url added to env

backend/controllers/appController.js

@@ -314,4 +314,50 @@ export async function resetPassword(req,res){
     }
 }
 
+/** POST: http://localhost:5001/api/emotions/retrieve */
+/** Middleware for saving emotions for a user */
+export async function saveEmotions(req, res, next) {
+    try {
+      const { username, emotions } = req.body;
+
+      // Check if the user exists
+      const user = await UserModel.findOne({ username });
+
+      if (!user) {
+        return res.status(404).send({ error: "User not found" });
+      }
+
+      // Update the user's emotions field with the new data
+      user.emotions = emotions;
+
+      await user.save();
+
+      next();
+    } catch (error) {
+      return res.status(500).send({ error: "Server Error" });
+    }
+  }
+
 
+
+/** POST: http://localhost:5001/api/emotions/save */
+/** Middleware for retrieving emotions for a user */
+export async function retrieveEmotions(req, res, next) {
+    try {
+      const { username } = req.method === "GET" ? req.query : req.body;
+
+      // Check if the user exists
+      const user = await UserModel.findOne({ username });
+
+      if (!user) {
+        return res.status(404).send({ error: "User not found" });
+      }
+
+      // Assuming that emotions are stored as a field in the user model
+      req.userEmotions = user.emotions;
+
+      next();
+    } catch (error) {
+      return res.status(500).send({ error: "Server Error" });
+    }
+  }

backend/controllers/mlController.js

@@ -130,11 +130,11 @@ export async function speech_to_text(req, res) {
 */
 
 export async function emotion_analyzer(req, res) {
-  const { tweet } = req.body;
+  const { message } = req.body;
   try {
-    console.log(tweet)
+    console.log(message)
     const process = spawn("/usr/src/app/venv/bin/python3", ["/usr/src/app/ml_models/emotion_detection/emotionScript.py", tweet,]);
-    // const process = spawn("python3", ["../backend/ml_models/emotion_detection/emotionScript.py", tweet,]);
+    // const process = spawn("python3", ["../backend/ml_models/emotion_detection/emotionScript.py", message,]);
 
     let emotion = ""
     process.stdout.on("data", (data) => {
@@ -155,6 +155,7 @@ export async function emotion_analyzer(req, res) {
           const jsonData = JSON.parse(emotion);
           // const joy = jsonData.emotion;
           // console.log(joy);
+          // console.log(emotion);
           res.status(200).send(jsonData);
         } catch (error) {
           res.status(500).json({ error: "Failed to parse JSON response" });

backend/ml_models/spotify_recommendation/.cache

@@ -1 +1 @@
-{"access_token": "BQC80QFI4ychDG5CWRViF_eHXkYOT42vHCZY1aPIIPlhVcy4eKZLJA5c2CUdutVIBQTa-5hrNyKT_G6LY154cQythJwSjrfoM2AbG2dUdgAj8Seb63tdcbVVGQ_bGdTfu0WrCDUHTkzz26k5YmB9RKkP8qTWaXLIe1AQyTSnj43YBHe8Za2b8k-ZNl94NVoc68nAmiVZJ0fg5q9q_CnSU4-vPNolZYhCNI429g", "token_type": "Bearer", "expires_in": 3600, "scope": "playlist-read-private user-library-read user-read-recently-played user-top-read", "expires_at": 1698560410, "refresh_token": "AQDPFQQaeVerMmQwRulEXf-J4KgjjUINngJ-7MU19CyzrhZMYfh20NqYWXpHpc-XVrLBe7_gig_4zOmcnQCE2DvOrjAUUaK5YigF0Wg2C4f-NYpccD2MBMTl7qZ4vm3falU"}
+{"access_token": "BQC9OMFKcpyPnUKxRzR2QITzzr8GhYPCH2KCS8hk4na6TntReXKzKR91CKupGAvUBqcg0SLic6eae5RFdO4fv0Z1sPNj1cyRsShXpuV7EixiC3HDNGajiGKqRtcd0Ox8gotnH9tj2W09qijsnUuQ-V_M67mc0qRjjQN7nH5lTUBJTxIyfrt6AU27MDdITmZ4Y2POvDLupBjEWxnB3TYWaGuSo-4SxfCb78njbw", "token_type": "Bearer", "expires_in": 3600, "scope": "playlist-read-private user-library-read user-read-recently-played user-top-read", "expires_at": 1698634377, "refresh_token": "AQDPFQQaeVerMmQwRulEXf-J4KgjjUINngJ-7MU19CyzrhZMYfh20NqYWXpHpc-XVrLBe7_gig_4zOmcnQCE2DvOrjAUUaK5YigF0Wg2C4f-NYpccD2MBMTl7qZ4vm3falU"}

