafin=pd.read_csv('nutrition_container.csv')
+    datafin.head(5)
+    
+    dataTog=datafin.T
+    bmicls=[0,1,2,3,4]
+    agecls=[0,1,2,3,4]
+    weightlosscat = dataTog.iloc[[1,2,7,8]]
+    weightlosscat=weightlosscat.T
+    weightgaincat= dataTog.iloc[[0,1,2,3,4,7,9,10]]
+    weightgaincat=weightgaincat.T
+    healthycat = dataTog.iloc[[1,2,3,4,6,7,9]]
+    healthycat=healthycat.T
+    weightlosscatDdata=weightlosscat.to_numpy()
+    weightgaincatDdata=weightgaincat.to_numpy()
+    healthycatDdata=healthycat.to_numpy()
+    weightlosscat=weightlosscatDdata[1:,0:len(weightlosscatDdata)]
+    weightgaincat=weightgaincatDdata[1:,0:len(weightgaincatDdata)]
+    healthycat=healthycatDdata[1:,0:len(healthycatDdata)]
+    
+   
+    weightlossfin=np.zeros((len(weightlosscat)*5,6),dtype=np.float32)
+    weightgainfin=np.zeros((len(weightgaincat)*5,10),dtype=np.float32)
+    healthycatfin=np.zeros((len(healthycat)*5,9),dtype=np.float32)
+    t=0
+    r=0
+    s=0
+    yt=[]
+    yr=[]
+    ys=[]
+    for zz in range(5):
+        for jj in range(len(weightlosscat)):
+            valloc=list(weightlosscat[jj])
+            valloc.append(bmicls[zz])
+            valloc.append(agecls[zz])
+            weightlossfin[t]=np.array(valloc)
+            yt.append(brklbl[jj])
+            t+=1
+        for jj in range(len(weightgaincat)):
+            valloc=list(weightgaincat[jj])
+            #print (valloc)
+            valloc.append(bmicls[zz])
+            valloc.append(agecls[zz])
+            weightgainfin[r]=np.array(valloc)
+            yr.append(lnchlbl[jj])
+            r+=1
+        for jj in range(len(healthycat)):
+            valloc=list(healthycat[jj])
+            valloc.append(bmicls[zz])
+            valloc.append(agecls[zz])
+            healthycatfin[s]=np.array(valloc)
+            ys.append(dnrlbl[jj])
+            s+=1
+
+    
+    X_test=np.zeros((len(weightgaincat),10),dtype=np.float32)
+
+    print('####################')
+    # In[287]:
+    for jj in range(len(weightgaincat)):
+        valloc=list(weightgaincat[jj])
+        valloc.append(agecl)
+        valloc.append(clbmi)
+        X_test[jj]=np.array(valloc)*ti
+    
+    
+    X_train=weightgainfin# Features
+    y_train=yr # Labels
+    
+   
+    
+    
+    #Create a Gaussian Classifier
+    clf=RandomForestClassifier(n_estimators=100)
+    
+    #Train the model using the training sets y_pred=clf.predict(X_test)
+    clf.fit(X_train,y_train)
+    
+   
+    X_test2=X_test
+    y_pred=clf.predict(X_test)
+    
+    
+    
+    print ('SUGGESTED FOOD ITEMS ::')
+    for ii in range(len(y_pred)):
+        if y_pred[ii]==2:
