app.py

import os

# Disable problematic optimizations for ZeroGPU compatibility
os.environ["TORCHDYNAMO_DISABLE"] = "1"
os.environ["TORCH_COMPILE_DISABLE"] = "1"
os.environ["PYTORCH_DISABLE_CUDNN_BENCHMARK"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Check if we're in ZeroGPU or similar restricted environment
def is_restricted_environment():
    return (
        os.getenv("ZERO_GPU") or 
        "zero" in str(os.getenv("SPACE_ID", "")).lower() or
        os.getenv("SPACES_ZERO_GPU") or
        "spaces" in str(os.getenv("HOSTNAME", "")).lower()
    )

# Disable Unsloth optimizations in restricted environments
if is_restricted_environment():
    os.environ["UNSLOTH_DISABLE"] = "1"
    os.environ["DISABLE_UNSLOTH"] = "1" 
    os.environ["UNSLOTH_IGNORE_ERRORS"] = "1"
    os.environ["UNSLOTH_NO_COMPILE"] = "1"
    print("🚀 ZeroGPU detected - Unsloth optimizations disabled for compatibility")
else:
    print("🔧 Local environment detected - Unsloth optimizations enabled")

# Check if we're in ZeroGPU or similar restricted environment
def is_restricted_environment():
    return (
        os.getenv("ZERO_GPU") or 
        "zero" in str(os.getenv("SPACE_ID", "")).lower() or
        os.getenv("SPACES_ZERO_GPU")
    )

# Disable Unsloth optimizations in restricted environments
if is_restricted_environment():
    os.environ["UNSLOTH_DISABLE"] = "1"
    os.environ["DISABLE_UNSLOTH"] = "1"
    os.environ["UNSLOTH_IGNORE_ERRORS"] = "1"
    print("🚀 ZeroGPU detected - Unsloth optimizations disabled for compatibility")

import torch
import gradio as gr
import numpy as np
import spaces
import logging
from huggingface_hub import login
import threading
import time

torch._dynamo.config.disable = True
torch._dynamo.config.suppress_errors = True

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

hf_token = os.getenv("HF_TOKEN")
if hf_token:
    login(token=hf_token)

# Global variables for model caching
_tts_model = None
_speakers_dict = None
_model_initialized = False
_initialization_in_progress = False

def get_speakers_dict():
    """Get speakers dictionary using the correct SDK structure"""
    try:
        # Import the Speakers class (not individual speakers)
        from maliba_ai.config.settings import Speakers
        
        # Access all 10 speakers through the Speakers class
        speakers_dict = {
            "Adama": Speakers.Adama,
            "Moussa": Speakers.Moussa,
            "Bourama": Speakers.Bourama,
            "Modibo": Speakers.Modibo,
            "Seydou": Speakers.Seydou,
            "Amadou": Speakers.Amadou,
            "Bakary": Speakers.Bakary,
            "Ngolo": Speakers.Ngolo,
            "Amara": Speakers.Amara,
            "Ibrahima": Speakers.Ibrahima
        }
        
        logger.info(f"🎤 Successfully loaded {len(speakers_dict)} speakers: {list(speakers_dict.keys())}")
        return speakers_dict
        
    except Exception as e:
        logger.error(f"❌ Failed to import Speakers class: {e}")
        return {}

@spaces.GPU()
def initialize_model_once():
    """Initialize model exactly like your old working version"""
    global _tts_model, _speakers_dict, _model_initialized, _initialization_in_progress
    
    if _model_initialized:
        logger.info("Model already initialized, returning existing instance")
        return _tts_model, _speakers_dict
    
    if _initialization_in_progress:
        logger.info("Initialization already in progress, waiting...")
        for _ in range(50): 
            time.sleep(0.1)
            if _model_initialized:
                return _tts_model, _speakers_dict
    
    _initialization_in_progress = True
    
    try:
        logger.info("Initializing Bambara TTS model...")
        start_time = time.time()
        
        # Use the correct import path
        from maliba_ai.tts.inference import BambaraTTSInference
        
        model = BambaraTTSInference()
        speakers = get_speakers_dict()
        
        if not speakers:
            raise ValueError("Failed to load speakers dictionary")
        
        _tts_model = model
        _speakers_dict = speakers
        _model_initialized = True
        
        elapsed = time.time() - start_time
        logger.info(f"Model initialized successfully in {elapsed:.2f} seconds!")
        
