"""
Version 5 — Multimodal AI Assistant (Image Generation)

Extends Version 4 with:
- On-demand image generation: after LLM response, decide if visual illustration helps
- Text-to-image via HF Inference API (FLUX.1-schnell) or DALL-E 3
- General Chat tab (free-form Q&A with image gen)
- Model: llama-3.3-70b-versatile | STT: whisper-large-v3 | TTS: gTTS
"""

# Patch for Hugging Face Spaces: HfFolder removed in huggingface_hub 0.26+
import huggingface_hub
if not hasattr(huggingface_hub, "HfFolder"):
    class _HfFolderStub:
        @staticmethod
        def save_token(token): pass
        @staticmethod
        def get_token(): return None
    huggingface_hub.HfFolder = _HfFolderStub

import json
import os
import re
import tempfile
import urllib3
from urllib.parse import urlparse

import gradio as gr
import requests
from bs4 import BeautifulSoup
from dotenv import load_dotenv
from groq import Groq
from youtube_transcript_api import YouTubeTranscriptApi

from storage import load_storage, save_storage, history_dicts_to_tuples

load_dotenv()

GROQ_API_KEY = os.getenv("GROQ_API_KEY")
BRIGHTDATA_API_KEY = os.getenv("BRIGHTDATA_API_KEY")
BRIGHTDATA_UNLOCKER_ZONE = os.getenv("BRIGHTDATA_UNLOCKER_ZONE", "goodreads_unlocker")
YOUTUBE_UNLOCKER_ZONE = os.getenv("YOUTUBE_UNLOCKER_ZONE", "").strip() or BRIGHTDATA_UNLOCKER_ZONE
HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGING_FACE_HUB_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

if not GROQ_API_KEY:
    raise ValueError("GROQ_API_KEY is not set.")

client = Groq(api_key=GROQ_API_KEY)
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

contexts = {"scraper": "", "youtube": ""}
storage = load_storage()

# Model config
LLM_MODEL = "llama-3.3-70b-versatile"  # fallback to llama-3.1-8b-instant if unavailable
IMAGE_MODELS_HF = ["black-forest-labs/FLUX.1-schnell", "stabilityai/stable-diffusion-xl-base-1.0"]

# Lazy-load Whisper (heavy model)
_transcriber = None

def _get_transcriber():
    global _transcriber
    if _transcriber is None:
        from transformers import pipeline
        _transcriber = pipeline(
            "automatic-speech-recognition",
            model="openai/whisper-large-v3",
            device=-1,
        )
    return _transcriber


# ---------------------------------------------------------------------------
# Image Generation
# ---------------------------------------------------------------------------

_IMAGE_KEYWORDS = (
    "visually", "visual", "show me", "diagram", "image", "picture", "illustrate",
    "illustration", "draw", "sketch", "look like", "looks like", "what does",
    "architecture", "structure", "solar system", "transformer", "neural network",
    "show", "display", "see", "demonstrate",
)

def _should_generate_image(user_query: str, bot_response: str) -> bool:
    """Decide if a visual illustration would help. Uses keyword check + optional LLM fallback."""
    q = (user_query or "").lower()
    for kw in _IMAGE_KEYWORDS:
        if kw in q or re.search(kw.replace(".*", ".*"), q):
            return True
    # Optional: short LLM call for edge cases
    try:
        resp = client.chat.completions.create(
            model="llama-3.1-8b-instant",
            messages=[
                {"role": "system", "content": "Answer YES or NO only. Would a visual illustration help explain the user's question?"},
                {"role": "user", "content": f"User asked: {user_query[:200]}"},
            ],
            max_tokens=10,
            temperature=0,
        )
        ans = (resp.choices[0].message.content or "").strip().upper()
        return "YES" in ans
    except Exception:
        return False


def _generate_image_prompt(user_query: str) -> str:
    """Create a condensed, descriptive prompt for the image model from the user query."""
    try:
        resp = client.chat.completions.create(
            model="llama-3.1-8b-instant",
            messages=[
                {"role": "system", "content": "Generate a short, descriptive image prompt (max 100 chars) for a text-to-image model. Describe the main visual subject. No quotes."},
                {"role": "user", "content": user_query[:300]},
            ],
            max_tokens=80,
            temperature=0.5,
        )
        prompt = (resp.choices[0].message.content or user_query[:100]).strip()
        return prompt[:200] or user_query[:100]
    except Exception:
        return user_query[:150]


