Update app.py

app.py

@@ -14,22 +14,30 @@ from settings import (
     language,
 )
 from ai_config import convert_text_to_speech, transcribe_audio, n_of_questions
-from prompt_instructions import get_interview_initial_message
+from prompt_instructions import get_interview_initial_message_sarah, get_interview_initial_message_aaron
 
 # Global variables
 temp_audio_files = []
 initial_audio_path = None
+selected_interviewer = "Sarah"
 
-
-def reset_interview_action():
-    global question_count, interview_history
+def reset_interview_action(voice):
+    global question_count, interview_history, selected_interviewer
+    selected_interviewer = voice
     question_count = 0
     interview_history.clear()
-    initial_message = get_interview_initial_message()
 
-    # Generate new audio for the initial message
+    if voice == "Sarah":
+        initial_message = get_interview_initial_message_sarah()
+        voice_setting = "alloy"
+    else:
+        initial_message = get_interview_initial_message_aaron()
+        voice_setting = "onyx"
+
+    initial_message = str(initial_message)
+
     initial_audio_buffer = BytesIO()
-    convert_text_to_speech(initial_message, initial_audio_buffer)
+    convert_text_to_speech(initial_message, initial_audio_buffer, voice_setting)
     initial_audio_buffer.seek(0)
 
     with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file:
@@ -39,21 +47,17 @@ def reset_interview_action():
     temp_audio_files.append(temp_audio_path)
 
     return (
-        [(None, initial_message)],
-        temp_audio_path,
-        gr.File(visible=False),
-        gr.Textbox(visible=True),
-        "Interview reset. You can start a new interview now."
+        [(None, initial_message[0] if isinstance(initial_message, tuple) else initial_message)],
+        gr.Audio(value=temp_audio_path, label=voice, autoplay=True),
+        gr.Textbox(value="")
     )
 
-# Initialize Gradio interface
 def create_app():
-    global initial_audio_path
-    initial_message = get_interview_initial_message()
+    global initial_audio_path, selected_interviewer
+    initial_message = get_interview_initial_message_sarah()
 
-    # Generate the audio for the initial message and save to a temporary file
     initial_audio_buffer = BytesIO()
-    convert_text_to_speech(initial_message, initial_audio_buffer)
+    convert_text_to_speech(initial_message, initial_audio_buffer, "alloy")
     initial_audio_buffer.seek(0)
 
     with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file:
@@ -63,53 +67,53 @@ def create_app():
     temp_audio_files.append(initial_audio_path)
 
     with gr.Blocks(title="Clinical Psychologist Interviewer 𝚿") as demo:
-        gr.Image(value="appendix/icon.jpeg", label='icon', width=20, scale=1, show_label=False, show_download_button=False, show_share_button=False, show_fullscreen_button=False)
+        gr.Image(value="appendix/icon.jpeg", label='icon', width=20, scale=1, show_label=False,
+                 show_download_button=False, show_share_button=False)
         gr.Markdown(
             """
             # Clinical Psychologist Interviewer 𝚿
             This chatbot conducts clinical interviews based on psychological knowledge.
             Please note that this is a simulation and should not be used as a substitute for professional medical advice.
-            
+
             The interviewer will prepare a clinical report based on the interview.
             """
         )
 
         with gr.Tab("Interview"):
-            audio_output = gr.Audio(
-                label="Sarah",
-                scale=1,
-                value=initial_audio_path,
-                autoplay=True,
-                visible=True,
-                show_download_button=False,
-            )
+            with gr.Row():
+                reset_button = gr.Button("Select Interviewer", size='sm', scale=1, icon='appendix/psi.png')
+                voice_radio = gr.Radio(["Sarah", "Aaron"], label="Select Interviewer", value="Sarah", scale=1, info='Each interviewer has a unique approach and a different professional background.')
+                audio_output = gr.Audio(
+                    label="Sarah",
+                    scale=3,
+                    value=initial_audio_path,
+                    autoplay=True,
+                    visible=True,
+                    show_download_button=False,
+                )
 
-            reset_button = gr.Button("Reset Interview", size='sm')
             chatbot = gr.Chatbot(value=[(None, f"{initial_message}")], label=f"Clinical Interview 𝚿📋")
             with gr.Row():
                 msg = gr.Textbox(label="Type your message here...", scale=3)
-                audio_input = gr.Audio(sources=(["microphone"]), label="Record your message", type="filepath", scale=2)
+                audio_input = gr.Audio(sources=(["microphone"]), label="Record your message", type="filepath", scale=1)
             send_button = gr.Button("Send")
             pdf_output = gr.File(label="Download Report", visible=False)
 
             def user(user_message, audio, history):
-                print(audio)
                 if audio is not None:
                     user_message = transcribe_audio(audio)
-                    print(user_message)
-
                 return "", None, history + [[user_message, None]]
 
