Update qa.py

qa.py

@@ -7,7 +7,6 @@ from utils import generate_audio_mp3
 def transcribe_audio_deepgram(local_audio_path: str) -> str:
     """
     Sends a local audio file to Deepgram for STT (Speech-to-Text).
-    Returns the transcript text if successful, or raises an error if failed.
     """
     DEEPGRAM_API_KEY = os.environ.get("DEEPGRAM_API_KEY")
     if not DEEPGRAM_API_KEY:
@@ -24,14 +23,11 @@ def transcribe_audio_deepgram(local_audio_path: str) -> str:
     response.raise_for_status()
 
     data = response.json()
-    transcript = data["results"]["channels"][0]["alternatives"][0].get("transcript", "")
-    return transcript
-
+    return data["results"]["channels"][0]["alternatives"][0].get("transcript", "")
 
 def call_llm_for_qa(conversation_so_far: str, user_question: str) -> dict:
     """
-    Calls the LLM (Groq API) to generate a structured response to a follow-up question.
-    Returns a Python dict: {"speaker": "Guest", "text": "..."}
+    Calls LLM (Groq API) to generate a structured response to a follow-up question.
     """
     system_prompt = f"""
     You are an expert guest in a podcast session. The user is asking a follow-up question.
@@ -51,11 +47,10 @@ def call_llm_for_qa(conversation_so_far: str, user_question: str) -> dict:
     response_dict = json.loads(raw_json_response)
     return response_dict
 
-
 def handle_qa_exchange(user_question: str) -> (bytes, str):
     """
     1) Reads conversation_so_far from session_state
-    2) Calls the LLM for a short follow-up answer
+    2) Calls the LLM for a follow-up answer
     3) Generates TTS audio
     4) Returns (audio_bytes, answer_text)
     """
@@ -71,7 +66,6 @@ def handle_qa_exchange(user_question: str) -> (bytes, str):
     if not answer_text.strip():
         return (None, "")
 
-    # Generate text-to-speech audio response
     audio_file_path = generate_audio_mp3(answer_text, speaker)
     with open(audio_file_path, "rb") as f:
         audio_bytes = f.read()