import gradio as gr import tempfile import os from pathlib import Path from io import BytesIO from settings import ( respond, generate_random_string, reset_interview, generate_interview_report, generate_report_from_file, interview_history, question_count, language, ) from ai_config import convert_text_to_speech, transcribe_audio, n_of_questions 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(voice): global question_count, interview_history, selected_interviewer selected_interviewer = voice question_count = 0 interview_history.clear() 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, voice_setting) initial_audio_buffer.seek(0) with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file: temp_audio_path = temp_file.name temp_file.write(initial_audio_buffer.getvalue()) temp_audio_files.append(temp_audio_path) return ( [(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="") ) def create_app(): global initial_audio_path, selected_interviewer initial_message = get_interview_initial_message_sarah() initial_audio_buffer = BytesIO() 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: initial_audio_path = temp_file.name temp_file.write(initial_audio_buffer.getvalue()) temp_audio_files.append(initial_audio_path) with gr.Blocks(title="AI Clinical Psychologist Interviewer 𝚿") as demo: gr.Image(value="appendix/icon.jpeg", label='icon', width=20, scale=1, show_label=False, show_fullscreen_button=False, show_download_button=False, show_share_button=False) gr.Markdown( """ # Clinical Psychologist Interviewer 𝚿 This chatbot conducts clinical interviews based on psychological knowledge. The interviewer will prepare a clinical report based on the interview. * Please note that this is a simulation and should not be used as a substitute for professional medical advice. * It is important to emphasize that any information shared is confidential and cannot be accessed. * In any case, it is recommended not to share sensitive information. """ ) with gr.Tab("Interview"): with gr.Row(): reset_button = gr.Button("Select Interviewer", size='sm', scale=1) 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, ) chatbot = gr.Chatbot(value=[(None, f"{initial_message}")], label=f"Clinical Interview πšΏπŸ“‹") 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=1) send_button = gr.Button("Send") pdf_output = gr.File(label="Download Report", visible=False) def user(user_message, audio, history): if audio is not None: user_message = transcribe_audio(audio) return "", None, history + [[user_message, None]] def bot_response(chatbot, message, voice_selection): global question_count, temp_audio_files, selected_interviewer selected_interviewer = voice_selection question_count += 1 last_user_message = chatbot[-1][0] if chatbot else message voice = "alloy" if selected_interviewer == "Sarah" else "onyx" response, audio_buffer = respond(chatbot, last_user_message, voice, selected_interviewer) for bot_message in response: chatbot.append((None, bot_message[1])) if isinstance(audio_buffer, BytesIO): with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file: temp_audio_path = temp_file.name temp_file.write(audio_buffer.getvalue()) temp_audio_files.append(temp_audio_path) audio_output = gr.Audio(value=temp_audio_path, label=voice_selection, autoplay=True) else: 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, 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 = gr.Audio(value=temp_audio_path, label=voice_selection, autoplay=True) report_content, pdf_path = generate_interview_report(interview_history, language) chatbot.append((None, f"Interview Report:\n\n{report_content}")) return chatbot, audio_output, gr.File(visible=True, value=pdf_path) 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, 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, voice_radio], [chatbot, audio_output, pdf_output] ) reset_button.click( reset_interview_action, inputs=[voice_radio], outputs=[chatbot, audio_output, msg] ) with gr.Tab("Upload Document"): gr.Markdown('Please upload a document that contains content written about a patient or by the patient.') gr.Markdown('* Maximum length is up to 100K characters.') gr.Markdown('* It is important to emphasize that the uploaded document is confidential and cannot be accessed.') gr.Markdown('* In any case, it is recommended not to upload sensitive documents.') file_input = gr.File(label="Upload a TXT, PDF, or DOCX file") #language_input = gr.Textbox(label="Preferred Language for Report") language_input = 'English' generate_button = gr.Button("Generate Report") report_output = gr.Textbox(label="Generated Report", lines=100, visible=False) pdf_output = gr.File(label="Download Report", visible=True) def generate_report_and_pdf(file, language): report_content, pdf_path = generate_report_from_file(file, language) return report_content, pdf_path, gr.File(visible=True) generate_button.click( generate_report_and_pdf, inputs=[file_input], outputs=[report_output, pdf_output, pdf_output] ) with gr.Tab("Description"): with open('appendix/description.txt', 'r', encoding='utf-8') as file: description_txt = file.read() gr.Markdown(description_txt) gr.HTML("
") 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(): global temp_audio_files, initial_audio_path for audio_file in temp_audio_files: if os.path.exists(audio_file): os.unlink(audio_file) temp_audio_files.clear() if initial_audio_path and os.path.exists(initial_audio_path): os.unlink(initial_audio_path) if __name__ == "__main__": app = create_app() try: app.launch() finally: cleanup()