Update app.py

app.py

@@ -6,6 +6,7 @@ import random
 import librosa
 import soundfile as sf
 import pandas as pd
+from gradio_client import Client, handle_file
 from transformers import pipeline
 from datasets import load_dataset, Audio
 from gradio_client import Client
@@ -98,11 +99,11 @@ def process_audio_step_2(audio_path, norm_whisper):
         # Connect to the private API
         client = Client(PRIVATE_BACKEND_URL, token=HF_TOKEN)
         
-        # Call the endpoint 'predict_dsr' defined in the Private Space
-        # We send the audio file and the normalized whisper transcript
+        # FIX: Wrap audio_path with handle_file()
+        # This sends the metadata required by Pydantic ('gradio.FileData')
         prediction = client.predict(
-            audio_path, 
-            norm_whisper, 
+            audio_path=handle_file(audio_path), 
+            whisper_norm=norm_whisper, 
             api_name="/predict_dsr"
         )
         return prediction