final version: todo last readme

.gitignore

@@ -163,4 +163,7 @@ cython_debug/
 *.ipynb
 
 # Vscode 
-.vscode/
+.vscode/
+
+# DS_Store
+*.DS_Store

app/js/end_game.js

@@ -28,23 +28,23 @@ document.addEventListener('DOMContentLoaded', () => {
         difficulty: difficulty
     };
 
-    // console.log('data to be sent:');
-    // console.log(JSON.stringify(postData));
-
-    // fetch('http://127.0.0.1:8000/add_score', {
-    //     method: 'POST',
-    //     headers: {
-    //         'Content-Type': 'application/json'
-    //     },
-    //     body: JSON.stringify(postData)
-    // })
-    //     .then(response => response.json())
-    //     .then(data => {
-    //         console.log('Success:', data);
-    //     })
-    //     .catch((error) => {
-    //         console.error('Error:', error);
-    //     });
+    console.log('data to be sent:');
+    console.log(JSON.stringify(postData));
+
+    fetch('http://localhost:8000/add_score', {
+        method: 'POST',
+        headers: {
+            'Content-Type': 'application/json'
+        },
+        body: JSON.stringify(postData)
+    })
+        .then(response => response.json())
+        .then(data => {
+            console.log('Success:', data);
+        })
+        .catch((error) => {
+            console.error('Error:', error);
+        });
 
     // Get the podium from the server 
     fetch('http://localhost:8000/scores')

endpoints/predict_gpu.py

@@ -171,10 +171,9 @@ async def get_labels():
 
 
 @app.get("/scores")
-async def get_scores(mode: str, difficulty: str):
+async def get_scores():
     """
-    Function to return the top 3 scores from the database
-    filtered by the given mode and difficulty.
+    Function to return all scores from the database without any filters.
     It returns a dictionary with the scores.
 
     score1: {   user: user1,
@@ -205,14 +204,9 @@ async def get_scores(mode: str, difficulty: str):
 
     data = _data[1]
     df = pd.DataFrame(data)
-    df_filtered = df[(df['mode'] == mode) & (df['difficulty'] == difficulty)]
 
-    df_filtered = df_filtered.sort_values(by=['score', 'mean_time'],
-                                          ascending=[False, True])
-
-    df_top3 = df_filtered.head(3)
-
-    scores = df_top3.to_dict(orient='records')
+    # Return the DataFrame as a dictionary without any filtering
+    scores = df.to_dict(orient='records')
 
     return scores
 

endpoints/predict_mps.py

@@ -173,10 +173,9 @@ async def get_labels():
 
 
 @app.get("/scores")
-async def get_scores(mode: str, difficulty: str):
+async def get_scores():
     """
-    Function to return the top 3 scores from the database
-    filtered by the given mode and difficulty.
+    Function to return all scores from the database without any filters.
     It returns a dictionary with the scores.
 
     score1: {   user: user1,
@@ -207,14 +206,9 @@ async def get_scores(mode: str, difficulty: str):
 
     data = _data[1]
     df = pd.DataFrame(data)
-    df_filtered = df[(df['mode'] == mode) & (df['difficulty'] == difficulty)]
 
-    df_filtered = df_filtered.sort_values(by=['score', 'mean_time'],
-                                          ascending=[False, True])
-
-    df_top3 = df_filtered.head(3)
-
-    scores = df_top3.to_dict(orient='records')
+    # Return the DataFrame as a dictionary without any filtering
+    scores = df.to_dict(orient='records')
 
     return scores