import gradio as gr
import tempfile
import os
from pathlib import Path
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, n_of_questions
from prompt_instructions import get_interview_initial_message

# Global variables
temp_mp3_files = []
initial_audio_path = None


# Initialize Gradio interface
def create_app():
    global initial_audio_path
    initial_message = get_interview_initial_message()

    # Generate and save the audio for the initial message in a temporary file
    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_initial_audio:
        initial_audio_path = temp_initial_audio.name
        convert_text_to_speech(initial_message, initial_audio_path)
        temp_mp3_files.append(initial_audio_path)

    with gr.Blocks(title="Clinical Psychologist Interviewer 𝚿") as demo:
        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.
            """
        )

        with gr.Tab("Interview"):
            audio_output = gr.Audio(
                label="Sarah",
                scale=1,
                value=initial_audio_path,
                autoplay=True,
                visible=False,
                show_download_button=False,
            )
            chatbot = gr.Chatbot(value=[("", f"{initial_message}")], label=f"Clinical Interview 𝚿📋")
            msg = gr.Textbox(label="Type your message here...")
            send_button = gr.Button("Send")

            pdf_output = gr.File(label="Download Report", visible=False)

            def user(user_message, history):
                if not isinstance(history, list):
                    history = []
                return "", history + [[user_message, None]]

            def bot_response(chatbot, message):
                global question_count, temp_mp3_files
                question_count += 1

                response, audio = respond(chatbot, message)

                if isinstance(audio, str) and audio.endswith('.mp3'):
                    temp_mp3_files.append(audio)

                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!"
                    response.append((message, conclusion_message))

                    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_audio:
                        audio_path = temp_audio.name
                        convert_text_to_speech(conclusion_message, audio_path)
                        audio = audio_path
                        temp_mp3_files.append(audio_path)

                    # Generate report automatically
                    report_content, _ = generate_interview_report(interview_history, language)
                    with tempfile.NamedTemporaryFile(mode='w', suffix=".txt", delete=False,
                                                     encoding='utf-8') as temp_report:
                        temp_report.write(report_content)
                        temp_report_path = temp_report.name

                    _, pdf_path = generate_report_from_file(temp_report_path, language)

                    # Add report to the chat
                    response.append(("", f"Interview Report:\n\n{report_content}"))

                    # Clean up temporary files
                    os.unlink(temp_report_path)

                    # Clean up all MP3 files
                    for mp3_file in temp_mp3_files:
                        if os.path.exists(mp3_file):
                            os.unlink(mp3_file)
                    temp_mp3_files.clear()

                    return response, audio, gr.File(visible=True, value=pdf_path), gr.Textbox(visible=False)

                return response, audio, gr.File(visible=False), gr.Textbox(visible=True)

            msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
                bot_response, [chatbot, msg], [chatbot, audio_output, pdf_output, msg]
            )

            send_button.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
                bot_response, [chatbot, msg], [chatbot, audio_output, pdf_output, msg]
            )

        with gr.Tab("Upload Document"):
            file_input = gr.File(label="Upload a TXT, PDF, or DOCX file")
            language_input = gr.Textbox(label="Preferred Language for Report",
                                        placeholder="Enter language")
            generate_button = gr.Button("Generate Report")
            report_output = gr.Textbox(label="Generated Report", lines=100)
            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, language_input],
                outputs=[report_output, pdf_output, pdf_output]
            )

    return demo


# Clean up function
def cleanup():
    global temp_mp3_files, initial_audio_path
    for mp3_file in temp_mp3_files:
        if os.path.exists(mp3_file):
            os.unlink(mp3_file)
    temp_mp3_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()