I have encountered a strange problem where the GET API that I created was working fine in Postman, but not working at that specific URL when entered in the browser.
Here is what the input and output should look like as shown successfully in Postman: Trying API in Postman
Here is what the error is showing on my browser: Error in browser
I am also getting this error in React about CORS headers even though I have added in code to try to handle that issue: Error in React about CORS
- Here is the code in settings.py in Django:
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'Prediction',
'rest_framework',
'corsheaders',
]
MIDDLEWARE = [
'corsheaders.middleware.CorsMiddleware',
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
- Here is the code in local_settings.py in Django:
#########################################
## IMPORT LOCAL SETTINGS ##
#########################################
try:
from .local_settings import *
except ImportError:
pass
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'NAME': 'postgres',
'USER': 'postgres',
'PASSWORD': 'postgres',
'HOST': '127.0.0.1',
'PORT': '5432',
}
}
#################################################################
## (CORS) Cross-Origin Resource Sharing Settings ##
#################################################################
CORS_ORIGIN_ALLOW_ALL = True
- I also tried adding this code in index.js in React to handle the CORS headers problem but it didn't work:
const express = require('express');
const request = require('request');
const app = express();
app.use((req, res, next) => {
res.header('Access-Control-Allow-Origin', '*');
next();
});
app.get('/jokes/random', (req, res) => {
request({
url: 'https://joke-api-strict-cors.appspot.com/jokes/random'
},
(error, response, body) => {
if (error || response.statusCode !== 200) {
return res.status(500).json({
type: 'error',
message: err.message
});
}
res.json(JSON.parse(body));
}
)
});
const PORT = process.env.PORT || 3000;
app.listen(PORT, () => console.log(`listening on ${PORT}`));
Here is the code in App.js in React:
import logo from './logo.svg';
import './App.css';
import { useState } from 'react';
import axios from 'axios';
function App() {
const [json_response1, set_json_response1] = useState("1st algorithm - List of similar events");
function request_json_response() {
axios.get('http://127.0.0.1:8000/api/get_events_1st_alg', {
data: {
"ID": "User_ID1"
}
}).then(function (response) {
// handle success
set_json_response1(response);
});
}
return (
<div className="App">
<header className="App-header">
<img src={logo} className="App-logo" alt="logo" />
<p>
{json_response1}
</p>
<button onClick={request_json_response}>
Generate events for user
</button>
<a
className="App-link"
href="https://reactjs.org"
target="_blank"
rel="noopener noreferrer"
>
Learn React
</a>
</header>
</div>
);
}
export default App;
Here is the code in views.py in Django:
class get_events_1st_alg(APIView):
def get(self, request, format=None):
"""
data = request.data
banana_dictionary = {'banana':17}
return Response(banana_dictionary, status=status.HTTP_201_CREATED)
"""
import pandas as pd
import numpy as np
import psycopg2
import sqlalchemy
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.metrics import pairwise_distances
import requests
from sqlalchemy import create_engine
engine = create_engine('postgresql://postgres:postgres@localhost/postgres')
# pd.read_sql_query('''SELECT * FROM arts_user_interaction LIMIT 5;''', engine)
events = pd.read_sql_query('''SELECT * FROM arts_event;''', engine)
Ratings = pd.read_sql_query('''SELECT * FROM arts_user_interaction;''', engine)
Mean = Ratings.groupby(by="User_ID", as_index = False)['User Rating'].mean()
Rating_avg = pd.merge(Ratings, Mean, on = "User_ID")
Rating_avg['adg_rating']=Rating_avg['User Rating_x']-Rating_avg['User Rating_y']
check = pd.pivot_table(Rating_avg,values='User Rating_x',index='User_ID',columns='Event_ID')
final = pd.pivot_table(Rating_avg,values='adg_rating',index='User_ID',columns='Event_ID')
final_event = final.fillna(final.mean(axis=0))
final_user = final.apply(lambda row: row.fillna(row.mean()), axis=1)
cosine = cosine_similarity(final_event)
np.fill_diagonal(cosine, 0 )
similarity_with_event =pd.DataFrame(cosine,index=final_event.index)
similarity_with_event.columns=final_user.index
def find_n_neighbours(df,n):
order = np.argsort(df.values, axis=1)[:, :n]
df = df.apply(lambda x: pd.Series(x.sort_values(ascending=False)
.iloc[:n].index,
index=['top{}'.format(i) for i in range(1, n+1)]), axis=1)
return df
sim_user_30_e = find_n_neighbours(similarity_with_event,30)
def get_user_similar_events( user1, user2 ):
common_events = Rating_avg[Rating_avg.User_ID == user1].merge(
Rating_avg[Rating_avg.User_ID == user2],
on = "Event_ID",
how = "inner" )
return common_events.merge(events, on ='Event_ID')
a = get_user_similar_events('User_ID10','User_ID220')
a = a.reindex(columns= ['User Rating_x_x','User Rating_x_y','Name'])
Rating_avg = Rating_avg.astype({"Event_ID": str})
Movie_user = Rating_avg.groupby(by = 'User_ID')['Event_ID'].apply(lambda x:','.join(x))
def User_item_score1(user):
Movie_seen_by_user = check.columns[check[check.index==user].notna().any()].tolist()
a = sim_user_30_e[sim_user_30_e.index==user].values
b = a.squeeze().tolist()
d = Movie_user[Movie_user.index.isin(b)]
l = ','.join(d.values)
Movie_seen_by_similar_users = l.split(',')
Movies_under_consideration = list(set(Movie_seen_by_similar_users)-set(list(map(str, Movie_seen_by_user))))
Movies_under_consideration = list(map(str, Movies_under_consideration))
score = []
for item in Movies_under_consideration:
c = final_event.loc[:,item]
d = c[c.index.isin(b)]
f = d[d.notnull()]
avg_user = Mean.loc[Mean['User_ID'] == user,'User Rating'].values[0]
index = f.index.values.squeeze().tolist()
corr = similarity_with_event.loc[user,index]
fin = pd.concat([f, corr], axis=1)
fin.columns = ['adg_score','correlation']
fin['score']=fin.apply(lambda x:x['adg_score'] * x['correlation'],axis=1)
nume = fin['score'].sum()
deno = fin['correlation'].sum()
final_score = avg_user + (nume/deno)
score.append(final_score)
data = pd.DataFrame({'Event_ID':Movies_under_consideration,'score':score})
top_5_recommendation = data.sort_values(by='score',ascending=False).head(5)
Movie_Name = top_5_recommendation.merge(events, how='inner', on='Event_ID')
Movie_Names = Movie_Name.Name.values.tolist()
return Movie_Names
# user = input("Enter the user id to whom you want to recommend : ")
data = request.data
user = ""
for i, v in data.items():
user = str(v)
predicted_movies = User_item_score1(user)
return Response(predicted_movies, status=status.HTTP_201_CREATED)
I really don't know what I am doing as I am just following a bunch of tutorials online so I would love it if anyone can help with resolving the API issue in the browser and the React issue with CORS in the browser as well. Thank you so much!
