Update app.py

app.py

@@ -1,660 +1,660 @@
-import os
-import sys
-import torch
-import pickle
-import logging
-import tempfile
-import requests
-import re
-import asyncio
-import aiohttp
-from urllib.parse import quote_plus
-from pytube import Search
-from PIL import Image
-from torchvision import transforms
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline, AutoModelForCausalLM
-import gradio as gr
-import pandas as pd
-import plotly.express as px
-from reportlab.lib.pagesizes import letter
-from reportlab.lib import colors
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
-from reportlab.lib.styles import getSampleStyleSheet
-from io import BytesIO
-from langchain_huggingface import HuggingFacePipeline
-from langchain_core.runnables.history import RunnableWithMessageHistory
-from langchain_core.chat_history import InMemoryChatMessageHistory
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from pydub import AudioSegment
-from pydub.utils import which
-
-# Local imports (assumed to be available)
-from args import get_parser
-from model import get_model
-from output_utils import prepare_output
-
-# ============== DEVICE CONFIG ==============
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-map_loc = None if torch.cuda.is_available() else "cpu"
-logging.getLogger("pytube").setLevel(logging.ERROR)
-
-# ============== LOAD TRANSLATION MODELS ==============
-model_envit5_name = "VietAI/envit5-translation"
-try:
-    tokenizer_envit5 = AutoTokenizer.from_pretrained(model_envit5_name)
-    model_envit5 = AutoModelForSeq2SeqLM.from_pretrained(
-        model_envit5_name,
-        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
-    ).to(device)
-    pipe_envit5 = pipeline(
-        "text2text-generation",
-        model=model_envit5,
-        tokenizer=tokenizer_envit5,
-        device=0 if torch.cuda.is_available() else -1,
-        max_new_tokens=512,
-        do_sample=False
-    )
-except Exception as e:
-    print(f"Error loading Vietnamese model: {e}")
-    pipe_envit5 = None
-
-models = {
-    "Japanese": {"model_name": "Helsinki-NLP/opus-mt-en-jap"},
-    "Chinese": {"model_name": "Helsinki-NLP/opus-mt-en-zh"}
-}
-
-for lang in models:
-    try:
-        tokenizer = AutoTokenizer.from_pretrained(models[lang]["model_name"])
-        model = AutoModelForSeq2SeqLM.from_pretrained(
-            models[lang]["model_name"],
-            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
-        ).to(device)
-        models[lang]["pipe"] = pipeline(
-            "translation",
-            model=model,
-            tokenizer=tokenizer,
-            device=0 if torch.cuda.is_available() else -1,
-            max_length=512,
-            batch_size=4 if torch.cuda.is_available() else 1,
-            truncation=True
-        )
-    except Exception as e:
-        print(f"Error loading {lang} model: {e}")
-        models[lang]["pipe"] = None
-
-# ============== LOAD CHATBOT MODEL ==============
-chatbot_tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
-chatbot_model = AutoModelForCausalLM.from_pretrained(
-    "bigscience/bloomz-560m",
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
-).to(device)
-
-chatbot_pipeline = pipeline(
-    "text-generation",
-    model=chatbot_model,
-    tokenizer=chatbot_tokenizer,
-    device=0 if torch.cuda.is_available() else -1,
-    max_new_tokens=100,
-    do_sample=True,
-    temperature=0.6,
-    top_p=0.9,
-    pad_token_id=chatbot_tokenizer.eos_token_id,
-    batch_size=1
-)
-llm = HuggingFacePipeline(pipeline=chatbot_pipeline)
-
-# LangChain Chatbot Setup
-prompt = ChatPromptTemplate.from_template("""
-You are a professional culinary assistant. You will answer the user's question directly based on the provided recipe. 
-Do not repeat the recipe or question in your answer. Be concise.
-
-Dish: {title}
-Ingredients: {ingredients}
-Instructions: {instructions}
-
-User Question: {question}
-Answer:
-""")
-
-
-chain = prompt | llm
-chat_histories = {}
-
-def get_session_history(session_id):
-    if session_id not in chat_histories:
-        chat_histories[session_id] = InMemoryChatMessageHistory()
-    return chat_histories[session_id]
-
-chatbot_chain = RunnableWithMessageHistory(
-    chain,
-    get_session_history,
-    input_messages_key="question",
-    history_messages_key="history"
-)
-
-# ============== GLOBAL STATE ==============
-current_recipe_context = {"context": "", "title": "", "ingredients": [], "instructions": [], "image": None}
-
-# ============== RECIPE FORMAT & TRANSLATE ==============
-def format_recipe(title, ingredients, instructions, lang):
-    emoji = {"title": "🍽️", "ingredients": "🧂", "instructions": "📖"}
-    titles = {
-        "en": {"ingredients": "Ingredients", "instructions": "Instructions"},
-        "ja": {"ingredients": "Ingredients (材料)", "instructions": "Instructions (作り方)"},
-        "zh": {"ingredients": "Ingredients (食材)", "instructions": "Instructions (步骤)"},
-        "vi": {"ingredients": "Ingredients (Nguyên liệu)", "instructions": "Instructions (Cách làm)"},
-    }
-
-    code_mapping = {
-        "English (original)": "en",
-        "Japanese": "ja",
-        "Chinese": "zh",
-        "Vietnamese": "vi",
-    }
-    code = code_mapping.get(lang, "en")
-
-    result = [f"### {emoji['title']} {title}", f"**{emoji['ingredients']} {titles[code]['ingredients']}:**"]
-    result.extend([f"- {i}" for i in ingredients])
-    result.append(f"\n**{emoji['instructions']} {titles[code]['instructions']}:**")
-    result.extend([f"{i+1}. {step}" for i, step in enumerate(instructions)])
-    return "\n".join(result)
-
-def translate_section(text, lang):
-    if lang == "English (original)":
-        return text
-
-    if lang == "Vietnamese":
-        if pipe_envit5 is None:
-            return f"❗ Vietnamese translation model not available"
-        try:
-            max_chunk_length = 400
-            if len(text) > max_chunk_length:
-                sentences = text.split('. ')
-                chunks = []
-                current_chunk = ""
-                for sentence in sentences:
-                    if len(current_chunk) + len(sentence) < max_chunk_length:
-                        current_chunk += sentence + ". "
-                    else:
-                        chunks.append(current_chunk)
-                        current_chunk = sentence + ". "
-                if current_chunk:
-                    chunks.append(current_chunk)
-            else:
-                chunks = [text]
-
-            translated_chunks = []
-            for chunk in chunks:
-                chunk = f"en-vi: {chunk}"
-                translated = pipe_envit5(chunk, max_new_tokens=512)[0]["generated_text"]
-                translated = translated.replace("vi: vi: ", "").replace("vi: Vi: ", "").replace("vi: ", "").strip()
-                translated_chunks.append(translated)
-
-            return " ".join(translated_chunks)
-        except Exception as e:
-            print(f"Vietnamese translation error: {e}")
-            return text
-
-    if models.get(lang, {}).get("pipe") is None:
-        return f"❗ Translation model for {lang} not available"
-
-    try:
-        max_chunk_length = 400
-        if len(text) > max_chunk_length:
-            sentences = text.split('. ')
-            chunks = []
-            current_chunk = ""
-            for sentence in sentences:
-                if len(current_chunk) + len(sentence) < max_chunk_length:
-                    current_chunk += sentence + ". "
-                else:
-                    chunks.append(current_chunk)
-                    current_chunk = sentence + ". "
-            if current_chunk:
-                chunks.append(current_chunk)
-        else:
-            chunks = [text]
-
-        translated_chunks = []
-        for chunk in chunks:
-            translated = models[lang]["pipe"](chunk, max_length=512)[0]["translation_text"]
-            translated_chunks.append(translated)
-
-        return " ".join(translated_chunks)
-    except Exception as e:
-        print(f"Translation error ({lang}): {e}")
-        return text
-
-def translate_recipe(lang):
-    if not current_recipe_context["title"]:
-        return "❗ Please generate a recipe from an image first."