def _generate_image(prompt: str) -> str | None:
    """Generate image via HF Inference API (FLUX) or DALL-E 3. Returns path or None."""
    if not prompt:
        return None
    # Try DALL-E 3 first if OpenAI key is set
    if OPENAI_API_KEY:
        try:
            from openai import OpenAI
            oai = OpenAI(api_key=OPENAI_API_KEY)
            resp = oai.images.generate(model="dall-e-3", prompt=prompt, size="1024x1024", n=1)
            url = resp.data[0].url
            r = requests.get(url, timeout=30)
            r.raise_for_status()
            fd, path = tempfile.mkstemp(suffix=".png")
            os.close(fd)
            with open(path, "wb") as f:
                f.write(r.content)
            return path
        except Exception as e:
            import logging
            logging.warning(f"DALL-E 3 image gen failed: {e}")
    # HF Inference Providers (router.huggingface.co - old api-inference is deprecated)
    if HF_TOKEN:
        try:
            from huggingface_hub import InferenceClient
            hf_client = InferenceClient(provider="auto", api_key=HF_TOKEN)
            image = None
            for model_id in IMAGE_MODELS_HF:
                try:
                    image = hf_client.text_to_image(prompt, model=model_id)
                    if image is not None:
                        break
                except Exception:
                    continue
            if image is None:
                raise ValueError("All HF image models failed")
            fd, path = tempfile.mkstemp(suffix=".png")
            os.close(fd)
            if hasattr(image, "save"):
                image.save(path)
            else:
                with open(path, "wb") as f:
                    f.write(image if isinstance(image, bytes) else image)
            return path
        except Exception as e:
            import logging
            logging.warning(f"HF image gen failed: {e}")
    return None


# ---------------------------------------------------------------------------
# Tab 1: Bot-Protected Website Scraper
# ---------------------------------------------------------------------------

def scrape_website(url: str):
    if not url:
        return "Please enter a URL.", ""

    parsed = urlparse(url)
    target_url = url
    if "goodreads.com" in (parsed.netloc or "") and (parsed.path in ("", "/")):
        target_url = "https://www.goodreads.com/list/show/1.Best_Books_Ever"

    api_url = "https://api.brightdata.com/request"
    headers = {
        "Authorization": f"Bearer {BRIGHTDATA_API_KEY}",
        "Content-Type": "application/json",
    }
    payload = {
        "zone": BRIGHTDATA_UNLOCKER_ZONE,
        "url": target_url,
        "format": "raw",
        "method": "GET",
    }

    try:
        resp = requests.post(api_url, json=payload, headers=headers, timeout=120, verify=False)
        if resp.status_code in (400, 401):
            contexts["scraper"] = ""
            try:
                err_body = resp.json()
            except Exception:
                err_body = resp.text[:500] if resp.text else ""
            return (
                f"Bright Data error ({resp.status_code}): {resp.reason}. Details: {err_body}. "
                "Check BRIGHTDATA_API_KEY and BRIGHTDATA_UNLOCKER_ZONE.",
                "",
            )
        resp.raise_for_status()
        soup = BeautifulSoup(resp.text, "html.parser")

        if "goodreads.com/list/show/1.Best_Books_Ever" in target_url:
            books_data = []
            book_rows = soup.find_all("tr", itemtype="http://schema.org/Book")
            for idx, row in enumerate(book_rows):
                title_elem = row.find("a", class_="bookTitle")
                author_elem = row.find("a", class_="authorName")
                rating_elem = row.find("span", class_="minirating")
                title = title_elem.text.strip() if title_elem else "Unknown Title"
                author = author_elem.text.strip() if author_elem else "Unknown Author"
                rating = rating_elem.text.strip() if rating_elem else "Unknown Rating"
                books_data.append({"Rank": idx + 1, "Title": title, "Author": author, "Rating": rating})
            if books_data:
                lines = ["Here is the scraped data from Goodreads Best Books Ever list:\n"]
                for b in books_data:
                    lines.append(f"{b['Rank']}. {b['Title']} by {b['Author']} - {b['Rating']}")
                text_content = "\n".join(lines)
            else:
                text_content = soup.get_text(separator=" ", strip=True)
        else:
            text_content = soup.get_text(separator=" ", strip=True)

        contexts["scraper"] = text_content[:15000]
        preview = text_content[:500] + "..." if len(text_content) > 500 else text_content
        return "Website scraped successfully. You can now chat about it.", preview
    except Exception as e:
        contexts["scraper"] = ""
        return f"Error scraping website: {e}", ""


# ---------------------------------------------------------------------------
# Tab 2: YouTube Transcript Q&A
# ---------------------------------------------------------------------------

_YOUTUBE_ID_REGEX = re.compile(
    r"(https?://)?(www\.)?"
    r"(youtube|youtu|youtube-nocookie)\.(com|be)/"
    r"(watch\?v=|embed/|v/|.+\?v=)?([^&=%\?]{11})"
)

def _extract_video_id(url_or_id: str) -> str:
    match = _YOUTUBE_ID_REGEX.search(url_or_id)
    return match.group(6) if match else url_or_id.strip()


def _fetch_transcript_via_brightdata(video_id: str) -> str:
    if not BRIGHTDATA_API_KEY:
        raise ValueError("BRIGHTDATA_API_KEY is required for YouTube transcript on this environment.")
    api_url = "https://api.brightdata.com/request"
    headers = {
        "Authorization": f"Bearer {BRIGHTDATA_API_KEY}",
        "Content-Type": "application/json",
    }
    zone = YOUTUBE_UNLOCKER_ZONE
    watch_url = f"https://www.youtube.com/watch?v={video_id}"
    payload = {"zone": zone, "url": watch_url, "format": "raw", "method": "GET"}
    resp = requests.post(api_url, json=payload, headers=headers, timeout=120, verify=False)
    if resp.status_code == 400:
        raise ValueError(f"Bright Data zone '{zone}' rejected the YouTube URL.")
    resp.raise_for_status()
    html = resp.text
    match = re.search(r"ytInitialPlayerResponse\s*=\s*(\{)", html)
    if not match:
        raise ValueError("Could not find caption data on the video page.")
    start = match.end(1) - 1
    depth, i = 0, start
    while i < len(html):
        if html[i] == "{":
            depth += 1
        elif html[i] == "}":
            depth -= 1
            if depth == 0:
                player = json.loads(html[start : i + 1])
                break
        i += 1
    else:
        raise ValueError("Could not parse caption data from the video page.")
    captions = player.get("captions", {}) or {}
    renderer = captions.get("playerCaptionsTracklistRenderer", {})
    tracks = renderer.get("captionTracks", [])
    if not tracks:
        raise ValueError("No transcript available for this video.")
    base_url = tracks[0].get("baseUrl", "")
    if not base_url:
        raise ValueError("No caption track URL found.")
    caption_url = base_url + ("&" if "?" in base_url else "?") + "fmt=json3"
    payload2 = {"zone": zone, "url": caption_url, "format": "raw", "method": "GET"}
    resp2 = requests.post(api_url, json=payload2, headers=headers, timeout=60, verify=False)
    resp2.raise_for_status()
    caption_data = resp2.json()
    pieces = []
    for event in caption_data.get("events", []):
        for seg in event.get("segs", []):
            text = seg.get("utf8", "").strip()
            if text and text != "\n":
                pieces.append(text)
    return " ".join(pieces)


def fetch_youtube_transcript(video_input: str):
    if not video_input:
        return "Please enter a YouTube Video URL or ID.", ""

