changed python file in server

client/src/components/Card.js

@@ -25,7 +25,7 @@ export default function ImgMediaCard() {
                 </Typography>
             </CardContent>
             <CardActions>
-                <Button size="small">Share</Button>
+                <Button size="small">Connect</Button>
                 <Button size="small">Learn More</Button>
             </CardActions>
         </Card>

client/src/pages/Pair.js

@@ -1,9 +1,10 @@
 import React from 'react'
 import Card from '../components/Card'
+import { Button } from '@mui/material'
 
 export default function Pair() {
   return (
-    <div className='container w-fit mx-auto flex flex-wrap gap-6'>
+    <div className='container mx-auto p-10 flex flex-between flex-wrap gap-6'>
       <Card />
       <Card />
       <Card />

python/interest-classifier.py

@@ -5,68 +5,70 @@
 
 
 #import libraries
+from sklearn import metrics
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
 import numpy as np
-import matplotlib.pyplot as plt 
+import matplotlib.pyplot as plt
 import pandas as pd
 import sklearn
 
 
 # In[10]:
 
 
-#PULL DATA
+# PULL DATA
 
-#pull class list
+# pull class list
 
-#for now
+# for now
 careerFieldList = ['consulting', 'biotech', 'fintech']
 
-#pull dataset
+# pull dataset
 
-#for now
+# for now
 
 
 # In[11]:
 
 
-#format dataset
+# format dataset
 
-#make headers
+# make headers
 headers = ['userID']
 for careerField in careerFieldList:
     headers.append(careerField + '-explicit')
     headers.append(careerField + '-implicit')
-    
-#read csv
-#for now
+
+# read csv
+# for now
 
 dataset = pd.read_csv("data.csv")
 dataset.head(20)
 
-#TODO 
+# TODO
 
 
 # In[12]:
 
 
-#get values
+# get values
 x = dataset.iloc[:, 1:-1].values
 y = dataset.iloc[:, -1].values
 
 
 # In[13]:
 
 
-#train/test split
-from sklearn.model_selection import train_test_split
+# train/test split
 xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size=0.1)
 
 
 # In[14]:
 
 
-#scale values
-from sklearn.preprocessing import StandardScaler
+# scale values
 
 scaler = StandardScaler()
 scaler.fit(x)
@@ -76,8 +78,7 @@
 # In[15]:
 
 
-#fit model
-from sklearn.neighbors import KNeighborsClassifier
+# fit model
 
 classifier = KNeighborsClassifier(n_neighbors=5)
 classifier.fit(xTrain, yTrain)
@@ -86,8 +87,7 @@
 # In[16]:
 
 
-#test
-from sklearn import metrics
+# test
 predictions = classifier.predict(xTest)
 
 print('INPUT')
@@ -107,13 +107,12 @@
 # In[29]:
 
 
-#manual testing
-exampleInput = [[5,25,9,20,7,5]]
+# manual testing
+exampleInput = [[5, 25, 9, 20, 7, 5]]
 singlePrediction = classifier.predict(exampleInput)
 probabilities = classifier.predict_proba(exampleInput)
 
 print('INTERESTS w/ PROBABILITIES')
 classes = classifier.classes_
 print(classes)
 print(probabilities)
-

routes/api/mentees.js

@@ -59,7 +59,7 @@ router.delete('/:id', (req, res) => {
 // middleware function to run python script
 const runModel = (req, res, next) => {
   // note: responses should be in the form of an array
-  const child = spawn('python', ['../../python/process.py', JSON.stringify(req.body.responses)])
+  const child = spawn('python', ['../../python/interest-classifier.py', JSON.stringify(req.body.responses)])
 
   child.stdout.on('data', (data) => {
     const str = data.toString().replace(/'/g, `"`)