+            print (Food_itemsdata[ii])
+            findata=Food_itemsdata[ii]
+            if int(veg)==1:
+                datanv=['Chicken Burger']
+                for it in range(len(datanv)):
+                    if findata==datanv[it]:
+                        print('VegNovVeg')
+
+    print('\n Thank You for taking our recommendations. :)')
+
+
+
+def Healthy():
+    show_entry_fields()
+
+    breakfastfoodseparated=[]
+    Lunchfoodseparated=[]
+    Dinnerfoodseparated=[]
+        
+    breakfastfoodseparatedID=[]
+    LunchfoodseparatedID=[]
+    DinnerfoodseparatedID=[]
+        
+    for i in range(len(Breakfastdata)):
+        if BreakfastdataNumpy[i]==1:
+            breakfastfoodseparated.append( Food_itemsdata[i] )
+            breakfastfoodseparatedID.append(i)
+        if LunchdataNumpy[i]==1:
+            Lunchfoodseparated.append(Food_itemsdata[i])
+            LunchfoodseparatedID.append(i)
+        if DinnerdataNumpy[i]==1:
+            Dinnerfoodseparated.append(Food_itemsdata[i])
+            DinnerfoodseparatedID.append(i)
+        
+    # retrieving rows by loc method |
+    LunchfoodseparatedIDdata = data.iloc[LunchfoodseparatedID]
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.T
+    val=list(np.arange(5,15))
+    Valapnd=[0]+val
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.iloc[Valapnd]
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.T
+        
+    # retrieving rows by loc method 
+    breakfastfoodseparatedIDdata = data.iloc[breakfastfoodseparatedID]
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.T
+    val=list(np.arange(5,15))
+    Valapnd=[0]+val
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.iloc[Valapnd]
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.T
+        
+        
+    # retrieving rows by loc method 
+    DinnerfoodseparatedIDdata = data.iloc[DinnerfoodseparatedID]
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.T
+    val=list(np.arange(5,15))
+    Valapnd=[0]+val
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.iloc[Valapnd]
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.T
+        
+    
+    age=int(e1.get())
+    veg=float(e2.get())
+    weight=float(e3.get())
+    height=float(e4.get())
+    bmi = weight/((height/100)**2) 
+    agewiseinp=0
+        
+    for lp in range (0,80,20):
+        test_list=np.arange(lp,lp+20)
+        for i in test_list: 
+            if(i == age):
+                tr=round(lp/20)  
+                agecl=round(lp/20)    
+
+        
+    #conditions
+    print("Your body mass index is: ", bmi)
+    if ( bmi < 16):
