Update app.py

app.py

@@ -69,16 +69,26 @@ def model(text):
     for response in stream:
         if not response.token.text == "</s>":
             output += response.token.text
-    return output
+    return {"choices": [{"delta": {"content": output}}]}
+
 
 # Text-to-Speech function using edge_tts
 async def generate_audio_feedback(feedback_text):
     communicate = edge_tts.Communicate(feedback_text)
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
         tmp_path = tmp_file.name
-        await communicate.save(tmp_path)
+        try:
+            await communicate.save(tmp_path)
+        except asyncio.TimeoutError:
+            logging.error("Timeout occurred during TTS generation. Retrying...")
+            try:
+                await communicate.save(tmp_path)
+            except asyncio.TimeoutError:
+                logging.error("Retry failed. Unable to generate TTS.")
+                return None
     return tmp_path
 
+
 # Generating feedback for the Oral Coach
 async def generate_feedback(user_id, question_choice, strategy_choice, message, feedback_level):
     current_question_index = thinkingframes.questions.index(question_choice)
@@ -124,9 +134,9 @@ async def generate_feedback(user_id, question_choice, strategy_choice, message,
     chat_history = []  # Initialize chat history outside the loop
     full_feedback = ""  # Accumulate the entire feedback message
     try:
-        for chunk in response:
-            if chunk.choices[0].delta and chunk.choices[0].delta.content:
-                feedback_chunk = chunk.choices[0].delta.content
+        for chunk in response["choices"]:
+            if chunk["delta"] and chunk["delta"]["content"]:
+                feedback_chunk = chunk["delta"]["content"]
                 yield feedback_chunk  # Yield each feedback chunk as it is generated
                 await asyncio.sleep(0)
 
@@ -136,6 +146,7 @@ async def generate_feedback(user_id, question_choice, strategy_choice, message,
     questionNo = current_question_index + 1
     add_submission(user_id, message, full_feedback, int(0), "", questionNo)
 
+
 # Function to predict and handle the entire workflow
 async def predict(question_choice, strategy_choice, feedback_level, audio):
     current_audio_output = None  # Initialize current_audio_output to None