chatgpt.py

import gradio as gr
import whisper
import asyncio
import httpx
import tempfile
import os
import requests
import time
import threading
from datetime import datetime, timedelta

session = requests.Session()

from interview_protocol import protocols as interview_protocols

model = whisper.load_model("base")

base_url = "https://llm4socialisolation-fd4082d0a518.herokuapp.com"
# base_url = "http://localhost:8080"

timeout = 60
concurrency_count=10

# mapping between display names and internal chatbot_type values
display_to_value = {
    'Echo': 'enhanced',
    'Breeze': 'baseline'
}

value_to_display = {
    'enhanced': 'Echo',
    'baseline': 'Breeze'
}

def get_method_index(chapter, method):
    all_methods = []
    for chap in interview_protocols.values():
        all_methods.extend(chap)
    index = all_methods.index(method)
    return index

async def initialization(api_key, chapter_name, topic_name, username, prompts, chatbot_type):
    url = f"{base_url}/api/initialization"
    headers = {'Content-Type': 'application/json'}
    data = {
        'api_key': api_key,
        'chapter_name': chapter_name,
        'topic_name': topic_name,
        'username': username,
        'chatbot_type': chatbot_type, 
        **prompts
    }
    async with httpx.AsyncClient(timeout=timeout) as client:
        try:
            response = await client.post(url, json=data, headers=headers)
            if response.status_code == 200:
                return "Initialization successful."
            else:
                return f"Initialization failed: {response.text}"
        except asyncio.TimeoutError:
            print("The request timed out")
            return "Request timed out during initialization."
        except Exception as e:
            return f"Error in initialization: {str(e)}"

def fetch_default_prompts(chatbot_type):
    url = f"{base_url}?chatbot_type={chatbot_type}"
    try:
        response = httpx.get(url, timeout=timeout)
        if response.status_code == 200:
            prompts = response.json()
            print(prompts)
            return prompts
        else:
            print(f"Failed to fetch prompts: {response.status_code} - {response.text}")
            return {}
    except Exception as e:
        print(f"Error fetching prompts: {str(e)}")
        return {}

async def get_backend_response(api_key, patient_prompt, username, chatbot_type):
    url = f"{base_url}/responses/doctor"
    headers = {'Content-Type': 'application/json'}
    data = {
        'username': username,
        'patient_prompt': patient_prompt,
        'chatbot_type': chatbot_type
    }
    async with httpx.AsyncClient(timeout=timeout) as client:
        try:
            response = await client.post(url, json=data, headers=headers)
            if response.status_code == 200:
                response_data = response.json()
                return response_data
            else:
                return f"Failed to fetch response from backend: {response.text}"
        except Exception as e:
            return f"Error contacting backend service: {str(e)}"

async def save_conversation_and_memory(username, chatbot_type):
    url = f"{base_url}/save/end_and_save"
    headers = {'Content-Type': 'application/json'}
    data = {
        'username': username,
        'chatbot_type': chatbot_type
    }
    async with httpx.AsyncClient(timeout=timeout) as client:
        try:
            response = await client.post(url, json=data, headers=headers)
            if response.status_code == 200:
                response_data = response.json()
                return response_data.get('message', 'Saving Error!')
            else:
                return f"Failed to save conversations and memory graph: {response.text}"
        except Exception as e:
            return f"Error contacting backend service: {str(e)}"

async def get_conversation_histories(username, chatbot_type):
    url = f"{base_url}/save/download_conversations"
    headers = {'Content-Type': 'application/json'}
    data = {
        'username': username,
        'chatbot_type': chatbot_type
    }
    async with httpx.AsyncClient(timeout=timeout) as client:
        try:
            response = await client.post(url, json=data, headers=headers)
            if response.status_code == 200:
                conversation_data = response.json()
                return conversation_data
            else:
                return []
        except Exception as e:
            return []