-    title = translate_section(current_recipe_context["title"], lang)
-    ingrs = [translate_section(i, lang) for i in current_recipe_context["ingredients"]]
-    instrs = [translate_section(s, lang) for s in current_recipe_context["instructions"]]
-    return format_recipe(title, ingrs, instrs, lang)
-
-# ============== NUTRITION ANALYSIS ==============
-def nutrition_analysis(ingredient_input):
-    ingredients = " ".join(ingredient_input.strip().split())
-    api_url = f'https://api.api-ninjas.com/v1/nutrition?query={ingredients}'
-    headers = {'X-Api-Key': 'AHVy+tpkUoueBNdaFs9nCg==sFZTMRn8ikZVzx6E'}  
-    response = requests.get(api_url, headers=headers)
-    if response.status_code != 200:
-        return "❌ API error or quota exceeded.", None, None, None
-    data = response.json()
-    df = pd.DataFrame(data)
-    numeric_cols = []
-    for col in df.columns:
-        if col == "name":
-            continue
-        df[col] = pd.to_numeric(df[col], errors="coerce")
-        if df[col].notna().sum() > 0:
-            numeric_cols.append(col)
-    if df.empty or len(numeric_cols) < 3:
-        return "⚠️ Insufficient numerical data for charts (need at least 3 metrics).", None, None, None
-    draw_cols = numeric_cols[:3]
-    fig_bar = px.bar(df, x="name", y=draw_cols[0], title=f"Bar Chart: {draw_cols[0]}", text_auto=True)
-    pie_data = df[[draw_cols[1], "name"]].dropna()
-    if pie_data[draw_cols[1]].sum() > 0:
-        fig_pie = px.pie(pie_data, names="name", values=draw_cols[1], title=f"Pie Chart: {draw_cols[1]}")
-    else:
-        fig_pie = px.bar(title="⚠️ Insufficient data for pie chart")
-    fig_line = px.line(df, x="name", y=draw_cols[2], markers=True, title=f"Line Chart: {draw_cols[2]}")
-    return "✅ Analysis successful!", fig_bar, fig_pie, fig_line
-
-def load_recipe_ingredients():
-    if not current_recipe_context["ingredients"]:
-        return "⚠️ No ingredients available. Generate a recipe first."
-    return "\n".join(current_recipe_context["ingredients"])
-
-# ============== CHATBOT ==============
-def clean_response(response):
-    # Remove everything before "Answer:" if present
-    if "Answer:" in response:
-        response = response.split("Answer:")[-1]
-
-    # Remove potential repetitions of Dish, Ingredients, Instructions
-    response = re.sub(r"Dish:.*?(Ingredients:|Instructions:).*?", "", response, flags=re.DOTALL)
-    response = re.sub(r"Ingredients:.*?(Instructions:).*?", "", response, flags=re.DOTALL)
-    response = re.sub(r"Instructions:.*", "", response, flags=re.DOTALL)
-
-    # Remove redundant system info
-    response = re.sub(r"You are a professional culinary assistant.*?Answer:", "", response, flags=re.DOTALL)
-    
-    # Remove duplicate user question inside response (very common in these LLM outputs)
-    response = re.sub(r"User Question:.*", "", response, flags=re.DOTALL)
-    
-    # Final strip + cleanup
-    return response.strip()
-
-
-def validate_cooking_time(question, instructions):
-    # Extract cooking times from instructions
-    time_pattern = r"(\d+)\s*(minutes|minute)"
-    total_time = 0
-    for instr in instructions:
-        matches = re.findall(time_pattern, instr)
-        for match in matches:
-            total_time += int(match[0])
-    
-    # Check if user question contains a time
-    user_time = re.search(time_pattern, question)
-    if user_time:
-        user_minutes = int(user_time.group(1))
-        if user_minutes != total_time:
-            return f"The recipe takes about {total_time} minutes to cook, not {user_minutes} minutes."
-    return None
-
-def generate_chat_response(message, session_id="default"):
-    if not current_recipe_context["title"]:
-        return "Please generate a recipe from an image before asking about the dish."
-    
-    # Validate cooking time if relevant
-    correction = validate_cooking_time(message, current_recipe_context["instructions"])
-    
-    response = chatbot_chain.invoke(
-        {
-            "title": current_recipe_context["title"],
-            "ingredients": ", ".join(current_recipe_context["ingredients"]),
-            "instructions": " ".join(current_recipe_context["instructions"]),
-            "question": message
-        },
-        config={"configurable": {"session_id": session_id}}
-    )
-    
-    response = clean_response(response)
-    if correction:
-        response = f"{correction} {response}"
-    
-    return response.strip()
-
-
-def chat_with_bot(message, chat_history, session_id="default"):
-    if not message.strip():
-        return "", chat_history
-    response = generate_chat_response(message, session_id)
-    chat_history.append({"role": "user", "content": message})
-    chat_history.append({"role": "assistant", "content": response})
-    return "", chat_history
-
-# ============== IMAGE TO RECIPE ==============
-with open("ingr_vocab.pkl", 'rb') as f:
-    ingrs_vocab = pickle.load(f)
-with open("instr_vocab.pkl", 'rb') as f:
-    vocab = pickle.load(f)
-
-args = get_parser()
-args.maxseqlen = 15
-args.ingrs_only = False
-model_ic = get_model(args, len(ingrs_vocab), len(vocab))
-model_ic.load_state_dict(torch.load("modelbest.ckpt", map_location=map_loc, weights_only=True))
-model_ic.to(device).eval()
-
-transform = transforms.Compose([
-    transforms.Resize(256),
-    transforms.CenterCrop(224),
-    transforms.ToTensor(),
-    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
-])
-
-def generate_recipe(image):
-    if image is None:
-        return "❗ Please upload an image."
