spotify array printing correction

backend/controllers/mlController.js

@@ -248,18 +248,18 @@ export async function spotify_recommend(req, res) {
 
     // Listen for data events from the Python script's stdout
     pythonProcess.stdout.on("data", (data) => {
-      output += data;
-      // output += data;
+      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).send(output);
+          res.status(200).json(result);
           // res.status(200).json({"result":output});
         } catch (error) {
           res.status(500).json({ error: "Failed to parse JSON response" });

backend/ml_models/recommanded_system/test.js

@@ -0,0 +1,15 @@
+
+
+// # The JSON string
+var json_str = {'result': '["090fexGQyBy6tjejeKZSAN", "090fexGQyBy6tjejeKZSAN", "090fexGQyBy6tjejeKZSAN", "090fexGQyBy6tjejeKZSAN"]'}
+
+// Parse the JSON string
+// var jsonData = JSON.parse(json_str);
+
+// Access the list inside the 'result' key
+var resultList = JSON.parse(json_str.result);
+
+// Now you can work with the list
+resultList.forEach(function(item) {
+  console.log(item);
+});

backend/ml_models/spotify_recommendation/.cache

@@ -1 +1 @@
-{"access_token": "BQC7oaTQMhH1cupHvmFFaUkZ8DeZKc0fN0M96sYkhe6-A35ewKfGDDTjo7QC--dmpLJ1WZfYIp8rl4pnjTwQmw7IQEl0oEwkaHgR7oZqJ98ch0opJaWEtKVuUJUA79EuuyvERBCqxRZz_lbV-kiETs_dWCT5GoZhpFaPNOBKqQL2SRFZ3FzHZ18m80NlxZ_1o-5_oAoYRkq63NUAgp6VB1vkdZKuSS4JBmfnZw", "token_type": "Bearer", "expires_in": 3600, "scope": "playlist-read-private user-library-read user-read-recently-played user-top-read", "expires_at": 1698431800, "refresh_token": "AQDPFQQaeVerMmQwRulEXf-J4KgjjUINngJ-7MU19CyzrhZMYfh20NqYWXpHpc-XVrLBe7_gig_4zOmcnQCE2DvOrjAUUaK5YigF0Wg2C4f-NYpccD2MBMTl7qZ4vm3falU"}
+{"access_token": "BQCOQJMEAu3mc1Pk9QX_XLcX7mXJh4lWaz7JvRvEdhhty4euS2Uh3IzKh0R5AJ1uzRqbr6-9qWV4mnl5YooPwXfHl3Z7HdOWmRJvDMoHCZJ37S8yJ3G9jxDAtu8u_yQ3l1yUSzML3n_SI3rP_ny5-Y2WiTKCcufLwossCnnD-HXleWNpdRdsf36zFf1SNNxUTs-Oxv5z3PQxylRKcY0C3KpltfXzu7miNZqFyQ", "token_type": "Bearer", "expires_in": 3600, "scope": "playlist-read-private user-library-read user-read-recently-played user-top-read", "expires_at": 1698518625, "refresh_token": "AQDPFQQaeVerMmQwRulEXf-J4KgjjUINngJ-7MU19CyzrhZMYfh20NqYWXpHpc-XVrLBe7_gig_4zOmcnQCE2DvOrjAUUaK5YigF0Wg2C4f-NYpccD2MBMTl7qZ4vm3falU"}

backend/ml_models/spotify_recommendation/spotifyRecommendExecution.py

@@ -33,13 +33,14 @@ def script_run(input_mood):
         # 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 = json.dumps(result)
         # response = str(result)
-        # output = {"result": result}
+        output = {"result": result}
 
         # output_json = json.dumps(output)
         # sys.stdout.flush()
-        print(result)
+        # print(result)
+        print(output)
 
     except Exception as e:
         error_message = str(e)

backend/ml_models/spotify_recommendation/spotifyRecommendScript.py

@@ -12,6 +12,7 @@
 import pickle
 import os
 import config as cf
+import contextlib
 
 
 
@@ -141,10 +142,16 @@ def initialize():
 def getRecommendation(mood, model, scaler):
 
     df_recentSongs = getRecentlyPlayed()
-    df2 = pd.DataFrame(model.predict(scaler.fit_transform(df_recentSongs.iloc[:, 1:])))
+
+    # Use contextlib.redirect_stdout to suppress output
+    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)
     df_recentSongs['Mood'] = df2['Mood'].apply(decodeLabels)
     filtered_df = df_recentSongs[df_recentSongs['Mood']==mood]
+
     return filtered_df.tail(10)[0].tolist()[::-1]
 
 if __name__ == "__main__":

frontend/src/pages/AI_Assistant.js

@@ -144,7 +144,7 @@ const AI_Assistant = () => {
          .post('http://localhost:5001/api/emotion_analyze', { message: message })
          .then((response) => {
             setEmotion(response.data.emotion);
-            // console.log(response.data.emotion)
+            console.log(response.data.emotion)
          })
          .catch((error) => console.error(error));
       // Clear the input field after submitting