ui.py

"""Gradio UI setup"""
import time
import gradio as gr
import spaces
from config import TITLE, DESCRIPTION, CSS, MEDSWIN_MODELS, DEFAULT_MEDICAL_MODEL
import config
from indexing import create_or_update_index
from pipeline import stream_chat
from voice import transcribe_audio, generate_speech
from models import (
    initialize_medical_model,
    is_model_loaded,
    get_model_loading_state,
    set_model_loading_state,
    initialize_tts_model,
    initialize_whisper_model,
    TTS_AVAILABLE,
    WHISPER_AVAILABLE,
)
from logger import logger

MAX_DURATION = 120

def create_demo():
    """Create and return Gradio demo interface"""
    with gr.Blocks(css=CSS, theme=gr.themes.Soft()) as demo:
        gr.HTML(TITLE)
        gr.HTML(DESCRIPTION)
        
        with gr.Row(elem_classes="main-container"):
            with gr.Column(elem_classes="upload-section"):
                file_upload = gr.File(
                    file_count="multiple",
                    label="Drag and Drop Files Here",
                    file_types=[".pdf", ".txt", ".doc", ".docx", ".md", ".json", ".xml", ".csv"],
                    elem_id="file-upload"
                )
                upload_button = gr.Button("Upload & Index", elem_classes="upload-button")
                status_output = gr.Textbox(
                    label="Status",
                    placeholder="Upload files to start...",
                    interactive=False
                )
                file_info_output = gr.HTML(
                    label="File Information",
                    elem_classes="processing-info"
                )
                upload_button.click(
                    fn=create_or_update_index,
                    inputs=[file_upload],
                    outputs=[status_output, file_info_output]
                )
            
            with gr.Column(elem_classes="chatbot-container"):
                chatbot = gr.Chatbot(
                    height=500,
                    placeholder="Chat with MedSwin... Type your question below.",
                    show_label=False,
                    type="messages"
                )
                with gr.Row(elem_classes="input-row"):
                    message_input = gr.Textbox(
                        placeholder="Type your medical question here...",
                        show_label=False,
                        container=False,
                        lines=1,
                        scale=10
                    )
                    mic_button = gr.Audio(
                        sources=["microphone"],
                        type="filepath",
                        label="",
                        show_label=False,
                        container=False,
                        scale=1
                    )
                    submit_button = gr.Button("➤", elem_classes="submit-btn", scale=1)
                
                recording_timer = gr.Textbox(
                    value="",
                    label="",
                    show_label=False,
                    interactive=False,
                    visible=False,
                    container=False,
                    elem_classes="recording-timer"
                )
                
                recording_start_time = [None]
                
                def handle_recording_start():
                    """Called when recording starts"""
                    recording_start_time[0] = time.time()
                    return gr.update(visible=True, value="Recording... 0s")
                
                def handle_recording_stop(audio):
                    """Called when recording stops"""
                    recording_start_time[0] = None
                    if audio is None:
                        return gr.update(visible=False, value=""), ""
                    transcribed = transcribe_audio(audio)
                    return gr.update(visible=False, value=""), transcribed
                
                mic_button.start_recording(
                    fn=handle_recording_start,
                    outputs=[recording_timer]
                )
                
                mic_button.stop_recording(
                    fn=handle_recording_stop,
                    inputs=[mic_button],
                    outputs=[recording_timer, message_input]
                )
                
                with gr.Row(visible=False) as tts_row:
                    tts_text = gr.Textbox(visible=False)
                    tts_audio = gr.Audio(label="Generated Speech", visible=False)
                
                def generate_speech_from_chat(history):
                    """Extract last assistant message and generate speech"""
                    if not history or len(history) == 0:
                        return None
                    last_msg = history[-1]
                    if last_msg.get("role") == "assistant":
                        text = last_msg.get("content", "").replace(" 🔊", "").strip()
                        if text:
                            audio_path = generate_speech(text)
                            return audio_path
                    return None
                
                tts_button = gr.Button("🔊 Play Response", visible=False, size="sm")
                
                def update_tts_button(history):
                    if history and len(history) > 0 and history[-1].get("role") == "assistant":
                        return gr.update(visible=True)
                    return gr.update(visible=False)
                
                chatbot.change(
                    fn=update_tts_button,
                    inputs=[chatbot],
                    outputs=[tts_button]
                )
                