-    current_recipe_context["image"] = image
-    image = transform(image.convert("RGB")).unsqueeze(0).to(device)
-    with torch.no_grad():
-        outputs = model_ic.sample(image, greedy=True, temperature=1.0, beam=-1, true_ingrs=None)
-    ids = (outputs['ingr_ids'].cpu().numpy(), outputs['recipe_ids'].cpu().numpy())
-    outs, valid = prepare_output(ids[1][0], ids[0][0], ingrs_vocab, vocab)
-    if not valid['is_valid']:
-        return f"❌ Invalid recipe: {valid['reason']}"
-    current_recipe_context.update({
-        "title": outs['title'],
-        "ingredients": outs['ingrs'],
-        "instructions": outs['recipe']
-    })
-    return format_recipe(outs['title'], outs['ingrs'], outs['recipe'], "English (original)")
-
-# ============== GOOGLE TTS ==============
-languages_tts = {
-    "English": "en",
-    "Chinese": "zh-CN",
-    "Japanese": "ja",
-    "Vietnamese": "vi",
-}
-
-async def fetch_tts_audio_async(session, chunk, lang_code):
-    url = f"https://translate.google.com/translate_tts?ie=UTF-8&q={quote_plus(chunk)}&tl={lang_code}&client=tw-ob"
-    headers = {
-        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
-        "Referer": "https://translate.google.com/",
-    }
-    try:
-        async with session.get(url, headers=headers, timeout=10) as response:
-            response.raise_for_status()
-            return await response.read()
-    except Exception as e:
-        print(f"TTS Error for chunk: {e}")
-        return None
-
-async def fetch_all_tts_audio(chunks, lang_code):
-    async with aiohttp.ClientSession() as session:
-        tasks = [fetch_tts_audio_async(session, chunk, lang_code) for chunk in chunks]
-        return await asyncio.gather(*tasks)
-
-def google_tts(text, lang):
-    if not text or text.startswith("❗"):
-        return None, gr.update(visible=False)
-    
-    # Clean text for TTS
-    clean_text = text.replace("**", "").replace("###", "").replace("- ", "")
-    for emoji in ["🍽️", "🧂", "📖"]:
-        clean_text = clean_text.replace(emoji, "")
-    
-    # Split into chunks (Google TTS max ~200 chars)
-    max_chunk_length = 200
-    chunks = [clean_text[i:i+max_chunk_length] for i in range(0, len(clean_text), max_chunk_length)]
-    if not chunks:
-        return None, gr.update(visible=False)
-    
-    # Fetch audio chunks asynchronously
-    lang_code = languages_tts.get(lang, "en")
-    audio_contents = asyncio.run(fetch_all_tts_audio(chunks, lang_code))
-    
-    # Filter out failed requests
-    audio_files = []
-    for i, content in enumerate(audio_contents):
-        if content:
-            with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as f:
-                f.write(content)
-                audio_files.append(f.name)
-    
-    if not audio_files:
-        return None, gr.update(visible=False)
-    
-    # Combine audio if FFmpeg is available
-    if len(audio_files) == 1:
-        return audio_files[0], gr.update(visible=True)
-    
-    if which("ffmpeg"):
-        try:
-            combined = AudioSegment.empty()
-            for file in audio_files:
-                combined += AudioSegment.from_mp3(file)
-            output_file = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False).name
-            combined.export(output_file, format="mp3")
-            for file in audio_files:
-                os.unlink(file)
-            return output_file, gr.update(visible=True)
-        except Exception as e:
-            print(f"Error combining audio files: {e}")
-            # Fallback to first chunk
-            for i in range(1, len(audio_files)):
-                os.unlink(audio_files[i])
-            return audio_files[0], gr.update(visible=True)
-    else:
-        print("FFmpeg not found, returning first audio chunk.")
-        for i in range(1, len(audio_files)):
-            os.unlink(audio_files[i])
-        return audio_files[0], gr.update(visible=True)
-
-# ============== VIDEO SEARCH ==============
-def search_top_3_videos(keyword):
-    if not keyword.strip():
-        return ["", "", ""] * 3
-    try:
-        search = Search(f"How to make {keyword}")
-        results = search.results[:3]
-        embeds, titles, urls = [], [], []
-        for video in results:
-            embed_html = f'''
-            <iframe width="520" height="320"
-            src="https://www.youtube.com/embed/{video.video_id}"
-            frameborder="0" allowfullscreen></iframe>
-            '''
-            embeds.append(embed_html)
-            titles.append(video.title)
-            urls.append(f"https://www.youtube.com/watch?v={video.video_id}")
-        while len(embeds) < 3:
-            embeds.append("No video found")
-            titles.append("")
-            urls.append("")
-        return embeds + titles + urls
-    except Exception as e:
-        print(f"Video search error: {e}")
-        return ["", "", ""] * 3
-
-# ============== RESTAURANT SEARCH ==============
-def get_google_maps_search_url(dish_name, city="Ho Chi Minh City"):
-    query = f"{dish_name} in {city}"
-    url = f"https://www.google.com/maps/search/{query.replace(' ', '+')}"
-    return url
-
-def search_and_show_link(dish):
-    if not dish.strip():
-        return "Go to Google Maps", gr.update(visible=False)
-    url = get_google_maps_search_url(dish)
-    return url, gr.update(visible=True)
-
-# ============== PDF GENERATION ==============
-def generate_pdf_recipe():
-    if not current_recipe_context["title"]:
-        return None, "❗ Please generate a recipe from an image first."