backend/ml_models/spotify_recommendation/spotifyRecommendScript.py

@@ -5,12 +5,14 @@
 from spotipy.oauth2 import SpotifyOAuth
 import pandas as pd
 from sklearn.model_selection import train_test_split
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # Suppress INFO and WARNING messages
 import tensorflow as tf
 from sklearn.preprocessing import StandardScaler
 from tensorflow.keras import layers, models
 from tensorflow.keras.optimizers.legacy import Adam
 import pickle
-import os
+
 import config as cf
 import contextlib
 
@@ -152,8 +154,8 @@ def getRecommendation(mood, model, scaler):
     df_recentSongs = getRecentlyPlayed()
 
     # Use contextlib.redirect_stdout to suppress output
-    with contextlib.redirect_stdout(None):
-        df2 = pd.DataFrame(model.predict(scaler.fit_transform(df_recentSongs.iloc[:, 1:])))
+    # with contextlib.redirect_stdout(None):
+    df2 = pd.DataFrame(model.predict(scaler.fit_transform(df_recentSongs.iloc[:, 1:])))
 
     # df2 = pd.DataFrame(model.predict(scaler.fit_transform(df_recentSongs.iloc[:, 1:])))
     df2['Mood']=df2.apply(get_max_index, axis=1)

frontend/package.json

@@ -10,6 +10,7 @@
     "antd": "^5.5.1",
     "axios": "^1.4.0",
     "buffer": "^6.0.3",
+    "dotenv": "^16.3.1",
     "extendable-media-recorder": "^9.1.3",
     "extendable-media-recorder-wav-encoder": "^7.0.99",
     "formik": "^2.4.2",

frontend/src/pages/AI_Assistant.js

@@ -9,6 +9,8 @@ import useFetch from '../hooks/fetch.hook';
 import { useAuthStore } from '../store/store';
 import { MediaRecorder, register } from 'extendable-media-recorder';
 import { connect } from 'extendable-media-recorder-wav-encoder';
+// import 'dotenv/config';
+
 
 import axios from 'axios';
 
@@ -38,14 +40,14 @@ const AI_Assistant = () => {
       }
    }, [response]);
 
-   useEffect(() =>  {
-      axios.get('http://localhost:5001/api/spotify_recommend', { mood: emotion })
-      .then((response) => {
-         setRecommendations(response.data);
-         console.log(response.data);
-      })
-      .catch((error) => console.error(error))
-   }, [emotion]);
+   // useEffect(() =>  {
+   //    axios.get('${process.env.SERVER_ENDPOINT}/api/spotify_recommend', { mood: emotion })
+   //    .then((response) => {
+   //       setRecommendations(response.data);
+   //       console.log(response.data);
+   //    })
+   //    .catch((error) => console.error(error))
+   // }, [emotion]);
 
    const startRecording = async () => {
       if (isRecording) {
@@ -80,7 +82,7 @@ const AI_Assistant = () => {
 
             // Send the audio data to the Node.js backend
             axios
-               .post('http://localhost:5001/api/voice-input', formData)
+               .post(`${process.env.REACT_APP_SERVER_ENDPOINT}/api/voice-input`, formData)
                .then((response) => {
                   console.log(response.data.result);
                   let data = response.data.result;
@@ -94,7 +96,7 @@ const AI_Assistant = () => {
                   // console.log(chatLog)
                   // Make an HTTP request to the backend API to analyze user input using axios
                   axios
-                     .post('http://localhost:5001/api/analyze', { message: voice_message })
+                     .post(`${process.env.REACT_APP_SERVER_ENDPOINT}/api/analyze`, { message: voice_message })
                      .then((response) => {
                         const updatedChatLogWithAI = [...chatLog, { user: 'User', message: voice_message }, { user: 'AI_Consultant', message: response.data.result }];
                         setChatLog(updatedChatLogWithAI);
@@ -103,7 +105,7 @@ const AI_Assistant = () => {
                      .catch((error) => console.error(error));
 
