mlcode.py

# -*- coding: utf-8 -*-
"""Untitled0.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1EzHFwThOmluL-YuLg_Wmnkcx1X2Pu-G3
"""

import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore

cred=credentials.Certificate("ninenine-c873e-firebase-adminsdk-2il1d-02ba5247e1.json")
firebase_admin.initialize_app(cred)

def getData():  
  db=firestore.client()
  user_ref=db.collection('users')
  users=user_ref.stream()
  theUsers={}
  theFoodItems={}
  for user in users:
    day_ref=user_ref.document(user.id).collection('daily')
    days=day_ref.stream()
    theUsers[user.id]=[]
    for day in days:
      doc_ref=day_ref.document(day.id).collection('FoodItems')
      docs=doc_ref.stream()
      for doc in docs:
        mydict=doc.to_dict()
        theFoodItems[doc.id]={}
        theUsers[user.id].append(doc.id)
        theFoodItems[doc.id]["calories"]=(mydict['calories'])
        theFoodItems[doc.id]["food_Name"]=(mydict['food_Name'])
  return theUsers,theFoodItems

from math import sqrt
def sim_pearson(prefs,p1,p2):
  si={}
  for item in prefs[p1]:
    if item in prefs[p2]:
      si[item]=1
  n=len(si)
  if n==0: return 0
  sum1=sum([prefs[p1][it] for it in si])
  sum2=sum([prefs[p2][it] for it in si])
  sum1Sq=sum([pow(prefs[p1][it],2) for it in si])
  sum2Sq=sum([pow(prefs[p2][it],2) for it in si])
  pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])
  num=pSum-(sum1*sum2/n)
  den=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n))
  if den==0:return 0
  r=num/den
  return r

def topMatches(prefs,person,n=5,similarity=sim_pearson):
  scores=[(similarity(prefs,person,other),other) for other in prefs if other!=person]
  scores.sort()
  scores.reverse()
  return scores[0:n]

def getRecomendations(prefs,person,n=5,similarity=sim_pearson):
  totals={}
  simSums={}
  for other in prefs:
    if other==person: continue
    sim=similarity(prefs,person,other)

    if sim<=0:continue
    for item in prefs[other]:
      if item not in prefs[person] or prefs[person][item]==0:
        totals.setdefault(item,0)
        totals[item]+=prefs[other][item]*sim
        simSums.setdefault(item,0)
        simSums[item]+=sim
  rankings=[(total/simSums[item],item) for item,total in totals.items()]

  rankings.sort()
  rankings.reverse()
  recom=[]
  for a in rankings[0:n]:
    recom.append(a[1])
  return recom

def transfromPrefs(prefs):
  result={}
  for person in prefs:
    for item in prefs[person]:
      result.setdefault(item,{})

      result[item][person]=prefs[person][item]
  return result

import pandas as pd

data1,data2=getData()
df=pd.DataFrame(0,index=data2.keys(),columns=data1.keys())
print(data1)
for users in data1:
  for dat in data1[users]:
    df.loc[dat,users]=1.0
df=df.to_dict()

USER="STsHPz3Gb7SWJqK9ChgD0yidaq52"                        

db=firestore.client()
recom=getRecomendations(df,USER,1)
print(recom)
for item in recom:
  recomend=data2[item]
  db.collection('users').document(USER).collection('Recomendation').document(item).set(recomend)