                tts_button.click(
                    fn=generate_speech_from_chat,
                    inputs=[chatbot],
                    outputs=[tts_audio]
                )
                
                with gr.Accordion("⚙️ Advanced Settings", open=False):
                    with gr.Row():
                        disable_agentic_reasoning = gr.Checkbox(
                            value=False,
                            label="Disable agentic reasoning",
                            info="Use MedSwin model alone without agentic reasoning, RAG, or web search"
                        )
                        show_agentic_thought = gr.Button(
                            "Show agentic thought",
                            size="sm"
                        )
                    enable_clinical_intake = gr.Checkbox(
                        value=True,
                        label="Enable clinical intake (max 5 Q&A)",
                        info="Ask focused follow-up questions before breaking down the case"
                    )
                    agentic_thoughts_box = gr.Textbox(
                        label="Agentic Thoughts",
                        placeholder="Internal thoughts from MedSwin and supervisor will appear here...",
                        lines=8,
                        max_lines=15,
                        interactive=False,
                        visible=False,
                        elem_classes="agentic-thoughts"
                    )
                    with gr.Row():
                        use_rag = gr.Checkbox(
                            value=False,
                            label="Enable Document RAG",
                            info="Answer based on uploaded documents (upload required)"
                        )
                        use_web_search = gr.Checkbox(
                            value=False,
                            label="Enable Web Search (MCP)",
                            info="Fetch knowledge from online medical resources"
                        )
                    
                    medical_model = gr.Radio(
                        choices=list(MEDSWIN_MODELS.keys()),
                        value=DEFAULT_MEDICAL_MODEL,
                        label="Medical Model",
                        info="MedSwin DT (default), others download on selection"
                    )
                    model_status = gr.Textbox(
                        value="Checking model status...",
                        label="Model Status",
                        interactive=False,
                        visible=True,
                        lines=3,
                        max_lines=3,
                        elem_classes="model-status"
                    )
                    
                    system_prompt = gr.Textbox(
                        value="As a medical specialist, provide detailed and accurate answers based on the provided medical documents and context. Ensure all information is clinically accurate and cite sources when available.",
                        label="System Prompt",
                        lines=3
                    )
                    
                    with gr.Tab("Generation Parameters"):
                        temperature = gr.Slider(
                            minimum=0,
                            maximum=1,
                            step=0.1,
                            value=0.2,
                            label="Temperature"
                        )
                        max_new_tokens = gr.Slider(
                            minimum=512,
                            maximum=4096,
                            step=128,
                            value=2048,
                            label="Max New Tokens",
                            info="Increased for medical models to prevent early stopping"
                        )
                        top_p = gr.Slider(
                            minimum=0.0,
                            maximum=1.0,
                            step=0.1,
                            value=0.7,
                            label="Top P"
                        )
                        top_k = gr.Slider(
                            minimum=1,
                            maximum=100,
                            step=1,
                            value=50,
                            label="Top K"
                        )
                        penalty = gr.Slider(
                            minimum=0.0,
                            maximum=2.0,
                            step=0.1,
                            value=1.2,
                            label="Repetition Penalty"
                        )
                    
                    with gr.Tab("Retrieval Parameters"):
                        retriever_k = gr.Slider(
                            minimum=5,
                            maximum=30,
                            step=1,
                            value=15,
                            label="Initial Retrieval Size (Top K)"
                        )
                        merge_threshold = gr.Slider(
                            minimum=0.1,
                            maximum=0.9,
                            step=0.1,
                            value=0.5,
                            label="Merge Threshold (lower = more merging)"
                        )
                    
                    # MedSwin Model Links
                    gr.Markdown(
                        """
                        <div style="margin-top: 20px; padding: 15px; background-color: #f5f5f5; border-radius: 8px;">
                            <h4 style="margin-top: 0; margin-bottom: 10px;">🔗 MedSwin Models on Hugging Face</h4>
                            <div style="display: flex; flex-wrap: wrap; gap: 10px;">
                                <a href="https://huggingface.co/MedSwin/MedSwin-Merged-DaRE-TIES-KD-0.7" target="_blank" style="text-decoration: none; padding: 5px 10px; background-color: #1a73e8; color: white; border-radius: 4px; font-size: 12px;">MedSwin DT</a>
                                <a href="https://huggingface.co/MedSwin/MedSwin-Merged-NuSLERP-KD-0.7" target="_blank" style="text-decoration: none; padding: 5px 10px; background-color: #1a73e8; color: white; border-radius: 4px; font-size: 12px;">MedSwin Nsl</a>
                                <a href="https://huggingface.co/MedSwin/MedSwin-Merged-DaRE-Linear-KD-0.7" target="_blank" style="text-decoration: none; padding: 5px 10px; background-color: #1a73e8; color: white; border-radius: 4px; font-size: 12px;">MedSwin DL</a>
                                <a href="https://huggingface.co/MedSwin/MedSwin-Merged-TIES-KD-0.7" target="_blank" style="text-decoration: none; padding: 5px 10px; background-color: #1a73e8; color: white; border-radius: 4px; font-size: 12px;">MedSwin Ti</a>
                                <a href="https://huggingface.co/MedSwin/MedSwin-Merged-TA-SFT-0.7" target="_blank" style="text-decoration: none; padding: 5px 10px; background-color: #1a73e8; color: white; border-radius: 4px; font-size: 12px;">MedSwin TA</a>
                                <a href="https://huggingface.co/MedSwin/MedSwin-7B-SFT" target="_blank" style="text-decoration: none; padding: 5px 10px; background-color: #1a73e8; color: white; border-radius: 4px; font-size: 12px;">MedSwin SFT</a>
                                <a href="https://huggingface.co/MedSwin/MedSwin-7B-KD" target="_blank" style="text-decoration: none; padding: 5px 10px; background-color: #1a73e8; color: white; border-radius: 4px; font-size: 12px;">MedSwin KD</a>
                            </div>
                            <p style="margin-top: 10px; margin-bottom: 0; font-size: 11px; color: #666;">Click any model name to view details on Hugging Face</p>
                        </div>
                        """
                        )
                
                show_thoughts_state = gr.State(value=False)
                
                def toggle_thoughts_box(current_state):
                    """Toggle visibility of agentic thoughts box"""
                    new_state = not current_state
                    return gr.update(visible=new_state), new_state
                
                show_agentic_thought.click(
                    fn=toggle_thoughts_box,
                    inputs=[show_thoughts_state],
                    outputs=[agentic_thoughts_box, show_thoughts_state]
                )
                
                # GPU-decorated function to load any model (for user selection)
                # @spaces.GPU(max_duration=MAX_DURATION)
                def load_model_with_gpu(model_name):
                    """Load medical model (GPU-decorated for ZeroGPU compatibility)"""
                    try:
                        if not is_model_loaded(model_name):
                            logger.info(f"Loading medical model: {model_name}...")
                            set_model_loading_state(model_name, "loading")
                            try:
                                initialize_medical_model(model_name)
                                logger.info(f"✅ Medical model {model_name} loaded successfully!")
                                return "✅ The model has been loaded successfully", True
                            except Exception as e:
                                logger.error(f"Failed to load medical model {model_name}: {e}")
                                set_model_loading_state(model_name, "error")
                                return f"❌ Error loading model: {str(e)[:100]}", False
                        else:
                            logger.info(f"Medical model {model_name} is already loaded")
                            return "✅ The model has been loaded successfully", True
                    except Exception as e:
                        logger.error(f"Error loading model {model_name}: {e}")
                        return f"❌ Error: {str(e)[:100]}", False
                
                def load_model_and_update_status(model_name):
                    """Load model and update status, return status text and whether model is ready"""
                    try:
                        status_lines = []
                        
                        # Medical model status
                        if is_model_loaded(model_name):
                            status_lines.append(f"✅ MedSwin ({model_name}): loaded and ready")
                        else:
                            state = get_model_loading_state(model_name)
                            if state == "loading":
                                status_lines.append(f"⏳ MedSwin ({model_name}): loading...")
                            elif state == "error":
                                status_lines.append(f"❌ MedSwin ({model_name}): error loading")
                            else:
                                # Use GPU-decorated function to load the model
                                try:
                                    result = load_model_with_gpu(model_name)
                                    if result and isinstance(result, tuple) and len(result) == 2:
                                        status_text, is_ready = result
                                        if is_ready:
                                            status_lines.append(f"✅ MedSwin ({model_name}): loaded and ready")
                                        else:
                                            status_lines.append(f"⏳ MedSwin ({model_name}): loading...")
                                    else:
                                        status_lines.append(f"⏳ MedSwin ({model_name}): loading...")
                                except Exception as e:
                                    logger.error(f"Error calling load_model_with_gpu: {e}")
                                    status_lines.append(f"⏳ MedSwin ({model_name}): loading...")
                        
                        # TTS model status (only show if available or if there's an issue)
                        if TTS_AVAILABLE:
                            if config.global_tts_model is not None:
                                status_lines.append("✅ TTS (maya1): loaded and ready")
                            else:
                                # TTS available but not loaded - optional feature
                                pass  # Don't show if not loaded, it's optional
                        # Don't show TTS status if library not available (it's optional)
                        
                        # ASR (Whisper) model status
                        if WHISPER_AVAILABLE:
                            if config.global_whisper_model is not None:
                                status_lines.append("✅ ASR (Whisper): loaded and ready")
                            else:
                                status_lines.append("⏳ ASR (Whisper): will load on first use")
                        else:
                            status_lines.append("❌ ASR: library not available")
                        
                        status_text = "\n".join(status_lines)
                        is_ready = is_model_loaded(model_name)
                        return status_text, is_ready
                    except Exception as e:
                        return f"❌ Error: {str(e)[:100]}", False
                
                def check_model_status(model_name):
                    """Check current model status without loading"""
                    status_lines = []
                    
                    # Medical model status
                    if is_model_loaded(model_name):
                        status_lines.append(f"✅ MedSwin ({model_name}): loaded and ready")
                    else:
                        state = get_model_loading_state(model_name)
                    if state == "loading":
                        status_lines.append(f"⏳ MedSwin ({model_name}): loading...")
                    elif state == "error":
                            status_lines.append(f"❌ MedSwin ({model_name}): error loading")
                    else:
                        status_lines.append(f"⚠️ MedSwin ({model_name}): not loaded")
                    
                    # TTS model status (only show if available and loaded)
                    if TTS_AVAILABLE:
                        if config.global_tts_model is not None:
                            status_lines.append("✅ TTS (maya1): loaded and ready")
                        # Don't show if TTS library available but model not loaded (optional feature)
                    # Don't show TTS status if library not available (it's optional)
                    
                    # ASR (Whisper) model status
                    if WHISPER_AVAILABLE:
                        if config.global_whisper_model is not None:
                            status_lines.append("✅ ASR (Whisper): loaded and ready")
                        else:
                            status_lines.append("⏳ ASR (Whisper): will load on first use")
                    else:
                        status_lines.append("❌ ASR: library not available")
                    
                    status_text = "\n".join(status_lines)
                    is_ready = is_model_loaded(model_name)
                    return status_text, is_ready
                
                # GPU-decorated function to load ONLY medical model on startup
                # According to ZeroGPU best practices:
                # 1. Load models to CPU in global scope (no GPU decorator needed)
                # 2. Move models to GPU only in inference functions (with @spaces.GPU decorator)
                # However, for large models, loading to CPU then moving to GPU uses more memory
                # So we use a hybrid approach: load to GPU directly but within GPU-decorated function
                
                def load_medical_model_on_startup_cpu():
                    """
                    Load model to CPU on startup (ZeroGPU best practice - no GPU decorator needed)
                    Model will be moved to GPU during first inference
                    """
                    status_messages = []
                    
                    try:
                        # Load only medical model (MedSwin) to CPU - TTS and Whisper load on-demand
                        if not is_model_loaded(DEFAULT_MEDICAL_MODEL):
                            logger.info(f"[STARTUP] Loading medical model to CPU: {DEFAULT_MEDICAL_MODEL}...")
                            set_model_loading_state(DEFAULT_MEDICAL_MODEL, "loading")
                            try:
                                # Load to CPU (no GPU decorator needed)
                                initialize_medical_model(DEFAULT_MEDICAL_MODEL, load_to_gpu=False)
                                # Verify model is actually loaded
                                if is_model_loaded(DEFAULT_MEDICAL_MODEL):
                                    status_messages.append(f"✅ MedSwin ({DEFAULT_MEDICAL_MODEL}): loaded to CPU")
                                    logger.info(f"[STARTUP] ✅ Medical model {DEFAULT_MEDICAL_MODEL} loaded to CPU successfully!")
                                else:
                                    status_messages.append(f"⚠️ MedSwin ({DEFAULT_MEDICAL_MODEL}): loading failed")
                                    logger.warning(f"[STARTUP] Medical model {DEFAULT_MEDICAL_MODEL} initialization completed but not marked as loaded")
                                    set_model_loading_state(DEFAULT_MEDICAL_MODEL, "error")
                            except Exception as e:
                                status_messages.append(f"❌ MedSwin ({DEFAULT_MEDICAL_MODEL}): error - {str(e)[:50]}")
                                logger.error(f"[STARTUP] Failed to load medical model: {e}")
                                import traceback
                                logger.debug(f"[STARTUP] Full traceback: {traceback.format_exc()}")
                                set_model_loading_state(DEFAULT_MEDICAL_MODEL, "error")
                        else:
                            status_messages.append(f"✅ MedSwin ({DEFAULT_MEDICAL_MODEL}): already loaded")
                            logger.info(f"[STARTUP] Medical model {DEFAULT_MEDICAL_MODEL} already loaded")
                        
                        # Add ASR status (will load on first use)
                        if WHISPER_AVAILABLE:
                            status_messages.append("⏳ ASR (Whisper): will load on first use")
                        else:
                            status_messages.append("❌ ASR: library not available")
                        
                        # Return status
                        status_text = "\n".join(status_messages)
                        logger.info(f"[STARTUP] ✅ Model loading complete. Status:\n{status_text}")
                        return status_text
                        
                    except Exception as e:
                        error_msg = str(e)
                        logger.error(f"[STARTUP] Error loading model to CPU: {error_msg}")
                        return f"⚠️ Error loading model: {error_msg[:100]}"
                
                # Alternative: Load directly to GPU (requires GPU decorator)
                # @spaces.GPU(max_duration=MAX_DURATION)
                def load_medical_model_on_startup_gpu():
                    """
                    Load model directly to GPU on startup (alternative approach)
                    Uses GPU quota but model is immediately ready for inference
                    """
                    import torch
                    status_messages = []
                    
                    try:
                        # Clear GPU cache at start
                        if torch.cuda.is_available():
                            torch.cuda.empty_cache()
                            logger.info("[STARTUP] Cleared GPU cache before model loading")
                        
                        # Load only medical model (MedSwin) - TTS and Whisper load on-demand
                        if not is_model_loaded(DEFAULT_MEDICAL_MODEL):
                            logger.info(f"[STARTUP] Loading medical model to GPU: {DEFAULT_MEDICAL_MODEL}...")
                            set_model_loading_state(DEFAULT_MEDICAL_MODEL, "loading")
                            try:
                                # Load directly to GPU (within GPU-decorated function)
                                initialize_medical_model(DEFAULT_MEDICAL_MODEL, load_to_gpu=True)
                                # Verify model is actually loaded
                                if is_model_loaded(DEFAULT_MEDICAL_MODEL):
                                    status_messages.append(f"✅ MedSwin ({DEFAULT_MEDICAL_MODEL}): loaded to GPU")
                                    logger.info(f"[STARTUP] ✅ Medical model {DEFAULT_MEDICAL_MODEL} loaded to GPU successfully!")
                                else:
                                    status_messages.append(f"⚠️ MedSwin ({DEFAULT_MEDICAL_MODEL}): loading failed")
                                    logger.warning(f"[STARTUP] Medical model {DEFAULT_MEDICAL_MODEL} initialization completed but not marked as loaded")
                                    set_model_loading_state(DEFAULT_MEDICAL_MODEL, "error")
                            except Exception as e:
                                status_messages.append(f"❌ MedSwin ({DEFAULT_MEDICAL_MODEL}): error - {str(e)[:50]}")
                                logger.error(f"[STARTUP] Failed to load medical model: {e}")
                                import traceback
                                logger.debug(f"[STARTUP] Full traceback: {traceback.format_exc()}")
                                set_model_loading_state(DEFAULT_MEDICAL_MODEL, "error")
                        else:
                            status_messages.append(f"✅ MedSwin ({DEFAULT_MEDICAL_MODEL}): already loaded")
                            logger.info(f"[STARTUP] Medical model {DEFAULT_MEDICAL_MODEL} already loaded")
                        
                        # Add ASR status (will load on first use)
                        if WHISPER_AVAILABLE:
                            status_messages.append("⏳ ASR (Whisper): will load on first use")
                        else:
                            status_messages.append("❌ ASR: library not available")
                        
                        # Clear cache after loading
                        if torch.cuda.is_available():
                            torch.cuda.empty_cache()
                            logger.debug("[STARTUP] Cleared GPU cache after model loading")
                        
                        # Return status
                        status_text = "\n".join(status_messages)
                        logger.info(f"[STARTUP] ✅ Model loading complete. Status:\n{status_text}")
                        return status_text
                        
                    except Exception as e:
                        error_msg = str(e)
                        # Check if it's a ZeroGPU quota/rate limit error
                        is_quota_error = ("429" in error_msg or "Too Many Requests" in error_msg or 
                            "quota" in error_msg.lower() or "ZeroGPU" in error_msg or
                            "runnning out" in error_msg.lower() or "running out" in error_msg.lower())
                        
                        if is_quota_error:
                            logger.warning(f"[STARTUP] ZeroGPU quota/rate limit error detected: {error_msg[:100]}")
                            # Return status message indicating quota error (will be handled by retry logic)
                            status_messages.append("⚠️ ZeroGPU quota error - will retry")
                            status_text = "\n".join(status_messages)
                            # Also add ASR status
                            if WHISPER_AVAILABLE:
                                status_text += "\n⏳ ASR (Whisper): will load on first use"
                            return status_text  # Return status instead of raising, let wrapper handle retry
                        
                        logger.error(f"[STARTUP] ❌ Error in model loading startup: {e}")
                        import traceback
                        logger.debug(f"[STARTUP] Full traceback: {traceback.format_exc()}")
                        # Clear cache on error
                        if torch.cuda.is_available():
                            torch.cuda.empty_cache()
                        return f"⚠️ Startup loading error: {str(e)[:100]}"
                
                # Initialize status on load
                def init_model_status():
                    try:
                        result = check_model_status(DEFAULT_MEDICAL_MODEL)
                        if result and isinstance(result, tuple) and len(result) == 2:
                            status_text, is_ready = result
                            return status_text
                        else:
                            return "⚠️ Unable to check model status"
                    except Exception as e:
                        logger.error(f"Error in init_model_status: {e}")
                        return f"⚠️ Error: {str(e)[:100]}"
                
                # Update status when model selection changes
                def update_model_status_on_change(model_name):
                    try:
                        result = check_model_status(model_name)
                        if result and isinstance(result, tuple) and len(result) == 2:
                            status_text, is_ready = result
                            return status_text
                        else:
                            return "⚠️ Unable to check model status"
                    except Exception as e:
                        logger.error(f"Error in update_model_status_on_change: {e}")
                        return f"⚠️ Error: {str(e)[:100]}"
                
                # Handle model selection change
                def on_model_change(model_name):
                    try:
                        result = load_model_and_update_status(model_name)
                        if result and isinstance(result, tuple) and len(result) == 2:
                            status_text, is_ready = result
                            submit_enabled = is_ready
                            return (
                                status_text,
                                gr.update(interactive=submit_enabled),
                                gr.update(interactive=submit_enabled)
                            )
                        else:
                            error_msg = "⚠️ Unable to load model status"
                            return (
                                error_msg,
                                gr.update(interactive=False),
                                gr.update(interactive=False)
                            )
                    except Exception as e:
                        logger.error(f"Error in on_model_change: {e}")
                        error_msg = f"⚠️ Error: {str(e)[:100]}"
                        return (
                            error_msg,
                            gr.update(interactive=False),
                            gr.update(interactive=False)
                        )
                
                # Update status display periodically or on model status changes
                def refresh_model_status(model_name):
                    return update_model_status_on_change(model_name)
                
                medical_model.change(
                    fn=on_model_change,
                    inputs=[medical_model],
                    outputs=[model_status, submit_button, message_input]
                )
                
                # GPU-decorated function to load Whisper ASR model on-demand
                # @spaces.GPU(max_duration=MAX_DURATION)
                def load_whisper_model_on_demand():
                    """Load Whisper ASR model when needed"""
                    try:
                        if WHISPER_AVAILABLE and config.global_whisper_model is None:
                            logger.info("[ASR] Loading Whisper model on-demand...")
                            initialize_whisper_model()
                            if config.global_whisper_model is not None:
                                logger.info("[ASR] ✅ Whisper model loaded successfully!")
                                return "✅ ASR (Whisper): loaded"
                            else:
                                logger.warning("[ASR] ⚠️ Whisper model failed to load")
                                return "⚠️ ASR (Whisper): failed to load"
                        elif config.global_whisper_model is not None:
                            return "✅ ASR (Whisper): already loaded"
                        else:
                            return "❌ ASR: library not available"
                    except Exception as e:
                        logger.error(f"[ASR] Error loading Whisper model: {e}")
                        return f"❌ ASR: error - {str(e)[:100]}"
                
                # Load medical model on startup and update status
                # Use a wrapper to handle GPU context properly with retry logic
                def load_startup_and_update_ui():
                    """
                    Load model on startup with retry logic (max 3 attempts) and return status with UI updates
                    
                    Uses CPU-first approach (ZeroGPU best practice):
                    - Load model to CPU (no GPU decorator needed, avoids quota issues)
                    - Model will be moved to GPU during first inference
                    """
                    import time
                    max_retries = 3
                    base_delay = 5.0  # Start with 5 seconds delay
                    
                    for attempt in range(1, max_retries + 1):
                        try:
                            logger.info(f"[STARTUP] Attempt {attempt}/{max_retries} to load medical model to CPU...")
                            # Use CPU-first approach (no GPU decorator, avoids quota issues)
                            status_text = load_medical_model_on_startup_cpu()
                            # Check if model is ready and update submit button state
                            is_ready = is_model_loaded(DEFAULT_MEDICAL_MODEL)
                            if is_ready:
                                logger.info(f"[STARTUP] ✅ Model loaded successfully on attempt {attempt}")
                                return status_text, gr.update(interactive=is_ready), gr.update(interactive=is_ready)
                            else:
                                # Check if status text indicates quota error
                                if status_text and ("quota" in status_text.lower() or "ZeroGPU" in status_text or 
                                                   "429" in status_text or "runnning out" in status_text.lower() or 
                                                   "running out" in status_text.lower()):
                                    if attempt < max_retries:
                                        delay = base_delay * attempt
                                        logger.warning(f"[STARTUP] Quota error detected in status, retrying in {delay} seconds...")
                                        time.sleep(delay)
                                        continue
                                    else:
                                        # Quota exhausted after retries - allow user to proceed, model will load on-demand
                                        status_msg = "⚠️ ZeroGPU quota exhausted.\n⏳ Model will load automatically when you send a message.\n💡 You can also select a model from the dropdown."
                                        logger.info("[STARTUP] Quota exhausted after retries - allowing user to proceed with on-demand loading")
                                        return status_msg, gr.update(interactive=True), gr.update(interactive=True)
                                # Model didn't load, but no exception - might be a state issue
                                logger.warning(f"[STARTUP] Model not ready after attempt {attempt}, but no error")
                                if attempt < max_retries:
                                    delay = base_delay * attempt  # Exponential backoff: 5s, 10s, 15s
                                    logger.info(f"[STARTUP] Retrying in {delay} seconds...")
                                    time.sleep(delay)
                                    continue
                                else:
                                    # Even if model didn't load, allow user to try selecting another model
                                    return status_text + "\n⚠️ Model not loaded. Please select a model from dropdown.", gr.update(interactive=True), gr.update(interactive=True)
                        except Exception as e:
                            error_msg = str(e)
                            is_quota_error = ("429" in error_msg or "Too Many Requests" in error_msg or 
                                            "quota" in error_msg.lower() or "ZeroGPU" in error_msg or
                                            "runnning out" in error_msg.lower() or "running out" in error_msg.lower())
                            
                            if is_quota_error and attempt < max_retries:
                                delay = base_delay * attempt  # Exponential backoff: 5s, 10s, 15s
                                logger.warning(f"[STARTUP] ZeroGPU rate limit/quota error on attempt {attempt}/{max_retries}")
                                logger.info(f"[STARTUP] Retrying in {delay} seconds...")
                                time.sleep(delay)
                                continue
                            else:
                                logger.error(f"[STARTUP] Error in load_startup_and_update_ui (attempt {attempt}/{max_retries}): {e}")
                                import traceback
                                logger.debug(f"[STARTUP] Full traceback: {traceback.format_exc()}")
                                
                                if is_quota_error:
                                    # If quota exhausted, allow user to proceed - model will load on-demand
                                    error_display = "⚠️ ZeroGPU quota exhausted.\n⏳ Model will load automatically when you send a message.\n💡 You can also select a model from the dropdown."
                                    logger.info("[STARTUP] Quota exhausted - allowing user to proceed with on-demand loading")
                                    return error_display, gr.update(interactive=True), gr.update(interactive=True)
                                else:
                                    error_display = f"⚠️ Startup error: {str(e)[:100]}"
                                    if attempt >= max_retries:
                                        logger.error(f"[STARTUP] Failed after {max_retries} attempts")
                                        return error_display, gr.update(interactive=False), gr.update(interactive=False)
                    
