django for production of my recommender system

Question

I have written an content_based recommender system in python3 using the data from a mysql database. Now i have to use django for production so that I need not to take input each time new articles are added in the database. How to convert this python code into django production. i will connect the database with django database connections. I am really confused how to write this code in django?

my_recommender_system

import pandas as pd
import re
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
stop = set(stopwords.words('english'))
from string import punctuation
import functools
from matplotlib import pyplot as plt
from nltk.stem.snowball import SnowballStemmer
stemmer = SnowballStemmer("english", ignore_stopwords =True)
from tqdm import tqdm_notebook
tqdm_notebook().pandas()
import numpy as np
import math
from sklearn.metrics.pairwise import linear_kernel
#import text

from collections import Counter
df = pd.read_csv('target.csv')
df = df.loc[:,['id','combined_text']].astype(str)
df["combined_text"] = df["combined_text"].apply(lambda x: ' '.join(pd.unique(x.split())))
df.combined_text = df.combined_text.apply(lambda x: x.lower())   
df.combined_text = df.combined_text.str.replace('[^\w\s]',' ')
df['combined_text'] = df['combined_text'].str.replace('\d+', ' ')
df.combined_text = df.combined_text.str.replace('nbsp?' , ' ')
#df.combined_text = df.combined_text.str.replace('nan?' , ' ')
df.combined_text = df.combined_text.str.replace('value?' , ' ')
df = df.dropna(subset = ['combined_text'])
df.combined_text = df.combined_text.str.replace('\s+', ' ') 
#df.combined_text.map(len).hist(figsize=(15, 5), bins=100)
df = df[(df.combined_text.map(len) > 600)]
df.reset_index(inplace=True, drop=True)

#df1 = df[(df.combined_text.map(len) > 7500)]
stop_words = []

f = open('stopwords.txt', 'r')
for l in f.readlines():
    stop_words.append(l.replace('\n', ''))
    
additional_stop_words = ['t','aah','aap','don','doesn','isn','ve','ll','add', 'ndash','will','nan','q','article','lsquo','rsquo','ldquo','rdquo','personalised','please','read','download','app','here','more','experience','based','explore','bull','fact','myth','ndash','middot','lifestage','entire','collection','articles','reading','website','android','phone','a','zero']
stop_words += additional_stop_words
stop_words = list(filter(None, stop_words))
#print(len(stop_words))

def _removeNonAscii(s): 
    return "".join(i for i in s if ord(i)<128)

def clean_text(text):
    text = text.lower()
    text = re.sub(r"what's", "what is ", text)
    text = text.replace('(ap)', '')
    text = re.sub(r"\'s", " is ", text)
    text = re.sub(r"\'ve", " have ", text)
    text = re.sub(r"can't", "cannot ", text)
    text = re.sub(r"n't", " not ", text)
    text = re.sub(r"i'm", "i am ", text)
    text = re.sub(r"\'re", " are ", text)
    text = re.sub(r"\'d", " would ", text)
    text = re.sub(r"\'ll", " will ", text)
    text = re.sub(r'\W+', ' ', text)
    text = re.sub(r'\s+', ' ', text)
    text = re.sub(r"\\", "", text)
    text = re.sub(r"\'", "", text)    
    text = re.sub(r"\"", "", text)
    text = re.sub('[^a-zA-Z ?!]+', '', text)
    text = _removeNonAscii(text)
    text = text.strip()
    return text

def tokenizer(text):
    text = clean_text(text)    
    tokens = [word_tokenize(sent) for sent in sent_tokenize(text)]
    tokens = list(functools.reduce(lambda x,y: x+y, tokens))
    tokens = list(filter(lambda token: token not in (stop_words + list(punctuation)) , tokens))
    return tokens

#df['combined_text'] = df['combined_text'].map(lambda d: str.encode(d.decode('utf-8')))
    
df['tokens'] = ''
df['tokens'] = df['combined_text'].progress_map(lambda d: tokenizer(d))
df['text_stemmed']=df['tokens'].apply(lambda x : [stemmer.stem(y) for y in x])
df['text_stemmed_sentence']=df['text_stemmed'].apply(lambda x : " ".join(x))
df['stemmed_tokens'] = df['text_stemmed_sentence'].progress_map(lambda d: tokenizer(d))
df = df[['id','text_stemmed_sentence','stemmed_tokens']]
# =============================================================================
# for descripition, tokens in zip(df['combined_text'].head(5), df['tokens'].head(5)):
#     print('description:', descripition)
#     print('tokens:', tokens)
#     print()
#     
# =============================================================================
from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(min_df=5, analyzer='word', ngram_range=(1, 2), stop_words='english')
vz = vectorizer.fit_transform(list(df['stemmed_tokens'].map(lambda tokens: ' '.join(tokens))))
cosine_similarities = linear_kernel(vz,vz)
articlesRecommend = pd.DataFrame(cosine_similarities, columns = df.id, index = df.id)
y = np.array([articlesRecommend[c].nlargest(10).index.values for c in articlesRecommend])
articles_df = pd.DataFrame(data = y, index = articlesRecommend.columns) 

Answer 1

The complete answer to this question will be lengthy but i can just wrap it up simply as:

1. First make virtualenv and install django. Also, you will need to install all the python packages that you used in your python program like pandas etc.
2. run this simple command django-admin startproject <project_name>. Next, run django-admin startapp <app_name> this is for making an app in the django project since django can have many apps.
3. Open source/source/settings.py and in your INSTALLED_APPS list, mention the name of your app.
4. You will need to render the same code in /views.py . But there should be atleast one function with request argument which will achieve the same task.

5. Something like this: import pandas # and import other libs def some_func(request): ## your code

6. Next you will have to map this function with url in urls.py, that is something you can find here: mapping the urls to functions in views.py

Of course, you will have to run the server using python manage.py runserver, you'll be able to locate your project on 127.0.0.1/8000.

Honestly, If you understand the basic architechure of django, this is very easy task to do. This documentation can be of help to you.

Coming to the crux of your question: Since you explained that you'll be suggesting most related articles on the basis of already existing article. First, the source of the data from your Laravel project should flow data in JSON format, you can read that data in your views.py 's functions, once your read the data and run your already working code, next you should be able to send the most related articles information like id or something back through some url. For this purpose you can either do this using Django's rest framework or simply return JsonResponse from your function.