Update app.py

app.py

@@ -4,7 +4,7 @@ from langdetect import detect
 from transformers import pipeline
 import os
 
-openai.api_key = os.getenv("OPENAI_API_KEY")  # Set this as a secret in your Space settings
+openai.api_key = os.getenv("OPENAI_API_KEY")  # Set this in HF Space secrets
 
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
@@ -12,16 +12,15 @@ def process_audio(audio_path):
     if not audio_path or not isinstance(audio_path, str):
         return "No audio file provided.", "", "", ""
     try:
-        # Send audio to OpenAI Whisper API
         with open(audio_path, "rb") as audio_file:
-            transcript_response = openai.audio.transcriptions.create(
+            transcript = openai.audio.transcriptions.create(
                 model="whisper-1",
                 file=audio_file,
                 response_format="text"
             )
-        transcript = transcript_response
+        transcript = str(transcript).strip()  # Force to string
     except Exception as e:
-        return f"Error in transcription: {e}", "", "", ""
+        return f"Error in transcription: {str(e)}", "", "", ""
     try:
         detected_lang = detect(transcript)
     except Exception:
@@ -31,22 +30,22 @@ def process_audio(audio_path):
     transcript_en = transcript
     if detected_lang != "en":
         try:
-            # Re-send with task=translate for translation to English
             with open(audio_path, "rb") as audio_file:
-                translation_response = openai.audio.translations.create(
+                transcript_en = openai.audio.translations.create(
                     model="whisper-1",
                     file=audio_file,
                     response_format="text"
                 )
-            transcript_en = translation_response
+            transcript_en = str(transcript_en).strip()
         except Exception as e:
-            transcript_en = f"Error translating: {e}"
+            transcript_en = f"Error translating: {str(e)}"
     try:
         summary = summarizer(transcript_en, max_length=100, min_length=30, do_sample=False)
         summary_text = summary[0]["summary_text"]
+        summary_text = str(summary_text)
     except Exception as e:
-        summary_text = f"Error summarizing: {e}"
-    return lang_text, transcript, transcript_en, summary_text
+        summary_text = f"Error summarizing: {str(e)}"
+    return str(lang_text), str(transcript), str(transcript_en), str(summary_text)
 
 iface = gr.Interface(
     fn=process_audio,