+        print("Acoording to your BMI, you are Severely Underweight")
+        clbmi=4
+    elif ( bmi >= 16 and bmi < 18.5):
+        print("Acoording to your BMI, you are Underweight")
+        clbmi=3
+    elif ( bmi >= 18.5 and bmi < 25):
+        print("Acoording to your BMI, you are Healthy")
+        clbmi=2
+    elif ( bmi >= 25 and bmi < 30):
+        print("Acoording to your BMI, you are Overweight")
+        clbmi=1
+    elif ( bmi >=30):
+        print("Acoording to your BMI, you are Severely Overweight")
+        clbmi=0
+
+    
+    DinnerfoodseparatedIDdata=DinnerfoodseparatedIDdata.to_numpy()
+    LunchfoodseparatedIDdata=LunchfoodseparatedIDdata.to_numpy()
+    breakfastfoodseparatedIDdata=breakfastfoodseparatedIDdata.to_numpy()
+    ti=(bmi+agecl)/2
+    
+    
+
+    Datacalorie=DinnerfoodseparatedIDdata[1:,1:len(DinnerfoodseparatedIDdata)]
+    
+    X = np.array(Datacalorie)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
+    
+    XValu=np.arange(0,len(kmeans.labels_))
+    # fig,axs=plt.subplots(1,1,figsize=(15,5))
+    # plt.bar(XValu,kmeans.labels_)
+    dnrlbl=kmeans.labels_
+    # plt.title("Predicted Low-High Weigted Calorie Foods")
+    
+    Datacalorie=LunchfoodseparatedIDdata[1:,1:len(LunchfoodseparatedIDdata)]
+    
+    X = np.array(Datacalorie)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
+    #print ('## Prediction Result ##')
+    #print(kmeans.labels_)
+    XValu=np.arange(0,len(kmeans.labels_))
+    # fig,axs=plt.subplots(1,1,figsize=(15,5))
+    # plt.bar(XValu,kmeans.labels_)
+    lnchlbl=kmeans.labels_
+    # plt.title("Predicted Low-High Weigted Calorie Foods")
+   
+    Datacalorie=breakfastfoodseparatedIDdata[1:,1:len(breakfastfoodseparatedIDdata)]
+    
+    X = np.array(Datacalorie)
+    kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
+    
+    XValu=np.arange(0,len(kmeans.labels_))
+    # fig,axs=plt.subplots(1,1,figsize=(15,5))
+    # plt.bar(XValu,kmeans.labels_)
+    brklbl=kmeans.labels_
+    # print (len(brklbl))
+    # plt.title("Predicted Low-High Weigted Calorie Foods")
+    inp=[]
+    ## Reading of the Dataet
+    datafin=pd.read_csv('nutrition_container.csv')
+    datafin.head(5)
+   
+    dataTog=datafin.T
+    bmicls=[0,1,2,3,4]
+    agecls=[0,1,2,3,4]
+    weightlosscat = dataTog.iloc[[1,2,7,8]]
+    weightlosscat=weightlosscat.T
+    weightgaincat= dataTog.iloc[[0,1,2,3,4,7,9,10]]
+    weightgaincat=weightgaincat.T
+    healthycat = dataTog.iloc[[1,2,3,4,6,7,9]]
+    healthycat=healthycat.T
+    weightlosscatDdata=weightlosscat.to_numpy()
+    weightgaincatDdata=weightgaincat.to_numpy()
+    healthycatDdata=healthycat.to_numpy()