-
-    output_file = "recipe.pdf"
-    doc = SimpleDocTemplate(output_file, pagesize=letter)
-    styles = getSampleStyleSheet()
-    story = []
-
-    if current_recipe_context["image"]:
-        try:
-            img_buffer = BytesIO()
-            current_recipe_context["image"].save(img_buffer, format="PNG")
-            img_buffer.seek(0)
-            img = ReportLabImage(img_buffer, width=200, height=200)
-            story.append(img)
-            story.append(Spacer(1, 12))
-        except Exception as e:
-            print(f"Error adding image to PDF: {e}")
-
-    story.append(Paragraph(current_recipe_context["title"], styles['Title']))
-    story.append(Spacer(1, 12))
-    story.append(Paragraph("Ingredients:", styles['Heading2']))
-    for ingr in current_recipe_context["ingredients"]:
-        story.append(Paragraph(f"- {ingr}", styles['Normal']))
-    story.append(Spacer(1, 12))
-    story.append(Paragraph("Instructions:", styles['Heading2']))
-    for i, instr in enumerate(current_recipe_context["instructions"], 1):
-        story.append(Paragraph(f"{i}. {instr}", styles['Normal']))
-
-    doc.build(story)
-    return output_file, "✅ Recipe saved as recipe.pdf"
-
-# ============== GRADIO UI ==============
-with gr.Blocks(theme=gr.themes.Soft(), title="AI Recipe Generator") as demo:
-    gr.Markdown("""
-    # 🍳 AI Recipe Generator & Multilingual Cooking Assistant
-    Generate recipes from images, translate to multiple languages, get cooking videos, chat with a culinary assistant, analyze nutrition, and find restaurants!
-    """)
-
-    with gr.Tab("📷 Generate Recipe"):
-        with gr.Row():
-            with gr.Column():
-                image_input = gr.Image(type="pil", label="Upload Dish Image", height=300)
-                gen_btn = gr.Button("Generate Recipe", variant="primary", elem_id="action-btn")
-                save_pdf_btn = gr.Button("Save as PDF", variant="secondary", elem_id="action-btn")
-                pdf_output = gr.File(label="Download Recipe PDF", interactive=False)
-            recipe_output = gr.Markdown("### Your recipe will appear here", elem_classes="recipe-box")
-        gen_btn.click(generate_recipe, inputs=image_input, outputs=recipe_output)
-        save_pdf_btn.click(fn=generate_pdf_recipe, outputs=[pdf_output, recipe_output])
-
-    with gr.Tab("🌍 Translate & TTS"):
-        with gr.Row():
-            with gr.Column():
-                lang_dropdown = gr.Dropdown(
-                    choices=["English (original)", "Japanese", "Chinese", "Vietnamese"],
-                    value="Japanese",
-                    label="Select Language"
-                )
-                with gr.Row():
-                    trans_btn = gr.Button("Translate Recipe", variant="primary", elem_id="action-btn")
-                    tts_btn = gr.Button("🔈 Listen to Recipe", variant="secondary", elem_id="action-btn")
-            with gr.Column():
-                translation_output = gr.Markdown("### Translated recipe will appear here", elem_classes="recipe-box")
-                tts_audio = gr.Audio(interactive=False, label="Audio Output", visible=False)
-        trans_btn.click(fn=translate_recipe, inputs=lang_dropdown, outputs=translation_output)
-        tts_btn.click(fn=google_tts, inputs=[translation_output, lang_dropdown], outputs=[tts_audio, tts_audio])
-
-    with gr.Tab("🎥 Cooking Videos"):
-        with gr.Row():
-            with gr.Column():
-                video_keyword = gr.Textbox(label="Search Cooking Videos", placeholder="e.g. beef pho")
-                search_btn = gr.Button("Search Videos", variant="primary", elem_id="action-btn")
-        with gr.Column():
-            video_embeds, video_titles, video_urls = [], [], []
-            for i in range(3):
-                with gr.Column():
-                    video_embeds.append(gr.HTML(label=f"🎬 Video {i+1}"))
-                    video_titles.append(gr.Textbox(label=f"📌 Title {i+1}", interactive=False))
-                    video_urls.append(gr.Textbox(label=f"🔗 URL {i+1}", interactive=False, visible=False))
-            search_btn.click(fn=search_top_3_videos, inputs=video_keyword, outputs=video_embeds + video_titles + video_urls)
-
-    with gr.Tab("💬 Culinary Chatbot"):
-        chatbot = gr.Chatbot(height=400, type="messages")
-        with gr.Row():
-            chat_input = gr.Textbox(placeholder="Ask about the dish...", scale=4)
-            chat_btn = gr.Button("Send", variant="primary", scale=1, elem_id="action-btn")
-        chat_btn.click(chat_with_bot, inputs=[chat_input, chatbot], outputs=[chat_input, chatbot])
-        chat_input.submit(chat_with_bot, inputs=[chat_input, chatbot], outputs=[chat_input, chatbot])
-
-    with gr.Tab("🥗 Nutrition Analysis"):
-        with gr.Row():
-            with gr.Column():
-                ingredient_input = gr.Textbox(
-                    label="🧾 Enter Ingredients (one per line or space-separated)",
-                    lines=10,
-                    placeholder="cheese\npepper\negg\n..."
-                )
-                with gr.Row():
-                    load_ingredients_btn = gr.Button("Load Recipe Ingredients", variant="secondary", elem_id="action-btn")
-                    analyze_btn = gr.Button("Analyze Nutrition", variant="primary", elem_id="action-btn")
-            with gr.Column():
-                nutrition_message = gr.Textbox(label="🔔 Message", interactive=False)
-                bar_chart = gr.Plot(label="📊 Bar Chart")
-                pie_chart = gr.Plot(label="🥧 Pie Chart")
-                line_chart = gr.Plot(label="📈 Line Chart")
-        load_ingredients_btn.click(fn=load_recipe_ingredients, outputs=ingredient_input)
-        analyze_btn.click(
-            fn=nutrition_analysis,
-            inputs=ingredient_input,
-            outputs=[nutrition_message, bar_chart, pie_chart, line_chart]
-        )
-
-    with gr.Tab("🍽️ Find Restaurants"):
-        with gr.Row():
-            with gr.Column():
-                dish_input = gr.Textbox(label="Enter Dish Name", placeholder="e.g. beef pho", interactive=True)
-                search_restaurant_btn = gr.Button("Find Restaurants", variant="primary", elem_id="action-btn")
-                open_maps_btn = gr.Button("Go to Google Maps", visible=True, variant="secondary", elem_id="open-maps-btn")
-        search_restaurant_btn.click(fn=search_and_show_link, inputs=dish_input, outputs=[open_maps_btn, open_maps_btn])
-        open_maps_btn.click(
-            fn=lambda url: url,
-            inputs=open_maps_btn,
-            outputs=None,
-            js="(url) => { if(url) window.open(url, '_blank'); }"
-        )
-
-    demo.css = """
-    .recipe-box {
-        padding: 20px;
-        border-radius: 10px;
-        background: #f9f9f9;
-        border: 1px solid #e0e0e0;
-    }
-    .dark .recipe-box {
-        background: #2a2a2a;
-        border-color: #444;
-    }
-    .gr-box {
-        margin-bottom: 20px;
-    }
-    #action-btn {
-        max-width: 220px;
-        margin: 10px auto;
-        font-weight: 600;
-        font-size: 16px;
-        border-radius: 8px;
-    }
-    #open-maps-btn {
-        max-width: 220px;
-        margin: 10px auto;
-        font-weight: 600;
-        font-size: 16px;
-        border-radius: 8px;
-    }
-    """
-
-if __name__ == "__main__":
-    demo.launch(pwa=True)
+import os
+import sys
+import torch
+import pickle
+import logging
+import tempfile
+import requests
+import re
+import asyncio
+import aiohttp
+from urllib.parse import quote_plus
+from pytube import Search
+from PIL import Image
+from torchvision import transforms
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline, AutoModelForCausalLM
+import gradio as gr
+import pandas as pd
+import plotly.express as px
+from reportlab.lib.pagesizes import letter
+from reportlab.lib import colors
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
+from reportlab.lib.styles import getSampleStyleSheet
+from io import BytesIO
+from langchain_huggingface import HuggingFacePipeline
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_core.chat_history import InMemoryChatMessageHistory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from pydub import AudioSegment
+from pydub.utils import which
+
+# Local imports (assumed to be available)
+from args import get_parser
+from model import get_model
+from output_utils import prepare_output
+
+# ============== DEVICE CONFIG ==============
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+map_loc = None if torch.cuda.is_available() else "cpu"
+logging.getLogger("pytube").setLevel(logging.ERROR)
+
+# ============== LOAD TRANSLATION MODELS ==============
+model_envit5_name = "VietAI/envit5-translation"
+try:
+    tokenizer_envit5 = AutoTokenizer.from_pretrained(model_envit5_name)
+    model_envit5 = AutoModelForSeq2SeqLM.from_pretrained(
+        model_envit5_name,
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+    ).to(device)
+    pipe_envit5 = pipeline(
+        "text2text-generation",
+        model=model_envit5,
+        tokenizer=tokenizer_envit5,
+        device=0 if torch.cuda.is_available() else -1,
+        max_new_tokens=512,
+        do_sample=False
+    )
+except Exception as e:
+    print(f"Error loading Vietnamese model: {e}")
+    pipe_envit5 = None
+
+models = {
+    "Japanese": {"model_name": "Helsinki-NLP/opus-mt-en-jap"},
+    "Chinese": {"model_name": "Helsinki-NLP/opus-mt-en-zh"}
+}
+
+for lang in models:
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(models[lang]["model_name"])
+        model = AutoModelForSeq2SeqLM.from_pretrained(
+            models[lang]["model_name"],
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+        ).to(device)
+        models[lang]["pipe"] = pipeline(
+            "translation",
+            model=model,
+            tokenizer=tokenizer,
+            device=0 if torch.cuda.is_available() else -1,
+            max_length=512,
+            batch_size=4 if torch.cuda.is_available() else 1,
+            truncation=True
+        )
+    except Exception as e:
+        print(f"Error loading {lang} model: {e}")
+        models[lang]["pipe"] = None
+
+# ============== LOAD CHATBOT MODEL ==============
+chatbot_tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
+chatbot_model = AutoModelForCausalLM.from_pretrained(
+    "bigscience/bloomz-560m",
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+).to(device)
+
+chatbot_pipeline = pipeline(
+    "text-generation",
+    model=chatbot_model,
+    tokenizer=chatbot_tokenizer,
+    device=0 if torch.cuda.is_available() else -1,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+    pad_token_id=chatbot_tokenizer.eos_token_id,
+    batch_size=1
+)
+llm = HuggingFacePipeline(pipeline=chatbot_pipeline)
+
+# LangChain Chatbot Setup
+prompt = ChatPromptTemplate.from_template("""
+You are a professional culinary assistant. You will answer the user's question directly based on the provided recipe. 
+Do not repeat the recipe or question in your answer. Be concise.
+
+Dish: {title}
+Ingredients: {ingredients}
+Instructions: {instructions}
+
+User Question: {question}
+Answer:
+""")
+
+
+chain = prompt | llm
+chat_histories = {}
+
+def get_session_history(session_id):
+    if session_id not in chat_histories:
+        chat_histories[session_id] = InMemoryChatMessageHistory()
+    return chat_histories[session_id]
+
+chatbot_chain = RunnableWithMessageHistory(
+    chain,
+    get_session_history,
+    input_messages_key="question",
+    history_messages_key="history"
+)
+
+# ============== GLOBAL STATE ==============
+current_recipe_context = {"context": "", "title": "", "ingredients": [], "instructions": [], "image": None}
+
+# ============== RECIPE FORMAT & TRANSLATE ==============
+def format_recipe(title, ingredients, instructions, lang):
+    emoji = {"title": "🍽️", "ingredients": "🧂", "instructions": "📖"}
+    titles = {
+        "en": {"ingredients": "Ingredients", "instructions": "Instructions"},
+        "ja": {"ingredients": "Ingredients (材料)", "instructions": "Instructions (作り方)"},
+        "zh": {"ingredients": "Ingredients (食材)", "instructions": "Instructions (步骤)"},
+        "vi": {"ingredients": "Ingredients (Nguyên liệu)", "instructions": "Instructions (Cách làm)"},
+    }
+
+    code_mapping = {
+        "English (original)": "en",
+        "Japanese": "ja",
+        "Chinese": "zh",
+        "Vietnamese": "vi",
+    }
+    code = code_mapping.get(lang, "en")
+
+    result = [f"### {emoji['title']} {title}", f"**{emoji['ingredients']} {titles[code]['ingredients']}:**"]
+    result.extend([f"- {i}" for i in ingredients])
+    result.append(f"\n**{emoji['instructions']} {titles[code]['instructions']}:**")
+    result.extend([f"{i+1}. {step}" for i, step in enumerate(instructions)])
+    return "\n".join(result)
+
+def translate_section(text, lang):
+    if lang == "English (original)":
+        return text
+
+    if lang == "Vietnamese":
+        if pipe_envit5 is None:
+            return f"❗ Vietnamese translation model not available"
+        try:
+            max_chunk_length = 400
+            if len(text) > max_chunk_length:
+                sentences = text.split('. ')
+                chunks = []
+                current_chunk = ""
+                for sentence in sentences:
+                    if len(current_chunk) + len(sentence) < max_chunk_length:
+                        current_chunk += sentence + ". "
+                    else:
+                        chunks.append(current_chunk)
+                        current_chunk = sentence + ". "
+                if current_chunk:
+                    chunks.append(current_chunk)
+            else:
+                chunks = [text]
+
+            translated_chunks = []
+            for chunk in chunks:
+                chunk = f"en-vi: {chunk}"
+                translated = pipe_envit5(chunk, max_new_tokens=512)[0]["generated_text"]
+                translated = translated.replace("vi: vi: ", "").replace("vi: Vi: ", "").replace("vi: ", "").strip()
+                translated_chunks.append(translated)
+
+            return " ".join(translated_chunks)
+        except Exception as e:
+            print(f"Vietnamese translation error: {e}")
+            return text
+
+    if models.get(lang, {}).get("pipe") is None:
+        return f"❗ Translation model for {lang} not available"
+
+    try:
+        max_chunk_length = 400
+        if len(text) > max_chunk_length:
+            sentences = text.split('. ')
+            chunks = []
+            current_chunk = ""
+            for sentence in sentences:
+                if len(current_chunk) + len(sentence) < max_chunk_length:
+                    current_chunk += sentence + ". "
+                else:
+                    chunks.append(current_chunk)
+                    current_chunk = sentence + ". "
+            if current_chunk:
+                chunks.append(current_chunk)
+        else:
+            chunks = [text]
+
+        translated_chunks = []
+        for chunk in chunks:
+            translated = models[lang]["pipe"](chunk, max_length=512)[0]["translation_text"]
+            translated_chunks.append(translated)
+
+        return " ".join(translated_chunks)
+    except Exception as e:
+        print(f"Translation error ({lang}): {e}")
+        return text
+
+def translate_recipe(lang):
+    if not current_recipe_context["title"]:
+        return "❗ Please generate a recipe from an image first."
+    title = translate_section(current_recipe_context["title"], lang)
+    ingrs = [translate_section(i, lang) for i in current_recipe_context["ingredients"]]
+    instrs = [translate_section(s, lang) for s in current_recipe_context["instructions"]]
+    return format_recipe(title, ingrs, instrs, lang)
+
+# ============== NUTRITION ANALYSIS ==============
+def nutrition_analysis(ingredient_input):
+    ingredients = " ".join(ingredient_input.strip().split())
+    api_url = f'https://api.api-ninjas.com/v1/nutrition?query={ingredients}'
+    headers = {'X-Api-Key': 'AHVy+tpkUoueBNdaFs9nCg==sFZTMRn8ikZVzx6E'}  
+    response = requests.get(api_url, headers=headers)
+    if response.status_code != 200:
+        return "❌ API error or quota exceeded.", None, None, None
+    data = response.json()
+    df = pd.DataFrame(data)
+    numeric_cols = []
+    for col in df.columns:
+        if col == "name":
+            continue
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+        if df[col].notna().sum() > 0:
+            numeric_cols.append(col)
+    if df.empty or len(numeric_cols) < 3:
+        return "⚠️ Insufficient numerical data for charts (need at least 3 metrics).", None, None, None
+    draw_cols = numeric_cols[:3]
+    fig_bar = px.bar(df, x="name", y=draw_cols[0], title=f"Bar Chart: {draw_cols[0]}", text_auto=True)
+    pie_data = df[[draw_cols[1], "name"]].dropna()
+    if pie_data[draw_cols[1]].sum() > 0:
+        fig_pie = px.pie(pie_data, names="name", values=draw_cols[1], title=f"Pie Chart: {draw_cols[1]}")
+    else:
+        fig_pie = px.bar(title="⚠️ Insufficient data for pie chart")
+    fig_line = px.line(df, x="name", y=draw_cols[2], markers=True, title=f"Line Chart: {draw_cols[2]}")
+    return "✅ Analysis successful!", fig_bar, fig_pie, fig_line
+
+def load_recipe_ingredients():
+    if not current_recipe_context["ingredients"]:
+        return "⚠️ No ingredients available. Generate a recipe first."
+    return "\n".join(current_recipe_context["ingredients"])
+
+# ============== CHATBOT ==============
+def clean_response(response):
+    # Remove everything before "Answer:" if present
+    if "Answer:" in response:
+        response = response.split("Answer:")[-1]
+
+    # Remove potential repetitions of Dish, Ingredients, Instructions
+    response = re.sub(r"Dish:.*?(Ingredients:|Instructions:).*?", "", response, flags=re.DOTALL)
+    response = re.sub(r"Ingredients:.*?(Instructions:).*?", "", response, flags=re.DOTALL)
+    response = re.sub(r"Instructions:.*", "", response, flags=re.DOTALL)
+
+    # Remove redundant system info
+    response = re.sub(r"You are a professional culinary assistant.*?Answer:", "", response, flags=re.DOTALL)
+    
+    # Remove duplicate user question inside response (very common in these LLM outputs)
+    response = re.sub(r"User Question:.*", "", response, flags=re.DOTALL)
+    
+    # Final strip + cleanup
+    return response.strip()
+
+
+def validate_cooking_time(question, instructions):
+    # Extract cooking times from instructions
+    time_pattern = r"(\d+)\s*(minutes|minute)"
+    total_time = 0
+    for instr in instructions:
+        matches = re.findall(time_pattern, instr)
+        for match in matches:
+            total_time += int(match[0])
+    
+    # Check if user question contains a time
+    user_time = re.search(time_pattern, question)
+    if user_time:
+        user_minutes = int(user_time.group(1))
+        if user_minutes != total_time:
+            return f"The recipe takes about {total_time} minutes to cook, not {user_minutes} minutes."
+    return None
+
+def generate_chat_response(message, session_id="default"):
+    if not current_recipe_context["title"]:
+        return "Please generate a recipe from an image before asking about the dish."
+    
+    # Validate cooking time if relevant
+    correction = validate_cooking_time(message, current_recipe_context["instructions"])
+    
+    response = chatbot_chain.invoke(
+        {
+            "title": current_recipe_context["title"],
+            "ingredients": ", ".join(current_recipe_context["ingredients"]),
+            "instructions": " ".join(current_recipe_context["instructions"]),
+            "question": message
+        },
+        config={"configurable": {"session_id": session_id}}
+    )
+    
+    response = clean_response(response)
+    if correction:
+        response = f"{correction} {response}"
+    
+    return response.strip()
+
+
+def chat_with_bot(message, chat_history, session_id="default"):
+    if not message.strip():
+        return "", chat_history
+    response = generate_chat_response(message, session_id)
+    chat_history.append({"role": "user", "content": message})
+    chat_history.append({"role": "assistant", "content": response})
+    return "", chat_history
+
+# ============== IMAGE TO RECIPE ==============
+with open("ingr_vocab.pkl", 'rb') as f:
+    ingrs_vocab = pickle.load(f)
+with open("instr_vocab.pkl", 'rb') as f:
+    vocab = pickle.load(f)
+
+args = get_parser()
+args.maxseqlen = 15
+args.ingrs_only = False
+model_ic = get_model(args, len(ingrs_vocab), len(vocab))
+model_ic.load_state_dict(torch.load("modelbest.ckpt", map_location=map_loc, weights_only=True))
+model_ic.to(device).eval()
+
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+])
+
+def generate_recipe(image):
+    if image is None:
+        return "❗ Please upload an image."
+    current_recipe_context["image"] = image
+    image = transform(image.convert("RGB")).unsqueeze(0).to(device)
+    with torch.no_grad():
+        outputs = model_ic.sample(image, greedy=True, temperature=1.0, beam=-1, true_ingrs=None)
+    ids = (outputs['ingr_ids'].cpu().numpy(), outputs['recipe_ids'].cpu().numpy())
+    outs, valid = prepare_output(ids[1][0], ids[0][0], ingrs_vocab, vocab)
+    if not valid['is_valid']:
+        return f"❌ Invalid recipe: {valid['reason']}"
+    current_recipe_context.update({
+        "title": outs['title'],
+        "ingredients": outs['ingrs'],
+        "instructions": outs['recipe']
+    })
+    return format_recipe(outs['title'], outs['ingrs'], outs['recipe'], "English (original)")
+
+# ============== GOOGLE TTS ==============
+languages_tts = {
+    "English": "en",
+    "Chinese": "zh-CN",
+    "Japanese": "ja",
+    "Vietnamese": "vi",
+}
+
+async def fetch_tts_audio_async(session, chunk, lang_code):
+    url = f"https://translate.google.com/translate_tts?ie=UTF-8&q={quote_plus(chunk)}&tl={lang_code}&client=tw-ob"
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
+        "Referer": "https://translate.google.com/",
+    }
+    try:
+        async with session.get(url, headers=headers, timeout=10) as response:
+            response.raise_for_status()
+            return await response.read()
+    except Exception as e:
+        print(f"TTS Error for chunk: {e}")
+        return None
+
+async def fetch_all_tts_audio(chunks, lang_code):
+    async with aiohttp.ClientSession() as session:
+        tasks = [fetch_tts_audio_async(session, chunk, lang_code) for chunk in chunks]
+        return await asyncio.gather(*tasks)
+
+def google_tts(text, lang):
+    if not text or text.startswith("❗"):
+        return None, gr.update(visible=False)
+    
+    # Clean text for TTS
+    clean_text = text.replace("**", "").replace("###", "").replace("- ", "")
+    for emoji in ["🍽️", "🧂", "📖"]:
+        clean_text = clean_text.replace(emoji, "")
+    
+    # Split into chunks (Google TTS max ~200 chars)
+    max_chunk_length = 200
+    chunks = [clean_text[i:i+max_chunk_length] for i in range(0, len(clean_text), max_chunk_length)]
+    if not chunks:
+        return None, gr.update(visible=False)
+    
+    # Fetch audio chunks asynchronously
+    lang_code = languages_tts.get(lang, "en")
+    audio_contents = asyncio.run(fetch_all_tts_audio(chunks, lang_code))
+    
+    # Filter out failed requests
+    audio_files = []
+    for i, content in enumerate(audio_contents):
+        if content:
+            with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as f:
+                f.write(content)
+                audio_files.append(f.name)
+    
+    if not audio_files:
+        return None, gr.update(visible=False)
+    
+    # Combine audio if FFmpeg is available
+    if len(audio_files) == 1:
+        return audio_files[0], gr.update(visible=True)
+    
+    if which("ffmpeg"):
+        try:
+            combined = AudioSegment.empty()
+            for file in audio_files:
+                combined += AudioSegment.from_mp3(file)
+            output_file = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False).name
+            combined.export(output_file, format="mp3")
+            for file in audio_files:
+                os.unlink(file)
+            return output_file, gr.update(visible=True)
+        except Exception as e:
+            print(f"Error combining audio files: {e}")
+            # Fallback to first chunk
+            for i in range(1, len(audio_files)):
+                os.unlink(audio_files[i])
+            return audio_files[0], gr.update(visible=True)
+    else:
+        print("FFmpeg not found, returning first audio chunk.")
+        for i in range(1, len(audio_files)):
+            os.unlink(audio_files[i])
+        return audio_files[0], gr.update(visible=True)
+
+# ============== VIDEO SEARCH ==============
+def search_top_3_videos(keyword):
+    if not keyword.strip():
+        return ["", "", ""] * 3
+    try:
+        search = Search(f"How to make {keyword}")
+        results = search.results[:3]
+        embeds, titles, urls = [], [], []
+        for video in results:
+            embed_html = f'''
+            <iframe width="520" height="320"
+            src="https://www.youtube.com/embed/{video.video_id}"
+            frameborder="0" allowfullscreen></iframe>
+            '''
+            embeds.append(embed_html)
+            titles.append(video.title)
+            urls.append(f"https://www.youtube.com/watch?v={video.video_id}")
+        while len(embeds) < 3:
+            embeds.append("No video found")
+            titles.append("")
+            urls.append("")
+        return embeds + titles + urls
+    except Exception as e:
+        print(f"Video search error: {e}")
+        return ["", "", ""] * 3
+
+# ============== RESTAURANT SEARCH ==============
+def get_google_maps_search_url(dish_name, city="Ho Chi Minh City"):
+    query = f"{dish_name} in {city}"
+    url = f"https://www.google.com/maps/search/{query.replace(' ', '+')}"
+    return url
+
+def search_and_show_link(dish):
+    if not dish.strip():
+        return "Go to Google Maps", gr.update(visible=False)
+    url = get_google_maps_search_url(dish)
+    return url, gr.update(visible=True)
+
+# ============== PDF GENERATION ==============
+def generate_pdf_recipe():
+    if not current_recipe_context["title"]:
+        return None, "❗ Please generate a recipe from an image first."
+
+    output_file = "recipe.pdf"
+    doc = SimpleDocTemplate(output_file, pagesize=letter)
+    styles = getSampleStyleSheet()
+    story = []
+
+    if current_recipe_context["image"]:
+        try:
+            img_buffer = BytesIO()
+            current_recipe_context["image"].save(img_buffer, format="PNG")
+            img_buffer.seek(0)
+            img = ReportLabImage(img_buffer, width=200, height=200)
+            story.append(img)
+            story.append(Spacer(1, 12))
+        except Exception as e:
+            print(f"Error adding image to PDF: {e}")
+
+    story.append(Paragraph(current_recipe_context["title"], styles['Title']))
+    story.append(Spacer(1, 12))
+    story.append(Paragraph("Ingredients:", styles['Heading2']))
+    for ingr in current_recipe_context["ingredients"]:
+        story.append(Paragraph(f"- {ingr}", styles['Normal']))
+    story.append(Spacer(1, 12))
+    story.append(Paragraph("Instructions:", styles['Heading2']))
+    for i, instr in enumerate(current_recipe_context["instructions"], 1):
+        story.append(Paragraph(f"{i}. {instr}", styles['Normal']))
+
+    doc.build(story)
+    return output_file, "✅ Recipe saved as recipe.pdf"
+
+# ============== GRADIO UI ==============
+with gr.Blocks(theme=gr.themes.Soft(), title="AI Recipe Generator") as demo:
+    gr.Markdown("""
+    # 🍳 AI Recipe Generator & Multilingual Cooking Assistant
+    Generate recipes from images, translate to multiple languages, get cooking videos, chat with a culinary assistant, analyze nutrition, and find restaurants!