def download_conversations(username, chatbot_type):
    conversation_histories = asyncio.run(get_conversation_histories(username, chatbot_type))
    files = []
    temp_dir = tempfile.mkdtemp()
    for conversation_entry in conversation_histories:
        file_name = conversation_entry.get('file_name', f"Conversation_{len(files)+1}.txt")
        conversation = conversation_entry.get('conversation', [])
        conversation_text = ""
        for message_pair in conversation:
            if isinstance(message_pair, list) and len(message_pair) == 2:
                speaker, message = message_pair
                conversation_text += f"{speaker.capitalize()}: {message}\n\n"
            else:
                conversation_text += f"Unknown format: {message_pair}\n\n"
        temp_file_path = os.path.join(temp_dir, file_name)
        with open(temp_file_path, 'w') as temp_file:
            temp_file.write(conversation_text)
        files.append(temp_file_path)
    return files

async def get_biography(username, chatbot_type):
    url = f"{base_url}/save/generate_autobiography"
    headers = {'Content-Type': 'application/json'}
    data = {
        'username': username,
        'chatbot_type': chatbot_type
    }
    async with httpx.AsyncClient(timeout=timeout) as client:
        try:
            response = await client.post(url, json=data, headers=headers)
            if response.status_code == 200:
                biography_data = response.json()
                biography_text = biography_data.get('biography', '')
                return biography_text
            else:
                return "Failed to generate biography."
        except Exception as e:
            return f"Error contacting backend service: {str(e)}"

def download_biography(username, chatbot_type):
    biography_text = asyncio.run(get_biography(username, chatbot_type))
    if not biography_text or "Failed" in biography_text or "Error" in biography_text:
        return gr.update(value=None, visible=False), gr.update(value=biography_text, visible=True)
    temp_dir = tempfile.mkdtemp()
    temp_file_path = os.path.join(temp_dir, "biography.txt")
    with open(temp_file_path, 'w') as temp_file:
        temp_file.write(biography_text)
    return temp_file_path, gr.update(value=biography_text, visible=True)

def transcribe_audio(audio_file):
    transcription = model.transcribe(audio_file)["text"]
    return transcription

def submit_text_and_respond(edited_text, api_key, username, history, chatbot_type):
    response = asyncio.run(get_backend_response(api_key, edited_text, username, chatbot_type))
    print('------')
    print(response)
    if isinstance(response, str):
        history.append((edited_text, response))
        return history, "", []
    doctor_response = response['doctor_response']['response']
    memory_event = response.get('memory_events', [])
    history.append((edited_text, doctor_response))
    memory_graph = update_memory_graph(memory_event)
    return history, "", memory_graph  # Return memory_graph as output

def set_initialize_button(api_key_input, chapter_name, topic_name, username_input,
                          system_prompt_text, conv_instruction_prompt_text, therapy_prompt_text, autobio_prompt_text, chatbot_display_name):
    chatbot_type = display_to_value.get(chatbot_display_name, 'enhanced')
    prompts = {
        'system_prompt': system_prompt_text,
        'conv_instruction_prompt': conv_instruction_prompt_text,
        'therapy_prompt': therapy_prompt_text,
        'autobio_prompt': autobio_prompt_text
    }
    message = asyncio.run(initialization(api_key_input, chapter_name, topic_name, username_input, prompts, chatbot_type))
    print(message)
    return message, api_key_input, chatbot_type

def save_conversation(username, chatbot_type):
    response = asyncio.run(save_conversation_and_memory(username, chatbot_type))
    return response

def start_recording(audio_file):
    if not audio_file:
        return ""
    try:
        transcription = transcribe_audio(audio_file)
        return transcription
    except Exception as e:
        return f"Failed to transcribe: {str(e)}"

def update_methods(chapter):
    return gr.update(choices=interview_protocols[chapter], value=interview_protocols[chapter][0])

def update_memory_graph(memory_data):
    table_data = []
    for node in memory_data:
        table_data.append([
            node.get('date', ''),
            node.get('topic', ''),
            node.get('event_description', ''),
            node.get('people_involved', '')
        ])
    return table_data

def update_prompts(chatbot_display_name):
    chatbot_type = display_to_value.get(chatbot_display_name, 'enhanced')
    prompts = fetch_default_prompts(chatbot_type)
    return (
        gr.update(value=prompts.get('system_prompt', '')),
        gr.update(value=prompts.get('conv_instruction_prompt', '')),
        gr.update(value=prompts.get('therapy_prompt', '')),
        gr.update(value=prompts.get('autobio_generation_prompt', '')),
    )

def update_chatbot_type(chatbot_display_name):
    chatbot_type = display_to_value.get(chatbot_display_name, 'enhanced')
    return chatbot_type

