import gradio as gr
import os
from openai import OpenAI
from datetime import datetime

# Get dateTime strubg to build a filename reflecting the UserID + Timestamp
dt = datetime.now()
dt_string = str(dt)

# Initialize OpenAI API client with API key
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# Initialize empty history list to maintain the conversation context
history_openai_format = [
    {"role": "system", "content": os.getenv("PROMPT")}
]

# Define the function that takes a name and text input to generate the speech
def generate_speech(name, input_text):
    global history_openai_format  # Use the global history variable to maintain state

    # Get dateTime string to build a filename reflecting the UserID + Timestamp
    dt = datetime.now()
    dt_string = str(dt)

    # Define the user ID and construct the filename for the user's history file
    user_id = name if name else "unidentified"  # User identification, typically an email.
    user_id = user_id.lower()
    user_id = user_id.replace(" ", "")
    user_hist_file = "jcTSS-" + user_id + ".txt"  # Filename where the user history will be stored.

    # Check if the user's history file exists
    if os.path.exists(user_hist_file):
        # If it exists, open and read its contents, then print it
        with open(user_hist_file, "r", encoding="UTF-8") as file:
            user_hist = file.read().strip()  # Remove leading/trailing whitespace
    else:
        # If it does not exist, create the file and initialize it with the user ID
        with open(user_hist_file, "w", encoding="UTF-8") as file:
            file.write("User ID: " + user_id)
    
    # Append user message to history with the name included
    input_text1 = f"I'm {name}. " + input_text
    history_openai_format.append({"role": "user", "content": input_text1})

    # Build completion with OpenAI using the accumulated history
    completion = client.chat.completions.create(
        model="gpt-3.5-turbo-1106",
        messages=history_openai_format
    )

    # Extract generated text (response by the assistant) from completion
    message_content = completion.choices[0].message.content.strip()

    # Remove "Johnny" from the beginning of the assistant's message if present
    if message_content.lower().startswith("johnny"):
        # Strip the leading "Johnny" from the message content
        message_content = message_content[6:].strip()

    # Append assistant's message to history
    history_openai_format.append({"role": "assistant", "content": message_content})

    # Use OpenAI's text-to-speech API to convert the text response to audio
    response = client.audio.speech.create(
        model="tts-1",
        voice="onyx",
        input=str(message_content)
    )

    # Prepare the transcript for the Textbox output
    # Exclude the system message from the transcript and show the user's actual input
    # Add line spaces between user and assistant output, and between user/assistant exchanges
    transcript = ""
    transcript += "Date/Time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n\n"
    for i, msg in enumerate(history_openai_format):
        if msg['role'] != 'system':
            if msg['role'] == 'user':
                # Extract the actual user input from the message content instead of using the latest input_text
                # Assuming input_text1 = f"I'm {name}. " + input_text, you must remove the "I'm {name}. " part.
                user_input = msg['content'].split(". ", 1)[1] if ". " in msg['content'] else msg['content']
                # Use the user_input for the Guest's content
                transcript += f"GUEST: {user_input}\n"
            else:
                transcript += f"JOHNNY: {msg['content']}\n\n"

    # Write the user and assistant messages to the history file after the exchange
    with open(user_hist_file, "a+", encoding="UTF-8") as file:
        file.write("\n\nDate/Time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
        for message in history_openai_format[-2:]:  # Last 2 messages include the user and assistant responses
            file.write(f"\n{message['role'].title()}: {message['content']}")

    # Return the binary audio data and the transcript
    return response.content, transcript

# Define the Gradio interface with inputs for name and user text
iface = gr.Interface(
    fn=generate_speech,
    inputs=[
        gr.Textbox(label="Your Name (REQUIRED):", placeholder="Enter your FIRST NAME"),
        gr.Textbox(label="Your question or comment for Johnny:")
    ],
    outputs=[gr.Audio(autoplay=True, label="Johnny's response:"), gr.Textbox(label="Transcript", max_lines=12, autoscroll="True", show_copy_button="True")],
    live=False
)

# Launch the interface
iface.launch(show_api=False)