output as json with processing

app.py

@@ -4,6 +4,45 @@ from parselmouth.praat import call
 import numpy as np
 import pandas as pd
 
+def find_rises_and_peaks_gradient(data, threshold=4):
+    data['rise_point'] = 0
+    data['peak_point'] = 0
+    
+    pitch_values = data['pitch'].values
+    gradients = np.gradient(pitch_values)
+    
+    in_rise = False
+    rise_start = 0
+    successive_rise_count = 0
+    min_successive_rise = 3  # Minimum successive values to qualify as a rise
+    checking_rise = False  # Flag to start checking for rises after NaN
+
+    for i in range(1, len(gradients)):
+        if np.isnan(pitch_values[i]):
+            checking_rise = False  # Reset flag when encountering NaN
+            in_rise = False
+            successive_rise_count = 0
+            continue
+        
+        if not checking_rise:
+            checking_rise = True  # Start checking for rises after NaN
+            continue
+        
+        if gradients[i] >= threshold:
+            if not in_rise:
+                in_rise = True
+                rise_start = i-1
+            successive_rise_count += 1
+        else:
+            if in_rise:
+                if successive_rise_count >= min_successive_rise:
+                    data.at[rise_start, 'rise_point'] = 1
+                    data.at[i-1, 'peak_point'] = 1
+                in_rise = False
+                successive_rise_count = 0
+
+    return data
+
 def get_pitch(audio_data):
     rate, data = audio_data
     if data.ndim > 1:  # Check if the audio is stereo or multi-channel
@@ -21,7 +60,11 @@ def get_pitch(audio_data):
         df_pitch = pd.DataFrame(np.column_stack([pitch.xs(), pitch_values]), 
                                 columns=['time', 'pitch']) 
         
-        return df_pitch
+        df_pitch = find_rises_and_peaks_gradient(df_pitch)
+
+        output = df_pitch.to_json(orient='records')
+        
+        return output
     except Exception as e:
         return "Error in pitch extraction: " + str(e)