# Function to start the periodic toggle
def start_timer():
    target_timestamp = datetime.now() + timedelta(seconds=8 * 60)
    return True, target_timestamp

def reset_timer():
    is_running = False
    return is_running, "Timer remaining: 8:00"


# Async function to manage periodic updates, running every second
def periodic_call(is_running, target_timestamp):
    if is_running:
        prefix = 'Time remaining:'
        time_difference = target_timestamp - datetime.now()
        second_left = int(round(time_difference.total_seconds()))
        if second_left <= 0:
            second_left = 0
        minutes, seconds = divmod(second_left, 60)
        new_remain_min = f'{minutes:02}'
        new_remain_second = f'{seconds:02}'
        new_info = f'{prefix} {new_remain_min}:{new_remain_second}'
        return new_info
    else:
        return 'Time remaining: 8:00'

# initialize prompts with empty strings
initial_prompts = {'system_prompt': '', 'conv_instruction_prompt': '', 'therapy_prompt': '', 'autobio_generation_prompt': ''}

# CSS to keep the buttons small
css = """
#start_button, #reset_button {
    padding: 4px 10px !important;
    font-size: 12px !important;
    width: auto !important;
}
"""

with gr.Blocks(css=css) as app:
    chatbot_type_state = gr.State('enhanced')
    api_key_state = gr.State()
    prompt_visibility_state = gr.State(False)

    is_running = gr.State()
    target_timestamp = gr.State()

    with gr.Row():
        with gr.Column(scale=1, min_width=250):
            gr.Markdown("## Settings")

            # chatbot Type Selection
            with gr.Box():
                gr.Markdown("### Chatbot Selection")
                chatbot_type_dropdown = gr.Dropdown(
                    label="Select Chatbot Type",
                    choices=['Echo', 'Breeze'],
                    value='Echo',
                )
                chatbot_type_dropdown.change(
                    fn=update_chatbot_type,
                    inputs=[chatbot_type_dropdown],
                    outputs=[chatbot_type_state]
                )

            # fetch initial prompts based on the default chatbot type
            system_prompt_value, conv_instruction_prompt_value, therapy_prompt_value, autobio_prompt_value = update_prompts('Echo')

            # interview protocol selection
            with gr.Box():
                gr.Markdown("### Interview Protocol")
                chapter_dropdown = gr.Dropdown(
                    label="Select Chapter",
                    choices=list(interview_protocols.keys()),
                    value=list(interview_protocols.keys())[1],
                )
                method_dropdown = gr.Dropdown(
                    label="Select Topic",
                    choices=interview_protocols[chapter_dropdown.value],
                    value=interview_protocols[chapter_dropdown.value][3],
                )

                chapter_dropdown.change(
                    fn=update_methods,
                    inputs=[chapter_dropdown],
                    outputs=[method_dropdown]
                )

                # Update states when selections change
                def update_chapter(chapter):
                    return chapter

                def update_method(method):
                    return method

                chapter_state = gr.State()
                method_state = gr.State()

                chapter_dropdown.change(
                    fn=update_chapter,
                    inputs=[chapter_dropdown],
                    outputs=[chapter_state]
                )

                method_dropdown.change(
                    fn=update_method,
                    inputs=[method_dropdown],
                    outputs=[method_state]
                )

            # customize Prompts
            with gr.Box():
                toggle_prompts_button = gr.Button("Show Prompts")

                # wrap the prompts in a component with initial visibility set to False
                with gr.Column(visible=False) as prompt_section:
                    gr.Markdown("### Customize Prompts")
                    system_prompt = gr.Textbox(
                        label="System Prompt",
                        placeholder="Enter the system prompt here.",
                        value=system_prompt_value['value']
                    )
                    conv_instruction_prompt = gr.Textbox(
                        label="Conversation Instruction Prompt",
                        placeholder="Enter the instruction for each conversation here.",
                        value=conv_instruction_prompt_value['value']
                    )
                    therapy_prompt = gr.Textbox(
                        label="Therapy Prompt",
                        placeholder="Enter the instruction for reminiscence therapy.",
                        value=therapy_prompt_value['value']
                    )
                    autobio_prompt = gr.Textbox(
                        label="Autobiography Generation Prompt",
                        placeholder="Enter the instruction for autobiography generation.",
                        value=autobio_prompt_value['value']
                    )