+    """)
+
+    with gr.Tab("📷 Generate Recipe"):
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(type="pil", label="Upload Dish Image", height=300)
+                gen_btn = gr.Button("Generate Recipe", variant="primary", elem_id="action-btn")
+                save_pdf_btn = gr.Button("Save as PDF", variant="secondary", elem_id="action-btn")
+                pdf_output = gr.File(label="Download Recipe PDF", interactive=False)
+            recipe_output = gr.Markdown("### Your recipe will appear here", elem_classes="recipe-box")
+        gen_btn.click(generate_recipe, inputs=image_input, outputs=recipe_output)
+        save_pdf_btn.click(fn=generate_pdf_recipe, outputs=[pdf_output, recipe_output])
+
+    with gr.Tab("🌍 Translate & TTS"):
+        with gr.Row():
+            with gr.Column():
+                lang_dropdown = gr.Dropdown(
+                    choices=["English (original)", "Japanese", "Chinese", "Vietnamese"],
+                    value="Japanese",
+                    label="Select Language"
+                )
+                with gr.Row():
+                    trans_btn = gr.Button("Translate Recipe", variant="primary", elem_id="action-btn")
+                    tts_btn = gr.Button("🔈 Listen to Recipe", variant="secondary", elem_id="action-btn")
+            with gr.Column():
+                translation_output = gr.Markdown("### Translated recipe will appear here", elem_classes="recipe-box")
+                tts_audio = gr.Audio(interactive=False, label="Audio Output", visible=False)
+        trans_btn.click(fn=translate_recipe, inputs=lang_dropdown, outputs=translation_output)
+        tts_btn.click(fn=google_tts, inputs=[translation_output, lang_dropdown], outputs=[tts_audio, tts_audio])
+
+    with gr.Tab("🎥 Cooking Videos"):
+        with gr.Row():
+            with gr.Column():
+                video_keyword = gr.Textbox(label="Search Cooking Videos", placeholder="e.g. beef pho")
+                search_btn = gr.Button("Search Videos", variant="primary", elem_id="action-btn")
+        with gr.Column():
+            video_embeds, video_titles, video_urls = [], [], []
+            for i in range(3):
+                with gr.Column():
+                    video_embeds.append(gr.HTML(label=f"🎬 Video {i+1}"))
+                    video_titles.append(gr.Textbox(label=f"📌 Title {i+1}", interactive=False))
+                    video_urls.append(gr.Textbox(label=f"🔗 URL {i+1}", interactive=False, visible=False))
+            search_btn.click(fn=search_top_3_videos, inputs=video_keyword, outputs=video_embeds + video_titles + video_urls)
+
+    with gr.Tab("💬 Culinary Chatbot"):
+        chatbot = gr.Chatbot(height=400, type="messages")
+        with gr.Row():
+            chat_input = gr.Textbox(placeholder="Ask about the dish...", scale=4)
+            chat_btn = gr.Button("Send", variant="primary", scale=1, elem_id="action-btn")
+        chat_btn.click(chat_with_bot, inputs=[chat_input, chatbot], outputs=[chat_input, chatbot])
+        chat_input.submit(chat_with_bot, inputs=[chat_input, chatbot], outputs=[chat_input, chatbot])
+
+    with gr.Tab("🥗 Nutrition Analysis"):
+        with gr.Row():
+            with gr.Column():
+                ingredient_input = gr.Textbox(
+                    label="🧾 Enter Ingredients (one per line or space-separated)",
+                    lines=10,
+                    placeholder="cheese\npepper\negg\n..."
+                )
+                with gr.Row():
+                    load_ingredients_btn = gr.Button("Load Recipe Ingredients", variant="secondary", elem_id="action-btn")
+                    analyze_btn = gr.Button("Analyze Nutrition", variant="primary", elem_id="action-btn")
+            with gr.Column():
+                nutrition_message = gr.Textbox(label="🔔 Message", interactive=False)
+                bar_chart = gr.Plot(label="📊 Bar Chart")
+                pie_chart = gr.Plot(label="🥧 Pie Chart")
+                line_chart = gr.Plot(label="📈 Line Chart")
+        load_ingredients_btn.click(fn=load_recipe_ingredients, outputs=ingredient_input)
+        analyze_btn.click(
+            fn=nutrition_analysis,
+            inputs=ingredient_input,
+            outputs=[nutrition_message, bar_chart, pie_chart, line_chart]
+        )
+
+    with gr.Tab("🍽️ Find Restaurants"):
+        with gr.Row():
+            with gr.Column():
+                dish_input = gr.Textbox(label="Enter Dish Name", placeholder="e.g. beef pho", interactive=True)
+                search_restaurant_btn = gr.Button("Find Restaurants", variant="primary", elem_id="action-btn")
+                open_maps_btn = gr.Button("Go to Google Maps", visible=True, variant="secondary", elem_id="open-maps-btn")
+        search_restaurant_btn.click(fn=search_and_show_link, inputs=dish_input, outputs=[open_maps_btn, open_maps_btn])
+        open_maps_btn.click(
+            fn=lambda url: url,
+            inputs=open_maps_btn,
+            outputs=None,
+            js="(url) => { if(url) window.open(url, '_blank'); }"
+        )
+
+    demo.css = """
+    .recipe-box {
+        padding: 20px;
+        border-radius: 10px;
+        background: #f9f9f9;
+        border: 1px solid #e0e0e0;
+    }
+    .dark .recipe-box {
+        background: #2a2a2a;
+        border-color: #444;
+    }
+    .gr-box {
+        margin-bottom: 20px;
+    }
+    #action-btn {
+        max-width: 220px;
+        margin: 10px auto;
+        font-weight: 600;
+        font-size: 16px;
+        border-radius: 8px;
+    }
+    #open-maps-btn {
+        max-width: 220px;
+        margin: 10px auto;
+        font-weight: 600;
+        font-size: 16px;
+        border-radius: 8px;
+    }
+    """
+
+if __name__ == "__main__":
+    demo.launch()