-            def bot_response(chatbot, message):
-                global question_count, temp_audio_files
+            def bot_response(chatbot, message, voice_selection):
+                global question_count, temp_audio_files, selected_interviewer
+                selected_interviewer = voice_selection
                 question_count += 1
 
-                # Use the last user message from the chatbot history
                 last_user_message = chatbot[-1][0] if chatbot else message
 
-                response, audio_buffer = respond(chatbot, last_user_message)
+                voice = "alloy" if selected_interviewer == "Sarah" else "onyx"
+                response, audio_buffer = respond(chatbot, last_user_message, voice, selected_interviewer)
 
-                # Add all bot responses to the chatbot history
                 for bot_message in response:
                     chatbot.append((None, bot_message[1]))
 
@@ -118,46 +122,43 @@ def create_app():
                         temp_audio_path = temp_file.name
                         temp_file.write(audio_buffer.getvalue())
                     temp_audio_files.append(temp_audio_path)
-                    audio_output = temp_audio_path
+                    audio_output = gr.Audio(value=temp_audio_path, label=voice_selection, autoplay=True)
                 else:
-                    audio_output = audio_buffer
+                    audio_output = gr.Audio(value=audio_buffer, label=voice_selection, autoplay=True)
 
                 if question_count >= n_of_questions():
                     conclusion_message = "Thank you for participating in this interview. We have reached the end of our session. I hope this conversation has been helpful. Take care!"
                     chatbot.append((None, conclusion_message))
 
                     conclusion_audio_buffer = BytesIO()
-                    convert_text_to_speech(conclusion_message, conclusion_audio_buffer)
+                    convert_text_to_speech(conclusion_message, conclusion_audio_buffer, voice)
                     conclusion_audio_buffer.seek(0)
 
                     with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file:
                         temp_audio_path = temp_file.name
                         temp_file.write(conclusion_audio_buffer.getvalue())
                     temp_audio_files.append(temp_audio_path)
-                    audio_output = temp_audio_path
+                    audio_output = gr.Audio(value=temp_audio_path, label=voice_selection, autoplay=True)
 
-                    # Generate report automatically
                     report_content, pdf_path = generate_interview_report(interview_history, language)
-
-                    # Add report to the chat
                     chatbot.append((None, f"Interview Report:\n\n{report_content}"))
 
-                    return chatbot, audio_output, gr.File(visible=True, value=pdf_path), gr.Textbox(visible=False)
+                    return chatbot, audio_output, gr.File(visible=True, value=pdf_path)
 
-                return chatbot, audio_output, gr.File(visible=False), gr.Textbox(visible=True)
+                return chatbot, audio_output, gr.File(visible=False)
 
             msg.submit(user, [msg, audio_input, chatbot], [msg, audio_input, chatbot], queue=False).then(
-                bot_response, [chatbot, msg], [chatbot, audio_output, pdf_output, msg]
+                bot_response, [chatbot, msg, voice_radio], [chatbot, audio_output, pdf_output]
             )
 
             send_button.click(user, [msg, audio_input, chatbot], [msg, audio_input, chatbot], queue=False).then(
-                bot_response, [chatbot, msg], [chatbot, audio_output, pdf_output, msg]
+                bot_response, [chatbot, msg, voice_radio], [chatbot, audio_output, pdf_output]
             )
 
             reset_button.click(
                 reset_interview_action,
-                inputs=[],
-                outputs=[chatbot, audio_output, pdf_output, msg, audio_input]
+                inputs=[voice_radio],
+                outputs=[chatbot, audio_output, msg]
             )
 
         with gr.Tab("Upload Document"):
@@ -178,14 +179,14 @@ def create_app():
                 outputs=[report_output, pdf_output, pdf_output]
             )
         with gr.Tab("Description"):
-            with open('appendix/description.txt', 'r') as file:
+            with open('appendix/description.txt', 'r', encoding='utf-8') as file:
                 description_txt = file.read()
             gr.Markdown(description_txt)
             gr.HTML("<div style='height: 15px;'></div>")
-            gr.Image(value="appendix/diagram.png", label='diagram', width=700, scale=1, show_label=False, show_download_button=False, show_share_button=False)
-            
-    return demo
+            gr.Image(value="appendix/diagram.png", label='diagram', width=700, scale=1, show_label=False,
+                     show_download_button=False, show_share_button=False)
 
+    return demo
 
 # Clean up function
 def cleanup():
@@ -198,7 +199,6 @@ def cleanup():
     if initial_audio_path and os.path.exists(initial_audio_path):
         os.unlink(initial_audio_path)
 
-
 if __name__ == "__main__":
     app = create_app()
     try: