"""Language Tutor Application

This script provides a Gradio-based web interface for a language tutoring assistant.
It uses OpenAI's GPT-4 model to generate language-specific responses and Speechify's
text-to-speech service to synthesize audio in multiple languages (Portuguese, French, Spanish).
The application supports running both locally and in Hugging Face Spaces environments.
"""

import os
import base64
import uuid
import gradio as gr
from openai import OpenAI
from speechify import Speechify
from dotenv import load_dotenv

# Detect Hugging Face environment
RUNNING_IN_SPACES = os.getenv("SYSTEM") == "spaces"

# Load API keys
# Load environment variables from .env when not running in Spaces
if not RUNNING_IN_SPACES:
    load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
speechify_api_key = os.getenv("SPEECHIFY_API_KEY")

# Sanity check (but don't print full keys)
print(f"✅ OPENAI_API_KEY loaded: {'✅' if openai_api_key else '❌ MISSING'}")
print(f"✅ SPEECHIFY_API_KEY loaded: {'✅' if speechify_api_key else '❌ MISSING'}")

# Initialize clients
openai_client = OpenAI(api_key=openai_api_key)
speechify_client = Speechify(token=speechify_api_key)

# Voice config
language_config = {
    "Portuguese": {
        "voice_id": "agueda",
        "language": "pt-PT",
        "model": "simba-multilingual",
        "audio_format": "mp3"
    },
    "French": {
        "voice_id": "leo",
        "language": "fr-FR",
        "model": "simba-multilingual",
        "audio_format": "mp3"
    },
    "Spanish": {
        "voice_id": "danna-sofia",
        "language": "es-MX",
        "model": "simba-multilingual",
        "audio_format": "mp3"
    },
    "Korean": {
        "voice_id": "yoon-jung",
        "language": "ko-KR",
        "model": "simba-multilingual",
        "audio_format": "mp3"
    },
}

def chat_and_speak(user_input, language_choice, history, show_translation):
    # Step 0: Initialize response variables
    gpt_response = ""
    english_translation = ""
    audio_output_path = None
    try:
        # Step 1: Input validation
        if not user_input or not user_input.strip():
            return None, ("", ""), "Please enter some text to process.", history

        print(f"🧠 User input: {user_input}")
        print(f"🗣️ Language choice: {language_choice}")

        # Build messages with history for GPT interaction
        system_message = f"You are a friendly {language_choice} language tutor. Respond only in {language_choice}."
        messages = [{"role": "system", "content": system_message}]
        if history:
            for user_msg, assistant_msg in history:
                messages.append({"role": "user", "content": user_msg})
                messages.append({"role": "assistant", "content": assistant_msg})
        messages.append({"role": "user", "content": user_input})

        # Step 2: GPT interaction to generate response
        completion = openai_client.chat.completions.create(
            model="gpt-4",
            messages=messages
        )
        gpt_response = completion.choices[0].message.content
        print(f"💬 GPT response: {gpt_response}")

        # Step 2b: Get English translation
        translation_prompt = f"Translate the following text to English:\n\n{gpt_response}"
        translation_completion = openai_client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "system", "content": "You translate text to English."},
                      {"role": "user", "content": translation_prompt}]
        )
        english_translation = translation_completion.choices[0].message.content
        print(f"📝 English translation: {english_translation}")

        # Step 3: Voice synthesis using Speechify
        config = language_config.get(language_choice)
        if not config:
            error_msg = f"⚠️ Language '{language_choice}' not supported."
            print(error_msg)
            return None, (gpt_response, english_translation), f"{gpt_response}\n\n{error_msg}", history

        tts_response = speechify_client.tts.audio.speech(
            input=gpt_response,
            voice_id=config["voice_id"],
            model=config["model"],
            audio_format=config["audio_format"]
        )

        if hasattr(tts_response, "audio_data") and isinstance(tts_response.audio_data, str) and tts_response.audio_data:
            try:
                audio_bytes = base64.b64decode(tts_response.audio_data)
                output_dir = "/tmp" if RUNNING_IN_SPACES else "speech_files"
                os.makedirs(output_dir, exist_ok=True)
                audio_output_path = os.path.join(output_dir, f"speech_{uuid.uuid4().hex}.mp3")
                with open(audio_output_path, "wb") as f:
                    f.write(audio_bytes)
            except Exception as audio_err:
                print(f"🔥 Error processing audio data: {audio_err}")
                return None, (gpt_response, english_translation), f"{gpt_response}\n\n⚠️ Error saving audio: {audio_err}", history
        else:
            print("⚠️ No audio data received from Speechify or audio_data is not a string.")
            return None, (gpt_response, english_translation), f"{gpt_response}\n\n⚠️ No audio data received from Speechify.", history

        # Append new interaction to history
        history = history or []
        history.append((user_input, gpt_response))

        return audio_output_path, (gpt_response, english_translation), history

    except Exception as e:
        # Step 4: Error handling
        print(f"🔥 An unexpected error occurred: {e}")
        error_message = f"⚠️ An unexpected error occurred: {e}"
        if gpt_response:
            return None, (gpt_response, english_translation), f"{gpt_response}\n\n{error_message}", history
        return None, ("", ""), error_message, history

# Load custom CSS for UI styling
with open("custom.css") as f:
    custom_css = f.read()

def update_display_text(chat_output_pair, show_translation):
    original, translated = chat_output_pair or ("", "")
    return translated if show_translation and translated else original

# Toggle translation display helper
def toggle_translation(chat_output_pair, show_translation):
    return update_display_text(chat_output_pair, show_translation)

# Define Gradio UI layout
with gr.Blocks(css=custom_css) as demo:
    gr.HTML(
        '<div class="custom-bar"><span class="custom-bar-title">Language Tutor</span></div>'
    )  
    with gr.Column(elem_classes="main-card"):
        with gr.Row():
            with gr.Column():
                user_input = gr.Textbox(label="Type in whatever language you prefer", placeholder="Type here...", lines=4)
                language_choice = gr.Dropdown(
                    choices=["Portuguese", "French", "Spanish", "Korean"],
                    value="Portuguese",
                    label="Language"
                )
                show_translation = gr.Checkbox(label="Show English Translation", value=False)
                submit_btn = gr.Button("Submit")
                chat_history = gr.State([])
                chat_output_pair = gr.State(("", ""))  # (original, translation)
            with gr.Column():
                audio_output = gr.Audio(label="Audio Playback", type="filepath", autoplay=True)
                gpt_output = gr.Textbox(label="The Response")

        submit_btn.click(
            fn=chat_and_speak,
            inputs=[user_input, language_choice, chat_history, show_translation],
            outputs=[audio_output, chat_output_pair, chat_history]
        ).then(
            fn=update_display_text,
            inputs=[chat_output_pair, show_translation],
            outputs=gpt_output
        )

        show_translation.change(
            fn=toggle_translation,
            inputs=[chat_output_pair, show_translation],
            outputs=gpt_output
        )

# Launch the Gradio app
if __name__ == "__main__":
    demo.launch()