        return _tts_model, _speakers_dict
        
    except Exception as e:
        logger.error(f"Failed to initialize model: {e}")
        _initialization_in_progress = False
        raise e
    finally:
        _initialization_in_progress = False

def validate_inputs(text, temperature, top_k, top_p, max_tokens):
    """Same validation as your old version"""
    if not text or not text.strip():
        return False, "Please enter some Bambara text."
    
    if not (0.001 <= temperature <= 2.0):
        return False, "Temperature must be between 0.001 and 2.0"
    
    if not (1 <= top_k <= 100):
        return False, "Top-K must be between 1 and 100"
    
    if not (0.1 <= top_p <= 1.0):
        return False, "Top-P must be between 0.1 and 1.0"
    
    return True, ""

@spaces.GPU()
def generate_speech(text, speaker_name, use_advanced, temperature, top_k, top_p, max_tokens):
    """Generate speech - exactly like your old working version"""
    if not text.strip():
        return None, "Please enter some Bambara text."
    
    try:
        tts, speakers = initialize_model_once()
        
        if not tts or not speakers:
            return None, "❌ Model not properly initialized"
        
        if speaker_name not in speakers:
            available_speakers = list(speakers.keys())
            return None, f"❌ Speaker '{speaker_name}' not found. Available: {available_speakers}"
        
        speaker = speakers[speaker_name]
        logger.info(f"Using speaker: {speaker_name}")
        
        if use_advanced:
            is_valid, error_msg = validate_inputs(text, temperature, top_k, top_p, max_tokens)
            if not is_valid:
                return None, f"❌ {error_msg}"
            
            waveform = tts.generate_speech(
                text=text.strip(),
                speaker_id=speaker,
                temperature=temperature,
                top_k=int(top_k),
                top_p=top_p,
                max_new_audio_tokens=int(max_tokens)
            )
        else:
            waveform = tts.generate_speech(
                text=text.strip(),
                speaker_id=speaker
            )
        
        if waveform is None or waveform.size == 0:
            return None, "Failed to generate audio. Please try again."
        
        # Convert to numpy if it's a tensor
        if isinstance(waveform, torch.Tensor):
            waveform = waveform.cpu().numpy()
        
        # Ensure proper audio format (convert float32 to int16 range but keep as float for Gradio)
        if waveform.dtype == np.float32:
            # Normalize to [-1, 1] range if needed
            if np.max(np.abs(waveform)) > 1.0:
                waveform = waveform / np.max(np.abs(waveform))
            # Keep as float32 but ensure proper range for Gradio
            waveform = np.clip(waveform, -1.0, 1.0)
        
        sample_rate = 16000
        return (sample_rate, waveform), f"✅ Audio generated successfully for speaker {speaker_name}"
        
    except Exception as e:
        logger.error(f"Speech generation failed: {e}")
        return None, f"❌ Error: {str(e)}"

# Use available speakers (detect what's actually available, prioritize Bourama)
def get_speaker_names():
    speakers = get_speakers_dict()
    if speakers:
        speaker_list = list(speakers.keys())
        # Reorder to match preferred order (Bourama first)
        preferred_order = ["Bourama", "Adama", "Moussa", "Modibo", "Seydou", 
                          "Amadou", "Bakary", "Ngolo", "Ibrahima", "Amara"]
        
        # Sort available speakers according to preferred order
        ordered_speakers = []
        for speaker in preferred_order:
            if speaker in speaker_list:
                ordered_speakers.append(speaker)
        
        # Add any remaining speakers not in preferred list
        for speaker in speaker_list:
            if speaker not in ordered_speakers:
                ordered_speakers.append(speaker)
                
        logger.info(f"Available speakers: {ordered_speakers}")
        return ordered_speakers
    else:
        # Final fallback with Bourama first
        logger.warning("No speakers loaded, using fallback list")
        return ["Bourama", "Adama", "Moussa", "Modibo", "Seydou"]

SPEAKER_NAMES = get_speaker_names()

# Examples representing ALL 10 speakers - with fallbacks for missing speakers
examples = [
    ["Aw ni ce", "Adama"],  # Natural conversational greeting
    ["Mali bɛna diya kɔsɛbɛ, ka a da a kan baara bɛ ka kɛ.", "Moussa"],  # Clear pronunciation for informative content
    ["Ne bɛ se ka sɛbɛnni yɛlɛma ka kɛ kuma ye", "Bourama"],  # Most stable for educational content
    ["I ka kɛnɛ wa?", "Modibo"],  # Expressive delivery for questions
    ["Lakɔli karamɔgɔw tun tɛ ka se ka sɛbɛnni kɛ ka ɲɛ walanda kan wa denmisɛnw tun tɛ ka se ka o sɛbɛnni ninnu ye, kuma tɛ ka u kalan. Denmisɛnw kɛra kunfinw ye.", "Adama"],  # Natural conversational tone for longer explanation
    ["sigikafɔ kɔnɔ jamanaw ni ɲɔgɔn cɛ, olu ye a haminankow ye, wa o ko ninnu ka kan ka kɛ sariya ani tilennenya kɔnɔ.", "Seydou"],  # Balanced characteristics for formal content
    ["Aw ni ce. Ne tɔgɔ ye Adama. Awɔ, ne ye maliden de ye. Aw Sanbɛ Sanbɛ. San min tɛ ɲinan ye, an bɛɛ ka jɛ ka o seli ɲɔgɔn fɛ, hɛɛrɛ ni lafiya la. Ala ka Mali suma. Ala ka Mali yiriwa. Ala ka Mali taa ɲɛ. Ala ka an ka seliw caya. Ala ka yafa an bɛɛ ma.", "Moussa"],  # Clear pronunciation for heartfelt long message
    ["An dɔlakelen bɛ masike bilenman don ka tɔw gɛn.", "Bourama"],  # Most stable for complex statement
    ["Aw ni ce. Seidu bɛ aw fo wa aw ka yafa a ma, ka da a kan tuma dɔw la kow ka can.", "Modibo"],  # Expressive delivery for personal greeting
    ["To tɔ nantan ni lafiya, o ka fisa ni so fa dumuniba kɛlɛma ye.", "Amadou"],  # Warm and friendly voice for wisdom saying
    ["Mali ye jamana ɲuman ye!", "Bakary"],  # Deep, authoritative tone for patriotic statement
    ["An ka ɲɔgɔn dɛmɛ ka baara kɛ ɲɔgɔn fɛ", "Ngolo"],  # Youthful and energetic for collaboration
    ["Hakili to yɔrɔ min na, sabali bɛ yen", "Ibrahima"],  # Calm and measured for philosophical thought
    ["Dɔnko ɲuman ye, a bɛ dɔn mɔgɔ kɔnɔ", "Amara"],  # Melodic and smooth for poetic expression
]

def get_safe_examples():
    """Get examples with speaker fallbacks for missing speakers"""
    safe_examples = []
    
    # Fallback mapping for missing speakers
    fallback_speakers = {
        "Amadou": "Adama",    # Warm -> Natural conversational
        "Bakary": "Modibo",   # Authoritative -> Expressive
        "Ngolo": "Adama",     # Youthful -> Natural conversational
        "Ibrahima": "Seydou", # Calm -> Balanced
        "Amara": "Moussa"     # Melodic -> Clear pronunciation
    }
    
    for text, speaker in examples:
        # Use original speaker if available, otherwise use fallback
        if speaker in SPEAKER_NAMES:
            safe_examples.append([text, speaker])
        elif speaker in fallback_speakers and fallback_speakers[speaker] in SPEAKER_NAMES:
            safe_examples.append([text, fallback_speakers[speaker]])
        else:
            # Final fallback to first available speaker
            safe_examples.append([text, SPEAKER_NAMES[0]])
    
    return safe_examples

def build_interface():
    """Build the Gradio interface - simplified like your old working version"""
    
    with gr.Blocks(title="Bambara TTS - MALIBA-AI") as demo:
        gr.Markdown("""
        # 🎤 Bambara Text-to-Speech
        
        **Powered by MALIBA-AI** | *First Open-Source Bambara TTS*
        
        Convert Bambara text to natural-sounding speech using our state-of-the-art neural TTS system.
        