+    weightlosscat=weightlosscatDdata[1:,0:len(weightlosscatDdata)]
+    weightgaincat=weightgaincatDdata[1:,0:len(weightgaincatDdata)]
+    healthycat=healthycatDdata[1:,0:len(healthycatDdata)]
+    
+    
+    weightlossfin=np.zeros((len(weightlosscat)*5,6),dtype=np.float32)
+    weightgainfin=np.zeros((len(weightgaincat)*5,10),dtype=np.float32)
+    healthycatfin=np.zeros((len(healthycat)*5,9),dtype=np.float32)
+    t=0
+    r=0
+    s=0
+    yt=[]
+    yr=[]
+    ys=[]
+    for zz in range(5):
+        for jj in range(len(weightlosscat)):
+            valloc=list(weightlosscat[jj])
+            valloc.append(bmicls[zz])
+            valloc.append(agecls[zz])
+            weightlossfin[t]=np.array(valloc)
+            yt.append(brklbl[jj])
+            t+=1
+        for jj in range(len(weightgaincat)):
+            valloc=list(weightgaincat[jj])
+            #print (valloc)
+            valloc.append(bmicls[zz])
+            valloc.append(agecls[zz])
+            weightgainfin[r]=np.array(valloc)
+            yr.append(lnchlbl[jj])
+            r+=1
+        for jj in range(len(healthycat)):
+            valloc=list(healthycat[jj])
+            valloc.append(bmicls[zz])
+            valloc.append(agecls[zz])
+            healthycatfin[s]=np.array(valloc)
+            ys.append(dnrlbl[jj])
+            s+=1
+
+    X_test=np.zeros((len(healthycat)*5,9),dtype=np.float32)
+    
+    for jj in range(len(healthycat)):
+        valloc=list(healthycat[jj])
+        valloc.append(agecl)
+        valloc.append(clbmi)
+        X_test[jj]=np.array(valloc)*ti
+    
+    
+    X_train=healthycatfin# Features
+    y_train=ys # Labels
+    
+    
+    
+    
+    #Create a Gaussian Classifier
+    clf=RandomForestClassifier(n_estimators=100)
+    
+    #Train the model using the training sets y_pred=clf.predict(X_test)
+    clf.fit(X_train,y_train)
+    
+    
+    X_test2=X_test
+    y_pred=clf.predict(X_test)
+   
+    
+    
+    print ('SUGGESTED FOOD ITEMS ::')
+    for ii in range(len(y_pred)):
+        if y_pred[ii]==2:
+            print (Food_itemsdata[ii])
+            findata=Food_itemsdata[ii]
+            if int(veg)==1:
+                datanv=['Chicken Burger']
+
+    print('\n Thank You for taking our recommendations. :)')
+
+if __name__ == '__main__':
+    main_win = Tk()
+    
+    Label(main_win,text="Age").grid(row=0,column=0,sticky=W,pady=4)
+    Label(main_win,text="veg/Non veg (1/0)").grid(row=1,column=0,sticky=W,pady=4)
+    Label(main_win,text="Weight (in kg)").grid(row=2,column=0,sticky=W,pady=4)
+    Label(main_win,text="Height (in cm)").grid(row=3,column=0,sticky=W,pady=4)
