Update app.py

app.py

@@ -49,10 +49,13 @@ def transcribe_in_background(audio_url, file_id, company, user):
         # 2) Load with librosa
         waveform, sr = librosa.load(audio_path, sr=16000)
 
-        # Optional: limit ~1 hour
+        # Optional: limit to ~1 hour
         max_sec = 3600
         waveform = waveform[: sr * max_sec]
 
+        # Calculate total duration actually analyzed
+        call_duration = int(len(waveform) / sr)  # Rounded to nearest second
+
         # 3) Split audio into 25-second chunks
         chunk_sec = 25
         chunk_size = sr * chunk_sec
@@ -72,9 +75,10 @@ def transcribe_in_background(audio_url, file_id, company, user):
             transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
             partial_text += transcription + "\n"
 
-        # 4) Post final transcription back to Make.com, including the extra fields
+        # 4) Post final transcription back to Make.com, including extra fields
         payload = {
             "Transcription": partial_text.strip(),
+            "callDuration": call_duration,
             "fileId": file_id,
             "company": company,
             "user": user
@@ -88,6 +92,7 @@ def transcribe_in_background(audio_url, file_id, company, user):
             "fileId": file_id,
             "company": company,
             "user": user
+            # You could optionally include "callDuration" here if relevant
         }
         requests.post(WEBHOOK_URL, json=error_payload)