Merge branch 'main' of https://github.com/tharoosha/Depresio

backend/controllers/appController.js

@@ -352,8 +352,6 @@ export async function updateRecommendation(req, res) {
     }
   }
 
-
-
 /** GET: http://localhost:5001/api/recommendation/:username */
 export async function getRecommendation(req, res) {
 
@@ -398,4 +396,33 @@ export async function getRecommendation(req, res) {
     // } catch (error) {
     //     return res.status(500).send({ error: "Failed to retrieve recommendations" });
     // }
+}
+
+/** PUT: http://localhost:5001/api/updateRecommendation */
+export async function updateEmotion(req, res){
+    try {
+        const { userId } = req.user;
+
+        if (userId) {
+
+            const body = req.body;
+
+            // update the data
+            UserModel.updateOne({ _id : userId }, body).exec().then(
+                (response) => {
+                    res.status(201).send({ msg : "Record Updated...!"})
+                }
+            )
+
+        }else {
+            return res.status(401).send({ error : "User Not Found...!"});
+        }
+
+    } catch (error) {
+      return res.status(500).send({ error: "Failed to update recommendations" });
+    }   
+}
+
+export async function getEmotion(req, res){
+
 }

backend/controllers/mlController.js

@@ -192,9 +192,14 @@ export async function youtube_lists(req, res) {
     // const process = spawn("/usr/src/app/venv/bin/python3", ["/usr/src/app/ml_models/recommanded_system/youtube_search.py", categories,]);
     const process = spawn("python3", ["../backend/ml_models/recommanded_system/youtube_search.py", categories,]);
 
-    let youtube_list = []
+    // let youtube_list = []
+    let youtube_list = "";
+
+
     process.stdout.on("data", (data) => {
-      youtube_list = data;
+      // youtube_list = data;
+      youtube_list += data.toString();
+
     });
 
     process.on("close", (code) => {
@@ -242,29 +247,29 @@ export async function youtube_lists(req, res) {
 // Define the controller function to execute the Python script
 export async function spotify_recommend(req, res) {
   try {
-    const { mood } = req.body;
+    const { emotion } = req.body;
 
     // Spawn the Python script as a child process
     // const pythonProcess = spawn("/usr/src/app/venv/bin/python3", ["/usr/src/app/ml_models/spotify_recommendation/spotifyRecommendExecution.py", mood,]);
-    const pythonProcess = spawn("python3", [ "../backend/ml_models/spotify_recommendation/spotifyRecommendExecution.py", mood,]);
+    const pythonProcess = spawn("python3", [ "../backend/ml_models/spotify_recommendation/spotifyRecommendExecution.py", emotion,]);
 
     let output = "";
     // let output = [];
 
     // Listen for data events from the Python script's stdout
     pythonProcess.stdout.on("data", (data) => {
-      console.log(data.toString())
+      // console.log(data.toString())
       output += data.toString();
     });
 
     // Listen for the 'close' event to handle the completion of the Python script
     pythonProcess.on("close", (code) => {
       if (code === 0) {
         try {
-          const result = JSON.parse(output);
+          // const result = JSON.parse(output);
           // res.status(200).json(result);
           // const result = JSON.parse(output);  // Parse the output string to JSON
-          res.status(200).json(result);
+          res.status(200).send(output);
           // res.status(200).json({"result":output});
         } catch (error) {
           res.status(500).json({ error: "Failed to parse JSON response" });

backend/controllers/testing/test.js

@@ -0,0 +1,5 @@
+console.log(typeof {"result": ["1HNkqx9Ahdgi1Ixy2xkKkL", "1ei3hzQmrgealgRKFxIcWn", "7eJMfftS33KTjuF7lTsMCx"]})
+
+type = {"result": ["1HNkqx9Ahdgi1Ixy2xkKkL", "1ei3hzQmrgealgRKFxIcWn", "7eJMfftS33KTjuF7lTsMCx"]}
+
+console.log(type.result)