                    # Should not reach here, but just in case
                    return "⚠️ Startup failed after retries. Please select a model from dropdown.", gr.update(interactive=True), gr.update(interactive=True)
                
                demo.load(
                    fn=load_startup_and_update_ui,
                    inputs=None,
                    outputs=[model_status, submit_button, message_input]
                )
                
                # Note: We removed the preload on focus functionality because:
                # 1. Model loading requires GPU access (device_map="auto" needs GPU in ZeroGPU)
                # 2. The startup function already loads the model with GPU decorator
                # 3. Preloading without GPU decorator would fail or cause conflicts
                # 4. If startup fails, user can select a model from dropdown to trigger loading
                
                # Wrap stream_chat - ensure model is loaded before starting (don't load inside stream_chat to save time)
                def stream_chat_with_model_check(
                    message, history, system_prompt, temperature, max_new_tokens,
                    top_p, top_k, penalty, retriever_k, merge_threshold,
                    use_rag, medical_model_name, use_web_search,
                    enable_clinical_intake, disable_agentic_reasoning, show_thoughts, request: gr.Request = None
                ):
                    # Check if model is loaded - if not, show error (don't load here to save stream_chat time)
                    model_loaded = is_model_loaded(medical_model_name)
                    if not model_loaded:
                        loading_state = get_model_loading_state(medical_model_name)
                        # Debug logging to understand why model check fails
                        logger.debug(f"[STREAM_CHAT] Model check: name={medical_model_name}, loaded={model_loaded}, state={loading_state}, in_dict={medical_model_name in config.global_medical_models}, model_exists={config.global_medical_models.get(medical_model_name) is not None if medical_model_name in config.global_medical_models else False}")
                        
                        if loading_state == "loading":
                            error_msg = f"⏳ {medical_model_name} is still loading. Please wait until the model status shows 'loaded and ready' before sending messages."
                        else:
                            error_msg = f"⚠️ {medical_model_name} is not loaded. Please wait a moment for the model to finish loading, or select a model from the dropdown to load it."
                        updated_history = history + [{"role": "assistant", "content": error_msg}]
                        yield updated_history, ""
                        return
                    
                    # If request is None, create a mock request for compatibility
                        if request is None:
                            class MockRequest:
                                session_hash = "anonymous"
                            request = MockRequest()
                        
                    # Model is loaded, proceed with stream_chat (no model loading here to save time)
                    try:
                        for result in stream_chat(
                            message, history, system_prompt, temperature, max_new_tokens,
                            top_p, top_k, penalty, retriever_k, merge_threshold,
                            use_rag, medical_model_name, use_web_search,
                            enable_clinical_intake, disable_agentic_reasoning, show_thoughts, request
                        ):
                            yield result
                    except Exception as e:
                        # Handle any errors gracefully
                        logger.error(f"Error in stream_chat_with_model_check: {e}")
                        import traceback
                        logger.debug(f"Full traceback: {traceback.format_exc()}")
                        error_msg = f"⚠️ An error occurred: {str(e)[:200]}"
                        updated_history = history + [{"role": "assistant", "content": error_msg}]
                        yield updated_history, ""
                
                submit_button.click(
                    fn=stream_chat_with_model_check,
                    inputs=[
                        message_input,
                        chatbot,
                        system_prompt,
                        temperature,
                        max_new_tokens,
                        top_p,
                        top_k,
                        penalty,
                        retriever_k,
                        merge_threshold,
                        use_rag,
                        medical_model,
                        use_web_search,
                        enable_clinical_intake,
                        disable_agentic_reasoning,
                        show_thoughts_state
                    ],
                    outputs=[chatbot, agentic_thoughts_box]
                )
                
                message_input.submit(
                    fn=stream_chat_with_model_check,
                    inputs=[
                        message_input,
                        chatbot,
                        system_prompt,
                        temperature,
                        max_new_tokens,
                        top_p,
                        top_k,
                        penalty,
                        retriever_k,
                        merge_threshold,
                        use_rag,
                        medical_model,
                        use_web_search,
                        enable_clinical_intake,
                        disable_agentic_reasoning,
                        show_thoughts_state
                    ],
                    outputs=[chatbot, agentic_thoughts_box]
                )
    
    return demo