    video_id = _extract_video_id(video_input)
    try:
        api = YouTubeTranscriptApi()
        transcript_list = api.list(video_id)
        transcript = None
        try:
            transcript = transcript_list.find_transcript(["en", "ur"])
        except Exception:
            try:
                transcript = transcript_list.find_generated_transcript(["en", "ur"])
            except Exception:
                for t in transcript_list:
                    transcript = t
                    break
        if transcript is None:
            raise Exception("No transcript available for this video.")
        transcript_data = transcript.fetch()
        pieces = []
        for t in transcript_data:
            if isinstance(t, dict):
                pieces.append(t.get("text", ""))
            else:
                pieces.append(getattr(t, "text", ""))
        transcript_text = " ".join(pieces)
        contexts["youtube"] = transcript_text[:15000]
        preview = transcript_text[:500] + "..." if len(transcript_text) > 500 else transcript_text
        return "Transcript fetched successfully. You can now chat about the video.", preview
    except Exception as e:
        err_str = str(e).lower()
        is_network_error = "resolve" in err_str or "hostname" in err_str or "no address" in err_str or "max retries" in err_str
        if is_network_error and BRIGHTDATA_API_KEY:
            try:
                transcript_text = _fetch_transcript_via_brightdata(video_id)
                contexts["youtube"] = transcript_text[:15000]
                preview = transcript_text[:500] + "..." if len(transcript_text) > 500 else transcript_text
                return "Transcript fetched via Bright Data. You can now chat about the video.", preview
            except Exception as fallback_err:
                contexts["youtube"] = ""
                return f"Direct fetch failed. Bright Data fallback failed: {fallback_err}", ""
        contexts["youtube"] = ""
        if is_network_error:
            msg = "YouTube transcript fetching failed (network restricted). Add BRIGHTDATA_API_KEY and YOUTUBE_UNLOCKER_ZONE."
        else:
            msg = f"Error: No transcript for video ID ({video_id}). Details: {e}"
        return msg, ""


# ---------------------------------------------------------------------------
# Multi-turn chat with image generation
# ---------------------------------------------------------------------------

def _build_system_prompt(mode: str) -> str:
    context = contexts.get(mode, "")
    prefs = storage.get("user_preferences", "").strip()
    ctx_placeholder = "(None — the user has NOT scraped or fetched transcript yet. You must refuse to answer and tell them to scrape/fetch first.)"
    base = (
        "You are a helpful assistant. You must use ONLY the provided context to answer. "
        "NEVER use external knowledge. If the context says 'None' or the user has not scraped yet, refuse to answer and tell them to scrape or fetch transcript first. "
        "If the answer is not in the context, say so. You have conversation history for follow-up questions.\n\n"
        f"Context:\n{context.strip() if context else ctx_placeholder}"
    )
    if prefs:
        base += f"\n\nUser preferences (follow these):\n{prefs}"
    return base


def _chat_with_image(mode: str, message: str, history_key: str, system_prompt_fn):
    """Shared chat logic with optional image generation. Returns (clear_msg, history_tuples, image_path)."""
    if not message or not message.strip():
        return "", history_dicts_to_tuples(storage.get(history_key, [])), None

    context = contexts.get(mode, "") or "" if mode != "general" else "general"
    if mode != "general" and not context.strip():
        history_dicts = list(storage.get(history_key, []))
        history_dicts.append({"role": "user", "content": message.strip()})
        history_dicts.append({"role": "assistant", "content": "Please scrape a website or fetch a transcript first, then ask questions."})
        storage[history_key] = history_dicts
        save_storage(storage)
        return "", history_dicts_to_tuples(history_dicts), None

    history_dicts = list(storage.get(history_key, []))
    history_dicts.append({"role": "user", "content": message.strip()})
    system_prompt = system_prompt_fn()
    messages = [{"role": "system", "content": system_prompt}]
    for m in history_dicts:
        if m.get("role") in ("user", "assistant"):
            messages.append({"role": m["role"], "content": m.get("content", "")})

    try:
        resp = client.chat.completions.create(
            model=LLM_MODEL,
            messages=messages,
            max_tokens=1024,
            temperature=0.3,
        )
        reply = resp.choices[0].message.content
    except Exception as e:
        try:
            resp = client.chat.completions.create(
                model="llama-3.1-8b-instant",
                messages=messages,
                max_tokens=1024,
                temperature=0.3,
            )
            reply = resp.choices[0].message.content
        except Exception:
            reply = f"Error communicating with Groq: {e}"

    image_path = None
    if _should_generate_image(message.strip(), reply):
        img_prompt = _generate_image_prompt(message.strip())
        image_path = _generate_image(img_prompt)
        if image_path:
            reply += "\n\n*Generated illustration below.*"
        elif HF_TOKEN:
            reply += "\n\n*(Image generation failed — check HF Inference Providers at hf.co/settings/inference-providers)*"

    history_dicts.append({"role": "assistant", "content": reply})
    storage[history_key] = history_dicts
    save_storage(storage)
    return "", history_dicts_to_tuples(history_dicts), image_path


def chat_turn_scraper(message: str, _history_ignored):
    out = _chat_with_image("scraper", message, "scraper_history", lambda: _build_system_prompt("scraper"))
    return out[0], out[1], out[2]