        **Bambara** is spoken by millions of people in Mali and West Africa.
        """)

        with gr.Row():
            with gr.Column(scale=2):
                text_input = gr.Textbox(
                    label="📝 Bambara Text",
                    placeholder="Type your Bambara text here...",
                    lines=3,
                    max_lines=10,
                    value="I ni ce"
                )
                
                speaker_dropdown = gr.Dropdown(
                    choices=SPEAKER_NAMES,
                    value="Bourama" if "Bourama" in SPEAKER_NAMES else SPEAKER_NAMES[0],
                    label="🗣️ Speaker Voice",
                    info=f"Choose from {len(SPEAKER_NAMES)} authentic Bambara voices"
                )
                
                generate_btn = gr.Button("🎵 Generate Speech", variant="primary", size="lg")
                
            with gr.Column(scale=1):
                use_advanced = gr.Checkbox(
                    label="⚙️ Use Advanced Settings", 
                    value=False,
                    info="Enable to customize generation parameters"
                )
                
                with gr.Group(visible=False) as advanced_group:
                    gr.Markdown("**Advanced Parameters:**")
                    
                    temperature = gr.Slider(
                        minimum=0.1, 
                        maximum=2.0, 
                        value=0.8, 
                        step=0.1,
                        label="Temperature",
                        info="Higher = more varied"
                    )
                    
                    top_k = gr.Slider(
                        minimum=1, 
                        maximum=100, 
                        value=50, 
                        step=5,
                        label="Top-K"
                    )
                    
                    top_p = gr.Slider(
                        minimum=0.1, 
                        maximum=1.0, 
                        value=0.9, 
                        step=0.05,
                        label="Top-P"
                    )
                    
                    max_tokens = gr.Slider(
                        minimum=256, 
                        maximum=4096, 
                        value=2048, 
                        step=256,
                        label="Max Length"
                    )
        
        gr.Markdown("### 🔊 Generated Audio")
        
        audio_output = gr.Audio(
            label="Generated Speech",
            type="numpy",
            interactive=False,
            format="wav"
        )
            
        status_output = gr.Textbox(
            label="Status",
            interactive=False,
            show_label=False,
            container=False
        )
        
        with gr.Accordion("Try These Examples", open=True):
            def load_example(text, speaker):
                return text, speaker, False, 0.8, 50, 0.9, 2048
            
            gr.Markdown("**Click any example below:**")
            
            # Use safe examples with fallbacks for missing speakers
            safe_examples = get_safe_examples()
            
            for i, (text, speaker) in enumerate(safe_examples):
                btn = gr.Button(f"{text[:30]}{'...' if len(text) > 30 else ''}", size="sm")
                btn.click(
                    fn=lambda t=text, s=speaker: load_example(t, s),
                    outputs=[text_input, speaker_dropdown, use_advanced, temperature, top_k, top_p, max_tokens]
                )
        
        with gr.Accordion("About", open=False):
            gr.Markdown(f"""
            ## About MALIBA-AI Bambara TTS
            
            - **🎯 Purpose**: First open-source Text-to-Speech system for Bambara language
            - **🗣️ Speakers**: {len(SPEAKER_NAMES)} authentic Bambara voices
            - **🔊 Quality**: 16kHz neural speech synthesis
            - **⚡ Performance**: Model loads once and stays in memory
            - **📱 Usage**: Educational, accessibility, and cultural preservation
            
            ### 🎭 Available Speakers:
            {', '.join(SPEAKER_NAMES)}
            
            **License**: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)
            
            ---
            
            **MALIBA-AI Mission**: Ensuring no Malian is left behind by technological advances 🇲🇱
            """)
        
        def toggle_advanced(use_adv):
            return gr.Group(visible=use_adv)
        
        use_advanced.change(
            fn=toggle_advanced,
            inputs=[use_advanced],
            outputs=[advanced_group]
        )
        
        generate_btn.click(
            fn=generate_speech,
            inputs=[text_input, speaker_dropdown, use_advanced, temperature, top_k, top_p, max_tokens],
            outputs=[audio_output, status_output],
            show_progress=True
        )
        
        text_input.submit(
            fn=generate_speech,
            inputs=[text_input, speaker_dropdown, use_advanced, temperature, top_k, top_p, max_tokens],
            outputs=[audio_output, status_output],
            show_progress=True
        )
    
    return demo

def main():
    """Main function to launch the Gradio interface"""
    logger.info("Starting Bambara TTS Gradio interface.")

    interface = build_interface()
    interface.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )
    
    logger.info("Gradio interface launched successfully.")

if __name__ == "__main__":
    main()