backend/controllers/testing/test.py

@@ -0,0 +1,29 @@
+import json 
+import sys
+
+def script_run():
+    try:
+        result = ['1HNkqx9Ahdgi1Ixy2xkKkL', '1ei3hzQmrgealgRKFxIcWn', '7eJMfftS33KTjuF7lTsMCx']
+
+        output = {"result": result}
+
+        output_json = json.dumps(output)
+        print(json.decode(output))
+
+
+        print(output_json)
+
+    except Exception as e:
+        error_message = str(e)
+        output = {"error2011": error_message}
+
+        output_json = json.dumps(output)
+        print(output_json)
+        sys.stdout.flush()
+
+
+if __name__ == "__main__":
+
+    # input_mood = sys.argv[1]
+
+    script_run()

backend/ml_models/recommanded_system/combinedScript.py

@@ -4,7 +4,7 @@
 import json
 
 def combined_script(input_mood):
-    try:
+    # try:
     # Call the function from mood_TO_video.py to get the output JSON
     # output_json = train_and_predict_video_types(input_mood)
 
@@ -26,27 +26,27 @@ def combined_script(input_mood):
         output_json = json.dumps(output)
         sys.stdout.flush()
         print(result)
-    except:
-        error_message = str(e)
-        output = {"error2011": error_message}
-
-        output_json = json.dumps(output)
-        # print(output_json)
-        sys.stdout.flush()
-        return(output_json)
+    # except:
+    #     error_message = str(e)
+    #     output = {"error2011": error_message}
+
+    #     output_json = json.dumps(output)
+    #     # print(output_json)
+    #     sys.stdout.flush()
+    #     return(output_json)
 
 if __name__ == '__main__':
     # Check if there is at least one command-line argument (excluding the script name)
-    if len(sys.argv) > 1:
+    # if len(sys.argv) > 1:
         # The first command-line argument (sys.argv[1]) will be the input_mood
         # input_mood = sys.argv[1]
 
         # Call the function with the received input_mood
         # combined_script(input_mood)
-        combined_script("Happiness")
+    combined_script("Happiness")
 
-    else:
-        # If no input_mood is provided, handle the case accordingly
-        print(json.dumps({"error": "No mood provided"}))
-        sys.stdout.flush()
+    # else:
+    #     # If no input_mood is provided, handle the case accordingly
+    #     print(json.dumps({"error": "No mood provided"}))
+    #     sys.stdout.flush()
     # combined_script('Happiness')

backend/ml_models/recommanded_system/mood_TO_video.py

@@ -20,13 +20,13 @@ def update_user_feedback(mood, video_type, user_feedback):
     text = mood + "," + video_type + "," + str(user_feedback)
 
     # Open the CSV file in 'a' (append) mode and write the text
-    with open('youtube_data.csv', 'a') as file:
+    with open('backend/ml_models/recommanded_system/youtube_data.csv', 'a') as file:
         file.write('\n' + text)
 
 
 def train_and_predict_video_types(input_mood):
     # Load the dataset with user feedback as NaN initially
-    df = pd.read_csv('youtube_data.csv')
+    df = pd.read_csv('backend/ml_models/recommanded_system/youtube_data.csv')
     # Fill missing user_feedback values with 0
     df['user_feedback'].fillna(0, inplace=True)
 

backend/ml_models/recommanded_system/youtube_search.py

@@ -76,7 +76,9 @@
 
 
 def search_youtube_videos(video_types_probabilities):
-    api_key = os.getenv("YOUTUBE_API_KEY")
+    # api_key = os.getenv("YOUTUBE_API_KEY")
+    api_key = "AIzaSyBLplcZSVmjupMn-6drtByHPuFFuQui4MI"
+
     max_videos = sum(video_types_probabilities.values())
     videos_info = []
 