def chat_turn_youtube(message: str, _history_ignored):
    out = _chat_with_image("youtube", message, "youtube_history", lambda: _build_system_prompt("youtube"))
    return out[0], out[1], out[2]


def chat_turn_general(message: str, _history_ignored):
    """General chat: free-form Q&A with image generation. No context constraint."""
    if not message or not message.strip():
        return "", history_dicts_to_tuples(storage.get("general_history", [])), None

    prefs = storage.get("user_preferences", "").strip()
    system_prompt = "You are a helpful assistant. Answer concisely. You may use general knowledge."
    if prefs:
        system_prompt += f"\n\nUser preferences (follow these):\n{prefs}"

    out = _chat_with_image("general", message, "general_history", lambda: system_prompt)
    return out[0], out[1], out[2]


def save_preferences(prefs: str):
    global storage
    storage["user_preferences"] = prefs or ""
    save_storage(storage)
    return "Preferences saved."


# ---------------------------------------------------------------------------
# Tab: Voice Assistant (Speech-to-Text → LLM → Text-to-Speech + optional Image)
# ---------------------------------------------------------------------------

def voice_chatbot(audio_input):
    if audio_input is None:
        return "No audio received.", None, "", None
    audio_path = audio_input[0] if isinstance(audio_input, tuple) else (audio_input.get("name") or audio_input.get("path", "") if isinstance(audio_input, dict) else audio_input)
    if not audio_path or not os.path.isfile(audio_path):
        return "Invalid audio file.", None, "", None

    try:
        transcriber = _get_transcriber()
        transcription = transcriber(audio_path)
        user_text = transcription.get("text", "").strip() or "(no speech detected)"
    except Exception as e:
        return f"Transcription error: {e}", None, "", None

    prefs = storage.get("user_preferences", "").strip()
    system_prompt = "You are a helpful assistant. Keep answers concise."
    if prefs:
        system_prompt += f"\n\nUser preferences (follow these):\n{prefs}"

    try:
        resp = client.chat.completions.create(
            model=LLM_MODEL,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_text},
            ],
            max_tokens=512,
            temperature=0.7,
        )
        bot_text = resp.choices[0].message.content
    except Exception:
        try:
            resp = client.chat.completions.create(
                model="llama-3.1-8b-instant",
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": user_text},
            ],
                max_tokens=512,
                temperature=0.7,
            )
            bot_text = resp.choices[0].message.content
        except Exception as e:
            bot_text = f"Error communicating with Groq: {e}"

    image_path = None
    if _should_generate_image(user_text, bot_text):
        img_prompt = _generate_image_prompt(user_text)
        image_path = _generate_image(img_prompt)
        if image_path:
            bot_text += "\n\n*Generated illustration below.*"
        elif HF_TOKEN:
            bot_text += "\n\n*(Image generation failed — check HF Inference Providers)*"

    try:
        from gtts import gTTS
        fd, output_path = tempfile.mkstemp(suffix=".mp3")
        os.close(fd)
        tts = gTTS(text=bot_text, lang="en")
        tts.save(output_path)
    except Exception as e:
        output_path = None
        bot_text += f"\n\n(TTS error: {e})"

    return bot_text, output_path, user_text, image_path


# ---------------------------------------------------------------------------
# Gradio UI
# ---------------------------------------------------------------------------

with gr.Blocks(title="Scraper Bot v5 — Multimodal AI (Image Gen)") as demo:
    gr.Markdown("# Version 5 — Multimodal AI Assistant (Image Generation)")
    gr.Markdown(
        "**Model:** llama-3.3-70b-versatile | **STT:** whisper-large-v3 | **TTS:** gTTS (🔓 Open-source)  \n"
        "Extends v4 with **on-demand image generation**: ask for visual explanations and get illustrations via FLUX.1-schnell (HF) or DALL-E 3."
    )