+
+    e1 = Entry(main_win)
+    e2 = Entry(main_win)
+    e3 = Entry(main_win)
+    e4 = Entry(main_win)
+
+    e1.grid(row=0, column=1)
+    e2.grid(row=1, column=1)
+    e3.grid(row=2, column=1)
+    e4.grid(row=3, column=1)
+
+    Button(main_win,text='clear',command=main_win.quit).grid(row=5,column=0,sticky=W,pady=4)
+    Button(main_win,text='Weight Loss',command=Weight_Loss).grid(row=1,column=4,sticky=W,pady=4)
+    Button(main_win,text='Weight Gain',command=Weight_Gain).grid(row=2,column=4,sticky=W,pady=4)
+    Button(main_win,text='Healthy',command=Healthy).grid(row=3,column=4,sticky=W,pady=4)
+    main_win.geometry("400x200")
+    main_win.wm_title("mechinelearning food recomender")
+
+    main_win.mainloop()

normal_food.csv

+Food_items,Breakfast,Lunch,Dinner,VegNovVeg,Calories,Fats,Proteins,Iron,Calcium,Sodium,Potassium,Carbohydrates,Fibre,VitaminD,Sugars
+Asparagus Cooked,0,1,1, ,22,0.2,2.4,0.91,23,14,224,4.1,2,0,1.3
+Avocados,1,0,0,0,160,15,2,0.55,12,7,485,8.5,6.7,0,0.7
+Bananas,1,0,0,0,89,0.3,1.1,0.26,5,1,358,23,2.6,0,12
+Bagels made in wheat,0,1,1,0,250,1.5,10,2.76,20,439,165,49,4.1,0,6.1
+Berries,1,0,0,0,349,0.4,14,6.8,190,298,77,77,13,0,46
+Brocolli,0,1,1,0,25,0.5,3.8,1.27,118,56,343,3.1,2.8,0,0.6
+Brown Rice,0,1,1,0,362,2.7,7.5,1.8,33,4,268,76,3.4,0,0
+Cauliflower,0,1,1,0,32,0.3,3,0.72,32,259,278,6.3,3.3,0,0
+American cheese,1,0,0,0,331,24,20,0.84,497,966,363,8.3,0,0,0
+Coffee,1,0,0,0,2,0,0.3,0.02,2,1,50,0.2,0,0,0
+Corn,1,1,1,0,97,1.4,3.3,0.55,2,253,3.3,22,2.7,0,7.7
+Dark chocolates,0,0,1,0,556,32,5.5,2.13,30,6,502,60,6.5,0,48
+Grapes,1,0,0,0,93,2.1,5.6,2.63,363,9,272,17,11,0,6.3
+Milk,1,0,1,0,97,6.9,3.8,0.12,169,52,178,5.2,0,0,0
+Cashew Nuts,1,0,0,0,553,44,18,6.68,37,12,660,30,3.3,0,5.9
+Onions,0,1,1,0,40,0.1,1.1,0.21,23,4,146,9.3,1.7,0,4.2
+Orange,1,0,0,0,97,0.2,1.5,0.8,161,3,212,25,11,0,0
+Pasta canned with tomato sauce,0,1,1,0,71,0.7,2.2,0.91,13,381,192,14,0.9,0,4
+Pears,1,0,0,0,57,0.1,0.4,0.18,9,1,116,15,3.1,0,9.8
+Peas,0,1,1,0,81,0.4,5.4,1.47,25,5,244,14,5.7,0,5.7
+Protein Powder,1,0,0,0,411,17,46,8.57,500,329,1129,19,7.1,200,5.7
+Pumpkin,0,1,1,0,18,0.1,0.7,0.57,15,237,230,4.3,1.1,0,2.1
+Tuna Salad,0,1,1,1,187,9.3,16,1,17,402,178,9.4,0,0,0
+Tuna Fish,0,0,1,1,184,6.3,30,1.31,10,50,323,0,0,0,0
+Peproni Pizza,0,0,1,0,298,14,12,2.14,146,692,199,30,1.8,0,3.2
+Cheese Pizza,0,0,1,0,276,11,11,2.47,192,580,170,33,2.1,0,2.5
+French Fries,0,1,1,0,289,14,3.5,0.91,17,357,545,37,3.9,0,0.3
+Chicken Burger,0,1,1,1,292,15,18,0.62,13,859,315,20,1.3,0,0
+Cheese Burger,0,1,1,0,256,12,13,2.78,92,660,178,25,1.4,0,0
+Chicken Sandwich,0,1,1,1,257,12,15,1.32,92,605,256,23,1.2,0,5
