Update app.py

app.py

@@ -20,6 +20,7 @@ initial_message = {"role": "system", "content": 'You are a Renal System USMLE Tu
                    to all input with a fun mneumonics to memorize that list. But you can answer up to 1200 words if the user requests longer response. \
                     You are going to keep answer and also challenge the student to learn renal phsysiology.'}
 messages = [initial_message]
+messages_rev = [initial_message]
 
 # Define the answer counter
 answer_count = 0
@@ -62,7 +63,7 @@ def transcribe(audio, text):
         input_text = tokenizer.decode(input_tokens)
     
         # Add the input text to the messages list
-    # messages.append({"role": "user", "content": transcript["text"]+input_text})
+    messages.append({"role": "user", "content": transcript["text"]+input_text})
 
 
     # Check if the accumulated tokens have exceeded 2096
@@ -104,16 +105,15 @@ def transcribe(audio, text):
     # Wait for the completion of the OpenAI API call
         
     # Add the system message to the messages list
-    # messages.append(system_message)
+    messages.append(system_message)
         
     # Add the system message to the beginning of the messages list
-    messages.insert(0, system_message)
+    messages_rev.insert(0, system_message)
     # Add the input text to the messages list
-    messages.insert(0, {"role": "user", "content": input_text + transcript["text"]})
+    messages_rev.insert(0, {"role": "user", "content": input_text + transcript["text"]})
 
-    
     # Concatenate the chat history
-    chat_transcript = "\n\n".join([f"[ANSWER {answer_count}]{message['role']}: {message['content']}" for message in messages if message['role'] != 'system'])
+    chat_transcript = "\n\n".join([f"[ANSWER {answer_count}]{messages_rev['role']}: {messages_rev['content']}" for messages_rev in messages_rev if messages_rev['role'] != 'system'])
 
     # chat_transcript_copy = chat_transcript
     # Append the number of tokens used to the end of the chat transcript