def test_recommendations() -> None: service = RecommendationService() request = RecommendationRequest( user_id=1, category=BookCategory.MYSTERY, max_results=1 ) response = service.Recommend(request, None) assert len(response.recommendations) == 1
def render_homepage(): recommendations_request = RecommendationRequest( user_id=1, category=BookCategory.MYSTERY, max_results=3) recommendations_response = recommendations_client.Recommend( recommendations_request) return render_template( "homepage.html", recommendations=recommendations_response.recommendations, )
async def render_homepage(request: Request): recommendations_request = RecommendationRequest( user_id=1, category=BookCategory.MYSTERY, max_results=3) recommendations_response = recommendations_client.Recommend( recommendations_request) return templates.TemplateResponse( "homepage.html", { "request": request, "recommendations": recommendations_response.recommendations, })
import os import grpc from recommendations_pb2 import RecommendationRequest, BookCategory from recommendations_pb2_grpc import RecommendationsStub recommendations_host = os.getenv("RECOMMENDATIONS_HOST", "localhost:50051") channel = grpc.insecure_channel(f"{recommendations_host}") client = RecommendationsStub(channel) request = RecommendationRequest(user_id=1, category=BookCategory.SCIENCE_FICTION, max_results=2) result = client.Recommend(request) print("Result from the server : ", result.recommendations)
import os import sys import csv import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt from recommendations_pb2_grpc import RecommendationsStub from recommendations_pb2 import RecommendationRequest sys.argv[1] = os.environ.get(sys.argv[1],sys.argv[1]) var = sys.argv[1] #var = 715318008 channel = grpc.insecure_channel("35.185.15.252:50051") client = RecommendationsStub(channel) request = RecommendationRequest(ID=int(var)) result = client.Recommend(request) print(result) # csv_columns = ['CLIENTNUM','Attrition_Flag','Customer_Age','Gender','Dependent_count','Education_Level','Marital_Status','Income_Category','Card_Category','Months_on_book','Total_Relantionship_Count','Months_Inactive_12_mon', 'Contacts_Count_12_mon', 'Credit_Limit','Total_Revolving_Bal','Avg_Open_To_Buy','Total_Amt_Chng_Q4_Q1','Total_Trans_Amt','Total_Trans_Ct','Total_Ct_Chng_Q4_Q1','Avg_Utilization_Ratio','Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon','Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2'] # result_to_dict = [ # {"CLIENTNUM" : result.recommendations.CLIENTNUM, # "Attrition_Flag" : result.recommendations.Attrition_Flag, # "Customer_Age" : result.recommendations.Customer_Age , # "Gender" : result.recommendations.Gender, # "Dependent_count" : result.recommendations.Dependent_count, # "Education_Level" : result.recommendations.Education_Level, # "Marital_Status" : result.recommendations.Marital_Status, # "Income_Category" : result.recommendations.Income_Category, # "Card_Category" : result.recommendations.Card_Category, # "Months_on_book" : result.recommendations.Months_on_book,