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### User preferences")
            prefs_input = gr.Textbox(
                label="Preferences",
                value=storage.get("user_preferences", ""),
                placeholder="e.g., Always respond formally.",
                lines=3,
            )
            save_prefs_btn = gr.Button("Save preferences")
            prefs_status = gr.Textbox(label="Status", interactive=False)
            save_prefs_btn.click(save_preferences, inputs=[prefs_input], outputs=[prefs_status])

        with gr.Column(scale=2):
            with gr.Tabs():
                with gr.TabItem("General Chat (Image Gen)"):
                    gr.Markdown("Free-form Q&A with optional image generation. Try: *Explain the solar system visually* or *Show me what a transformer architecture looks like*.")
                    gen_chatbot = gr.Chatbot(
                        value=history_dicts_to_tuples(storage.get("general_history", [])),
                        height=300,
                        label="Chat",
                    )
                    gen_img_out = gr.Image(label="Generated illustration", type="filepath")
                    gen_msg = gr.Textbox(label="Message", placeholder="Ask anything. Say 'visually' or 'show me' for images.")
                    gen_msg.submit(
                        lambda m, h: chat_turn_general(m, h),
                        inputs=[gen_msg, gen_chatbot],
                        outputs=[gen_msg, gen_chatbot, gen_img_out],
                    )

                with gr.TabItem("Bot-Protected Website Scraper"):
                    gr.Markdown("Scrape a URL, then chat. Image gen when you ask for visual explanations.")
                    with gr.Row():
                        url_input = gr.Textbox(label="URL", placeholder="https://www.goodreads.com/", scale=3)
                        scrape_btn = gr.Button("Scrape URL", scale=1)
                    scrape_status = gr.Textbox(label="Status", interactive=False)
                    scrape_preview = gr.Textbox(label="Content preview", interactive=False, lines=4)
                    scraper_chatbot = gr.Chatbot(
                        value=history_dicts_to_tuples(storage.get("scraper_history", [])),
                        height=280,
                        label="Chat",
                    )
                    scraper_img_out = gr.Image(label="Generated illustration", type="filepath")
                    scraper_msg = gr.Textbox(label="Message", placeholder="e.g., What are the top 5 books?")
                    scrape_btn.click(scrape_website, inputs=[url_input], outputs=[scrape_status, scrape_preview])
                    scraper_msg.submit(
                        lambda m, h: chat_turn_scraper(m, h),
                        inputs=[scraper_msg, scraper_chatbot],
                        outputs=[scraper_msg, scraper_chatbot, scraper_img_out],
                    )

                with gr.TabItem("YouTube Transcript Q&A"):
                    gr.Markdown("Fetch transcript, then chat. Image gen when you ask for visual explanations.")
                    with gr.Row():
                        yt_input = gr.Textbox(label="YouTube URL or ID", placeholder="dQw4w9WgXcQ", scale=3)
                        yt_btn = gr.Button("Get Transcript", scale=1)
                    yt_status = gr.Textbox(label="Status", interactive=False)
                    yt_preview = gr.Textbox(label="Transcript preview", interactive=False, lines=4)
                    yt_chatbot = gr.Chatbot(
                        value=history_dicts_to_tuples(storage.get("youtube_history", [])),
                        height=280,
                        label="Chat",
                    )
                    yt_img_out = gr.Image(label="Generated illustration", type="filepath")
                    yt_msg = gr.Textbox(label="Message", placeholder="e.g., Summarize the video.")
                    yt_btn.click(fetch_youtube_transcript, inputs=[yt_input], outputs=[yt_status, yt_preview])
                    yt_msg.submit(
                        lambda m, h: chat_turn_youtube(m, h),
                        inputs=[yt_msg, yt_chatbot],
                        outputs=[yt_msg, yt_chatbot, yt_img_out],
                    )

                with gr.TabItem("Voice Assistant"):
                    gr.Markdown("Speak your query. Get text + audio + optional image.")
                    mic_input = gr.Audio(
                        label="Speak",
                        sources=["microphone"],
                        type="filepath",
                    )
                    user_text_box = gr.Textbox(label="Transcribed text", interactive=False)
                    bot_text_output = gr.Textbox(label="Bot text response", interactive=False)
                    bot_audio_output = gr.Audio(label="Bot audio response", type="filepath", interactive=False)
                    voice_img_out = gr.Image(label="Generated illustration", type="filepath")

                    mic_input.change(
                        fn=voice_chatbot,
                        inputs=mic_input,
                        outputs=[bot_text_output, bot_audio_output, user_text_box, voice_img_out],
                    )

if __name__ == "__main__":
    if os.environ.get("SPACE_ID"):
        demo.launch()
    else:
        demo.launch(server_name="127.0.0.1", server_port=7866)