@@ -137,23 +139,34 @@ def get_youtube_videos_from_preferences(api_key, categories, max_results=5):
 def youtube_lists(input):
     try:
         # Parse the JSON data
+        api_key = "AIzaSyBLplcZSVmjupMn-6drtByHPuFFuQui4MI"
 
         category_list = input["categories"]
-        print(category_list)
+        # print(category_list)
         # print(category_list)
         # categories = ["Gaming"]
-        videos = get_youtube_videos_from_preferences(os.getenv("YOUTUBE_API_KEY"), category_list)
+
+        videos = get_youtube_videos_from_preferences(api_key, category_list)
+        # videos = ["FLDAuYJy4Tw", "XGdYhRxHNAQ", "MhZhezlx8ZU", "BejOWDwJYYM", "lg-oBTPvlgU"]
+
         # videos = get_youtube_videos_from_preferences(API_KEY, categories)
         # print(videos)
         # # for video in videos:
         # #     print(video)
 
+        output = {"result": videos}
+        # # output = {"result": ["lTxn2BuqyzU", "MU0HhH4dIHs", "i5gbWhRW_ZA", "FUqttxtk8y0", "8PD-Smkxsj0", "nqf8-vB_PWE", "TQgWzalijAw", "JbHk0ZjYM04", "GkIA4ldWRis", "puy5WYx1Wok", "DwuJeGYlYyw", "4GvI7M8PyKs", "n_NfxUQCoXE", "Fjp2TdlTTIU", "SCyFBeIgaDc"]}
+
+        output_json = json.dumps(output)
+        # # sys.stdout.flush()
+        # # print(result)
+        print(output_json)
         # json_string = json.dumps(videos)
         # json_string = json.dumps(videos)
         # print(json_string)
 
         # videos = videos.split(',')
-        print(videos)
+        # print(videos)
         # return videos
 
     except Exception as e:
@@ -170,8 +183,10 @@ def youtube_lists(input):
     # data = json.loads(input)
     # youtube_lists(input)
     input = {"categories" : [input]}
+    # input = ["Film & Animation","Music","Shows"]
     youtube_lists(input)
     # print(input)
+    # print(input)
 
 
 

backend/ml_models/spotify_recommendation/spotifyRecommendExecution.py

@@ -26,25 +26,30 @@
 def script_run(input_mood):
     try:
 
-        file_path = '/usr/src/app/ml_models/spotify_recommendation/tokenizer.pkl'
+        # file_path = '/usr/src/app/ml_models/spotify_recommendation/tokenizer.pkl'
         # file_path = '../backend/ml_models/spotify_recommendation/tokenizer.pkl'
-        if os.path.exists(file_path):
-            with open('/usr/src/app/ml_models/spotify_recommendation/tokenizer.pkl', 'rb') as f:
+        # if os.path.exists(file_path):
+            # with open('/usr/src/app/ml_models/spotify_recommendation/tokenizer.pkl', 'rb') as f:
             # with open('../backend/ml_models/spotify_recommendation/tokenizer.pkl', 'rb') as f:
-                scaler = pickle.load(f)
+                # scaler = pickle.load(f)
 
-        model = tf.keras.models.load_model('/usr/src/app/ml_models/spotify_recommendation/spotify_model')
+        # model = tf.keras.models.load_model('/usr/src/app/ml_models/spotify_recommendation/spotify_model')
         # model = tf.keras.models.load_model('../backend/ml_models/spotify_recommendation/spotify_model')
 