+Sugar Doughnuts,0,1,1,0,426,23,5.2,1.06,60,402,102,51,1.5,0,32
+Chocolate Doughnuts,0,1,1,0,452,25,4.9,4,24,326,201,51,1.9,0,27
+Pop Corn - Caramel,1,0,1,0,381,1.4,2,0.8,18,286,110,90,2.5,0,65
+Pop Corn,1,0,1,0,429,9.5,13,2.28,11,490,241,73,14,0,0.5
+Dosa,1,1,1,0,168,3.7,4.5,8,0.7,94,76,29,0.9,0,0.1
+Idli,1,1,1,0,156,1.7,5,17.2,4,207,63,30.2,2.1,0,0.74
+Poha,1,0,0,0,130,1.5,2.6,3.16,1,201,117,26.9,1.1,0,0.5
+Chappati,0,1,1,0,297,7.5,11,3.01,93,409,266,46,4.9,0,2.7
+Tomato,1,1,1,0,16,0.2,1.2,0.47,5,42,212,3.2,0.9,0,2.63
+Yogurt,1,1,1,0,60,4,3.1,0.08,183,70,234,7,0,1,7
+Brownie,1,0,0,0,407,6.2,4.4,3.81,17,457,51,84,2.9,0,55
+Noodles,0,1,1,0,108,0.2,1.8,0.14,4,19,4,24,1,0,0
+Uttapam,1,1,1,0,188,7.2,4.4,24,6.4,522,91,26.4,2.2,0,0.24
+Bhaji Pav,1,0,0,0,151,2.4,9,37.4,3.4,438,180,29.3,1,0,1.35
+Dal Makhani,0,1,1,0,109,8.5,2.1,35.2,8.3,243,366,6.3,1.5,0,3.29
+Almonds,1,0,0,0,579,50,21,3.71,269,1,733,22,13,0,4.4
+Mushrooms,1,1,1,0,22,0.3,3.1,0.5,3,5,318,3.3,1,7,2
+Egg Yolk cooked,1,0,0,1,196,15,14,1.89,62,207,152,0.8,0,88,0.4
+Sweet Potatoes cooked,1,1,0,0,76,0.1,1.4,0.72,27,27,230,18,2.5,0,5.7
+Boiled Potatoes,1,1,1,0,87,0.1,1.9,0.31,5,240,379,20,2,0,0.9
+White Rice,0,1,1,0,360,0.6,6.6,4.36,9,1,86,79,1.4,0,0
+Orange juice,1,0,0,0,45,0.2,0.7,0.2,11,1,200,10,0.2,0,8.4
+Greek yogurt plain,1,1,1,0,73,1.9,10,0.04,115,34,141,3.9,0,0,3.6
+Oat Bran Cooked,1,0,0,0,40,0.9,3.2,0.88,10,1,92,11,2.6,0,0
+Green Tea,1,0,0,0,1,0,0.2,0.02,0,1,0.2,0,0,0,0
+Chia seeds,1,0,0,0,486,31,17,7.72,631,16,407,42,34,0,0
+Cottage cheese with vegetables,0,1,1,0,95,4.2,11,0.1,56,403,86,3,0.1,0,0.4
+Salmon,0,1,1,1,127,4.4,21,0.38,7,75,366,0,0,435,0
+Cereals-Corn Flakes,1,0,0,0,384,0.9,5.9,19.4,2,571,107,88,2.7,286,7.8
+Beans,0,1,1,0,31,0.2,1.8,1.03,37,6,211,7,2.7,0,3.3
+Lentils,0,1,1,0,101,0.5,8.8,3.1,14,246,284,21,0,0,0
+Pasta with corn homemade,0,1,1,0,126,0.7,2.6,0.25,1,0,31,28,4.8,0,0
+Tea,1,0,0,0,1,0,0,0.08,2,1,9,0.2,0,0,0
+Apples,1,0,0,0,52,0.2,0.3,0.12,6,1,107,14,24,0,10
+Strawberries,1,0,0,0,32,0.3,0.7,0.41,16,1,153,7.7,2,0,4.9
+Quninoa,1,1,0,0,120,1.9,4.4,1.49,17,7,172,21,2.8,0,0.9
+Goat meat,0,1,1,1,109,2.3,21,2.83,13,82,385,57,0,0,0
+Rabbit meat,0,1,1,1,114,2.3,22,3.2,12,50,378,0,0,0,0
+Chicken Strips,0,0,1,1,295,15,19,0.72,11,798,334,22,1.1,0,0.3
+Steak Fries,0,1,1,1,255,13,3.3,0.69,19,43,551,31,3.5,0,0.9
+Mexican Rice,0,1,1,0,195,4.9,3.6,1.18,39,677,131,34,1.1,0,1.4
+Fried Shrimp,0,0,1,1,319,20,14,1.54,47,1400,128,21,1.5,0,0.8
+Spaghetti and meatballs,0,0,1,1,170,8.5,7.8,1.29,45,351,206,16,1.5,0,2
+Macroni n Cheese ,0,1,1,0,194,12,6.5,57,145,375,127,16,1.1,0,2.8
