#Created By: Sarika Padmashali
import json
import csv
from collections import defaultdict
import sys
#Taking inputs - user ID and restaurant ID from command line
user = sys.argv[1]
restaurant = sys.argv[2]
print "User ID entered: ", user
print "Restaurant ID entered: ", restaurant
data = []
sum_ratings = 0
count = 0
business_rating = {}
user_rating = {}
#Calculating Average Rating
with open('yelp_academic_dataset_review.json') as f:
    for line in f:
        data = json.loads(line)
        sum_ratings = sum_ratings + data["stars"]        
        count = count + 1
average_rating = sum_ratings/float(count)        
print "Sum of ratings: ",sum_ratings        
print "Count of ratings: ",count
print "Average Rating of Yelp: ", average_rating
#Calculating Restuarant Bias
with open('yelp_academic_dataset_review.json') as f:
    for line in f:
        data = json.loads(line)
        business_id = data["business_id"]
        stars = data["stars"]
        business_rating[business_id] = business_rating.get(business_id,[0,0])
        rate = business_rating.get(business_id)
        stars = rate[0] + stars
        count = rate[1] + 1
        business_rating[business_id] = [stars, count]
#Saving the restaurant id's and biases in a dictionary         
restaurant_bias = {}        
for k, v in business_rating.iteritems():
     average = v[0]/float(v[1]) 
     bias = float(average - average_rating)   
     restaurant_bias[k] = bias       
#Writing Restaurant biases in a csv file       
with open('restaurant_bias.csv', 'wb') as csv_file:
    writer = csv.writer(csv_file)
    for key, value in restaurant_bias.items():
       writer.writerow([key, value])
#User Bias
with open('yelp_academic_dataset_review.json') as f:
    for line in f:
        data = json.loads(line)
        user_id = data["user_id"]
        stars = data["stars"]
        user_rating[user_id] = user_rating.get(user_id,[0,0])
        rate = user_rating.get(user_id)
        stars = rate[0] + stars
        count = rate[1] + 1
        user_rating[user_id] = [stars, count]
# Saving the user id's and biases in a dictionary                
user_bias = {}        
for k, v in user_rating.iteritems():
     average = v[0]/float(v[1]) 
     bias = float(average - average_rating)    
     user_bias[k] = bias       
#Writing the user biases in a csv file       
with open('user_bias.csv', 'wb') as csv_file:
    writer = csv.writer(csv_file)
    for key, value in user_bias.items():
       writer.writerow([key, value])              
#Predicting ratings
current_restaurant_bias = restaurant_bias[restaurant] 
current_user_bias = user_bias[user] 
predicted_rating = float(average_rating + current_user_bias + current_restaurant_bias)
#Normalizing as rating is in the range [0,6]
if predicted_rating > 5.0:
    predicted_rating = 5.0
if predicted_rating < 0.0:
    predicted_rating = 0.0          
print "Predicted Rating: ", predicted_rating