                    # update prompts when chatbot_type changes
                    chatbot_type_dropdown.change(
                        fn=update_prompts,
                        inputs=[chatbot_type_dropdown],
                        outputs=[system_prompt, conv_instruction_prompt, therapy_prompt, autobio_prompt]
                    )

            with gr.Box():
                gr.Markdown("### User Information")
                username_input = gr.Textbox(
                    label="Username", placeholder="Enter your username"
                )
                
                api_key_input = gr.Textbox(
                    label="OpenAI API Key",
                    placeholder="Enter your openai api key",
                    value="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",
                    type="password"
                )

            initialize_button = gr.Button("Initialize", variant="primary", size="large")
            initialization_status = gr.Textbox(
                label="Status", interactive=False, placeholder="Initialization status will appear here."
            )

            initialize_button.click(
                fn=set_initialize_button,
                inputs=[api_key_input, chapter_dropdown, method_dropdown, username_input,
                        system_prompt, conv_instruction_prompt, therapy_prompt, autobio_prompt, chatbot_type_dropdown],
                outputs=[initialization_status, api_key_state, chatbot_type_state],
            )

            # define the function to toggle prompts visibility
            def toggle_prompts(visibility):
                new_visibility = not visibility
                button_text = "Hide Prompts" if new_visibility else "Show Prompts"
                return gr.update(value=button_text), gr.update(visible=new_visibility), new_visibility

            toggle_prompts_button.click(
                fn=toggle_prompts,
                inputs=[prompt_visibility_state],
                outputs=[toggle_prompts_button, prompt_section, prompt_visibility_state]
            )

        with gr.Column(scale=3):
            with gr.Row():
                timer_display = gr.Textbox(value="Time remaining: 08:00", label="")
                start_button = gr.Button("Start Timer", elem_id="start_button")

                start_button.click(start_timer, outputs=[is_running, target_timestamp]).then(
                    periodic_call, inputs=[is_running, target_timestamp], outputs=timer_display, every=1)

            chatbot = gr.Chatbot(label="Chat here for autobiography generation", height=500)

            with gr.Row():
                transcription_box = gr.Textbox(
                    label="Transcription (You can edit this)", lines=3
                )
                audio_input = gr.Audio(
                    source="microphone", type="filepath", label="🎤 Record Audio"
                )

            with gr.Row():
                submit_button = gr.Button("Submit", variant="primary", size="large")
                save_conversation_button = gr.Button("End and Save Conversation", variant="secondary")
                download_button = gr.Button("Download Conversations", variant="secondary")
                download_biography_button = gr.Button("Download Biography", variant="secondary")

            memory_graph_table = gr.Dataframe(
                headers=["Date", "Topic", "Description", "People Involved"],
                datatype=["str", "str", "str", "str"],
                interactive=False,
                label="Memory Events",
                max_rows=5
            )
            
            biography_textbox = gr.Textbox(label="Autobiography", visible=False)

            audio_input.change(
                fn=start_recording,
                inputs=[audio_input],
                outputs=[transcription_box]
            )

            state = gr.State([])

            submit_button.click(
                submit_text_and_respond,
                inputs=[transcription_box, api_key_state, username_input, state, chatbot_type_state],
                outputs=[chatbot, transcription_box, memory_graph_table]
            )

            download_button.click(
                fn=download_conversations,
                inputs=[username_input, chatbot_type_state],
                outputs=gr.Files()
            )

            download_biography_button.click(
                fn=download_biography,
                inputs=[username_input, chatbot_type_state],
                outputs=[gr.File(label="Biography.txt"), biography_textbox]
            )

            save_conversation_button.click(
                fn=save_conversation,
                inputs=[username_input, chatbot_type_state],
                outputs=None
            )


    app.queue()
    app.launch(share=True, max_threads=10)