+Pork cooked,0,0,1,1,297,21,26,1.29,22,73,362,0,0,0,0
+Bacon cooked,0,0,1,1,146,2.8,28,0.56,7,993,999,1.8,0,0,1.2
+Nachos,1,1,1,0,350,22,4.3,0.75,63,313,362,35,3.2,0,2.2
+Chicken Popcorn,0,1,1,1,351,22,18,1.42,32,1140,288,21,1,0,0
+Turkey cooked,0,1,1,1,203,10,27,1.52,28,78,294,0,0,8,0
+Oyster cooked,0,1,1,1,159,4,29,4.9,6,81,409,0,0,0,0
+Beef sticks,1,0,0,1,550,50,22,3.4,68,1531,257,5.4,0,0,0
+Banana Chips,1,0,0,0,519,34,2.3,1.25,18,6,536,58,7.7,0,35
+Honey,1,0,1,0,304,0,0.3,0.42,6,4,52,82,0.2,0,82
+Chocolate Icecream,0,0,1,0,216,11,3.8,0.93,109,76,249,28,1.2,0,25
+Vanilla Ice cream,0,0,1,0,207,11,3.5,0.09,128,80,199,24,0.7,0,21
+Strawberry Icecream,0,0,1,0,192,8.4,3.2,0.21,120,60,188,28,0.9,0,0
+Marshmallows,0,0,1,0,318,0.2,1.8,0.23,3,80,5,81,0.1,0,58
+Chocolate milk,1,0,0,0,535,30,7.7,2.35,189,79,372,59,3.4,0,52
+Rice Pudding,0,0,1,0,376,0.1,2.7,1.79,14,366,5,91,0.7,0,0

nutrition_container.csv

+0Calories ,1Fats (gm),2Proteins(g),3Iron(mg),4Calcium(mg),5Sodium(mg),6Potassium(mg),7Carbohydrates (gm),8Fibre (gm),9Vitamin D (mcg),10Sugars (gm)
+160,15,2,0.55,12,7,485,8.5,6.7,0,0.7
+89,0.3,1.1,0.26,5,1,358,8.5,2.6,0,12
+349,0.4,14,6.8,190,298,77,8.5,13,0,46
+331,24,20,0.84,497,966,363,8.5,0,0,0
+2,0,0.3,0.02,2,1,50,8.5,0,0,0
+97,1.4,3.3,0.55,2,253,3.3,8.5,2.7,0,7.7
+93,2.1,5.6,2.63,2,9,272,8.5,11,0,6.3
+97,6.9,3.8,0.12,2,52,178,8.5,0,0,0
+553,44,18,6.68,2,12,660,8.5,3.3,0,5.9
+97,0.2,1.5,0.8,2,3,212,8.5,11,0,0
+57,0.1,0.4,0.18,2,1,116,8.5,3.1,0,9.8
+411,17,46,8.57,2,329,1129,8.5,7.1,200,5.7
+381,1.4,2,0.8,2,286,110,8.5,2.5,0,65
+429,9.5,13,2.28,2,490,241,8.5,14,0,0.5
+168,3.7,4.5,8,2,94,76,8.5,0.9,0,0.1
+156,1.7,5,17.2,2,207,63,8.5,2.1,0,0.74
+130,1.5,2.6,3.16,2,201,117,8.5,1.1,0,0.5
+16,0.2,1.2,0.47,2,42,212,8.5,0.9,0,2.63
+60,4,3.1,0.08,2,70,234,8.5,0,1,7
+407,6.2,4.4,3.81,2,457,51,8.5,2.9,0,55
+188,7.2,4.4,24,2,522,91,8.5,2.2,0,0.24
+151,2.4,9,37.4,2,438,180,8.5,1,0,1.35
+579,50,21,3.71,2,1,733,8.5,13,0,4.4
+22,0.3,3.1,0.5,2,5,318,8.5,1,7,2
+196,15,14,1.89,2,207,152,8.5,0,88,0.4
+76,0.1,1.4,0.72,2,27,230,8.5,2.5,0,5.7
+87,0.1,1.9,0.31,2,240,379,8.5,2,0,0.9
+45,0.2,0.7,0.2,2,1,200,8.5,0.2,0,8.4
+73,1.9,10,0.04,2,34,141,8.5,0,0,3.6
+40,0.9,3.2,0.88,2,1,92,8.5,2.6,0,0
+1,0,0.2,0.02,2,1,0.2,8.5,0,0,0
+486,31,17,7.72,2,16,407,8.5,34,0,0
+384,0.9,5.9,19.4,2,571,107,8.5,2.7,286,7.8
+1,0,0,0.08,2,1,9,8.5,0,0,0
+52,0.2,0.3,0.12,2,1,107,8.5,24,0,10
+32,0.3,0.7,0.41,2,1,153,8.5,2,0,4.9
+120,1.9,4.4,1.49,2,7,172,8.5,2.8,0,0.9
+350,22,4.3,0.75,2,313,362,8.5,3.2,0,2.2
+550,50,22,3.4,2,1531,257,8.5,0,0,0
+519,34,2.3,1.25,2,6,536,8.5,7.7,0,35
+304,0,0.3,0.42,2,4,52,8.5,0.2,0,82
+535,30,7.7,2.35,2,79,372,8.5,3.4,0,52