-        result = spotifyRecommendScript.getRecommendation(input_mood, model, scaler)
-        result = json.dumps(result)
+        # result = spotifyRecommendScript.getRecommendation(input_mood, model, scaler)
+
+        # print({input_mood})
+        # result = '["1HNkqx9Ahdgi1Ixy2xkKkL", "1ei3hzQmrgealgRKFxIcWn", "7eJMfftS33KTjuF7lTsMCx"]'
+        # result = '["1bzsi0xA8MxBZICdtB23pO", "6XSCLKQcavtn1PaLAhmo1x", "0sfCRhw6gS3b1WepmTVPZE", "6u8KnNnGthVY4PaWiH2mgt", "4FIGOj3O3cyIf6Dg2TDoVv", "0FSk1OsGWoTCS8lA74x5lF", "55eUtE9v3uatR0y0NijM6N", "1bzsi0xA8MxBZICdtB23pO", "6XSCLKQcavtn1PaLAhmo1x", "0sfCRhw6gS3b1WepmTVPZE"]'
+        result = '["0wjno8lHmDep5uhLVowoSA", "42maX10SkL9u6KM2dCdgPm", "0ABeQP5ffDRiZTe3aorRGn", "3QPFWCPWmL7fed8jCQ8jty", "59s47nDkFz1UNXujnpzEPt", "4c2JHXheN3t6Wh13lqj91s", "6eABugchKfRPyrzUpv7UzJ", "42maX10SkL9u6KM2dCdgPm", "6PJrnbdGmbiXjFceEFlnaR", "4cFm2ZZcb4mQMXwKCeJgzs"]'
+        # result = json.dumps(result)
         # response = str(result)
         output = {"result": result}
 
-        # output_json = json.dumps(output)
+        output_json = json.dumps(output)
         # sys.stdout.flush()
         # print(result)
-        print(output)
+        print(output_json)
 
     except Exception as e:
         error_message = str(e)
@@ -87,6 +92,7 @@ def script_run(input_mood):
 
     # Call the function with the received input_mood
     script_run(input_mood)
+    # print({input_mood})
 
     #     # script_run("happiness")
 

backend/models/User.model.js

@@ -22,6 +22,16 @@ export const UserSchema  = new mongoose.Schema({
     address: { type: String},
     profile: { type: String},
     recommendation: { type: String},
+
+    emotions: {
+        Monday: { Joy: Number, Surprise: Number, Anger: Number, Sad: Number, Happy: Number },
+        Tuesday: { Joy: Number, Surprise: Number, Anger: Number, Sad: Number, Happy: Number },
+        Wednesday: { Joy: Number, Surprise: Number, Anger: Number, Sad: Number, Happy: Number },
+        Thursday: { Joy: Number, Surprise: Number, Anger: Number, Sad: Number, Happy: Number },
+        Friday: { Joy: Number, Surprise: Number, Anger: Number, Sad: Number, Happy: Number },
+        Saturday: { Joy: Number, Surprise: Number, Anger: Number, Sad: Number, Happy: Number },
+        Sunday: { Joy: Number, Surprise: Number, Anger: Number, Sad: Number, Happy: Number },
+    },
 });
 
 export default mongoose.model.Users || mongoose.model('User', UserSchema );

frontend/src/components/chart.js

@@ -3,19 +3,14 @@ import { Chart } from 'react-google-charts';
 
 const EmotionLineChart = () => {
   const data = [
-    ['Month', 'Joyful', 'Surprise', 'Anger', 'Sad', 'Happy'],
-    ['January', 20, 15, 10, 5, 8],
-    ['February', 18, 13, 11, 7, 10],
-    ['March', 22, 17, 9, 4, 6],
-    ['April', 25, 12, 15, 6, 9],
-    ['May', 16, 20, 11, 8, 12],
-    ['June', 19, 18, 8, 7, 11],
-    ['July', 23, 16, 13, 5, 9],
-    ['August', 17, 14, 12, 9, 7],
-    ['September', 20, 19, 14, 4, 8],
-    ['October', 18, 15, 10, 6, 10],
-    ['November', 21, 17, 9, 5, 11],
-    ['December', 24, 20, 11, 7, 6],
+    ['Day', 'Joyful', 'Surprise', 'Anger', 'Sad', 'Happy'],
+    ['Monday', 20, 15, 10, 5, 8],
+    ['Tuesday', 18, 13, 11, 7, 10],
+    ['Wednesday', 22, 17, 9, 4, 6],
+    ['Tuesday', 25, 12, 15, 6, 9],
+    ['Friday', 16, 20, 11, 8, 12],
+    ['Saturday', 19, 18, 8, 7, 11],
+    ['Sunday', 23, 16, 13, 5, 9]
   ];
 
   return (