                   axios
-                     .post('http://localhost:5001/api/emotion_analyze', { message: voice_message })
+                     .post(`${process.env.REACT_APP_SERVER_ENDPOINT}/api/emotion_analyze`, { message: voice_message })
                      .then((response) => {
                         setEmotion(response.data.emotion);
                         // console.log(response.data.emotion)
@@ -128,10 +130,13 @@ const AI_Assistant = () => {
       e.preventDefault();
       const updatedChatLog = [...chatLog, { user: 'User', message: message }];
       setChatLog(updatedChatLog);
+      // console.log('{$process.env.REACT_APP_SERVER_ENDPOINT}')
+
+
 
       // Make an HTTP request to the backend API to analyze user input using axios
       axios
-         .post('http://localhost:5001/api/analyze', { message: message })
+         .post(`${process.env.REACT_APP_SERVER_ENDPOINT}/api/analyze`, { message: message })
          .then((response) => {
             const updatedChatLogWithAI = [...updatedChatLog, { user: 'AI_Consultant', message: response.data.result }];
             setChatLog(updatedChatLogWithAI);
@@ -141,10 +146,10 @@ const AI_Assistant = () => {
          .catch((error) => console.error(error));
 
       axios
-         .post('http://localhost:5001/api/emotion_analyze', { message: message })
+         .post(`${process.env.REACT_APP_SERVER_ENDPOINT}/api/emotion_analyze`, { message: message })
          .then((response) => {
             setEmotion(response.data.emotion);
-            console.log(response.data.emotion)
+            console.log(emotion)
          })
          .catch((error) => console.error(error));
       // Clear the input field after submitting

frontend/src/pages/MusicTherapy.js

@@ -27,13 +27,15 @@ import profileImg from '../images/chat-user.svg';
 import chevronDown from '../images/chevron-down.svg';
 import { useEffect, useState } from 'react';
 import { Buffer } from 'buffer';
+// import 'dotenv/config';
 
 import axios from 'axios';
 const qs = require('qs');
-const client_id = '07f4d94fc95d4955ad32cdf68dbefa0c'; // Your client id
-const client_secret = 'cd95a4c259a94411b20b6929270c8ab8'; // Your secret
-// const client_id = process.env.SPOTIFY_CLIENT_ID; // Your client id
-// const client_secret = process.env.SPOTIFY_CLIENT_SECRET; // Your secret
+
+// const client_id = '07f4d94fc95d4955ad32cdf68dbefa0c'; // Your client id
+// const client_secret = 'cd95a4c259a94411b20b6929270c8ab8'; // Your secret
+const client_id = process.env.SPOTIFY_CLIENT_ID; // Your client id
+const client_secret = process.env.SPOTIFY_CLIENT_SECRET; // Your secret
 const redirect_uri = process.env.SPOTIFY_REDIRECT_URI;
 const auth_token = Buffer.from(`${client_id}:${client_secret}`, 'utf-8').toString('base64');
 

frontend/src/pages/YT_RecommendationView.js

@@ -4,6 +4,8 @@ import Footer from '../components/Footer';
 import '../styles/YT_RecommendationView.scss';
 import { Link } from 'react-router-dom';
 import axios from 'axios';
+// import 'dotenv/config';
+
 // get_youtube_videos_from_preferences
 
 /** POST: http://localhost:5001/api/youtube_list */
@@ -47,7 +49,7 @@ const YT_RecommendationView = () => {
          body: storedData,
       };
       axios
-         .post('http://localhost:5001/api/youtube_list', storedData)
+         .post(`${process.env.REACT_APP_SERVER_ENDPOINT}/api/youtube_list`, storedData)
          .then((response) => {
             console.log(typeof response.data);
             setVideoIds(response.data);