import torch
import soundfile as sf
import os
import re
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from speechbrain.pretrained import EncoderClassifier

# Define paths and device
model_path = "HAMMALE/speecht5-darija"  # Path to your model on HF Hub
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

# Load models
processor = SpeechT5Processor.from_pretrained(model_path)
model = SpeechT5ForTextToSpeech.from_pretrained(model_path).to(device)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)

# Load speaker embedding model
speaker_model = EncoderClassifier.from_hparams(
    source="speechbrain/spkrec-xvect-voxceleb",
    run_opts={"device": device},
    savedir=os.path.join("/tmp", "spkrec-xvect-voxceleb"),
)

# Load pre-computed speaker embeddings
male_embedding = torch.load("male_embedding.pt") if os.path.exists("male_embedding.pt") else torch.randn(1, 512)
female_embedding = torch.load("female_embedding.pt") if os.path.exists("female_embedding.pt") else torch.randn(1, 512)

# Text normalization function
def normalize_text(text):
    """Normalize text for TTS processing"""
    text = text.lower()
    # Keep letters, numbers, spaces and apostrophes - fixed regex
    text = re.sub(r'[^\w\s\'\u0600-\u06FF]', '', text)
    text = ' '.join(text.split())
    return text

# Function to synthesize speech
def synthesize_speech(text, voice_type="male", speed=1.0):
    """Generate speech from text using the specified voice type"""
    try:
        # Select speaker embedding based on voice type
        if voice_type == "male":
            speaker_embeddings = male_embedding.to(device)
        else:
            speaker_embeddings = female_embedding.to(device)

        # Normalize and tokenize input text
        normalized_text = normalize_text(text)
        inputs = processor(text=normalized_text, return_tensors="pt").to(device)

        # Generate speech
        with torch.no_grad():
            speech = model.generate_speech(
                inputs["input_ids"],
                speaker_embeddings,
                vocoder=vocoder
            )

        # Convert to numpy array and adjust speed if needed
        speech_np = speech.cpu().numpy()

        # Apply speed adjustment (simple resampling)
        if speed != 1.0:
            # This is a simple approach - for production use a proper resampling library
            import numpy as np
            from scipy import signal
            sample_rate = 16000
            new_length = int(len(speech_np) / speed)
            speech_np = signal.resample(speech_np, new_length)

        # Save temporary audio file
        output_file = "output_speech.wav"
        sf.write(output_file, speech_np, 16000)

        return output_file, None

    except Exception as e:
        return None, f"Error generating speech: {str(e)}"

# Gradio imports need to be added
import gradio as gr

# Custom CSS for a full-screen, modern design
custom_css = """
body, html {
    margin: 0;
    padding: 0;
    height: 100%;
    width: 100%;
    overflow-x: hidden;
}

.gradio-container {
    font-family: 'Montserrat', 'Arial', sans-serif !important;
    height: 100vh;
    width: 100vw;
    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
    display: flex;
    flex-direction: column;
    padding: 0;
    margin: 0;
    overflow-y: auto;
}

.main-header {
    background: linear-gradient(90deg, #d32f2f, #1976d2);
    color: white;
    padding: 2em;
    text-align: center;
    box-shadow: 0 6px 12px rgba(0, 0, 0, 0.15);
    border-bottom: 4px solid #ffffff33;
}

.main-header h1 {
    font-size: 2.8em;
    margin: 0;
    font-weight: 700;
    letter-spacing: 1px;
    text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);
}

.main-header p {
    font-size: 1.2em;
    margin: 0.5em 0 0;
    opacity: 0.9;
    font-weight: 300;
}

.container {
    max-width: 1200px;
    margin: 2em auto;
    padding: 0 1em;
    flex: 1;
}

.row {
    display: flex;
    gap: 2em;
    background: white;
    border-radius: 15px;
    padding: 2em;
    box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
    margin-bottom: 2em;
}

.column {
    flex: 1;
    padding: 1em;
}

.info-box {
    background: #fef6f6;
    border-left: 5px solid #d32f2f;
    padding: 1.5em;
    border-radius: 8px;
    margin-bottom: 1.5em;
    font-size: 1em;
    line-height: 1.6;
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
}

.textbox textarea {
    border: 2px solid #e0e0e0 !important;
    border-radius: 10px !important;
    padding: 1em !important;
    font-size: 1.1em !important;
    transition: border-color 0.3s ease !important;
}

.textbox textarea:focus {
    border-color: #d32f2f !important;
    box-shadow: 0 0 8px rgba(211, 47, 47, 0.2) !important;
}

.radio {
    display: flex;
    justify-content: center;
    gap: 1.5em;
    margin: 1em 0;
}

.radio label {
    background: #f5f5f5;
    padding: 0.8em 1.5em;
    border-radius: 25px;
    border: 2px solid #e0e0e0;
    cursor: pointer;
    transition: all 0.3s ease;
}

.radio input:checked + label {
    background: #d32f2f;
    color: white;
    border-color: #d32f2f;
    box-shadow: 0 4px 8px rgba(211, 47, 47, 0.2);
}

.slider {
    margin: 1.5em 0;
}

.slider input {
    accent-color: #d32f2f !important;
}

.button {
    background: linear-gradient(90deg, #d32f2f, #1976d2) !important;
    color: white !important;
    padding: 1em 2em !important;
    border-radius: 25px !important;
    border: none !important;
    font-size: 1.1em !important;
    font-weight: 600 !important;
    transition: transform 0.2s ease, box-shadow 0.3s ease !important;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15) !important;
}

.button:hover {
    transform: translateY(-2px) !important;
    box-shadow: 0 6px 12px rgba(0, 0, 0, 0.25) !important;
}

.audio {
    margin-top: 1em;
}

.audio audio {
    width: 100%;
    border-radius: 10px;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}

.example-header {
    font-weight: 600;
    color: #d32f2f;
    margin: 1.5em 0 0.5em;
    font-size: 1.2em;
}

ul {
    padding-left: 1.5em;
    color: #333;
}

li {
    margin: 0.5em 0;
    font-size: 1em;
}

.examples {
    margin-top: 1.5em;
    padding: 1em;
    background: #f9f9f9;
    border-radius: 10px;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.05);
}

footer {
    text-align: center;
    padding: 1.5em;
    background: #ffffff;
    color: #666;
    font-size: 0.95em;
    border-top: 1px solid #e0e0e0;
    margin-top: auto;
}

.flag-icon {
    width: 30px;
    height: 30px;
    vertical-align: middle;
    margin-right: 10px;
}
"""

# Create Gradio interface with enhanced design
with gr.Blocks(css=custom_css) as demo:
    gr.HTML(
        """
        <div class="main-header">
            <h1>🇲🇦 Moroccan Darija Text-to-Speech 🎙️</h1>
            <p>Transform your Darija text into lifelike speech with ease</p>
        </div>
        """
    )
    
    with gr.Row(elem_classes="row"):
        with gr.Column(elem_classes="column"):
            gr.HTML(
                """
                <div class="info-box">
                    <p>Experience high-quality Darija speech synthesis powered by the SpeechT5 model, fine-tuned on the DODa audio dataset. Customize the voice and speed to suit your needs.</p>
                </div>
                """
            )
            
            text_input = gr.Textbox(
                label="Enter Darija Text",
                placeholder="Kteb chi jomla b darija hna, bhal 'Salam, kifach nta?'...",
                lines=3,
                elem_classes="textbox"
            )
            
            with gr.Row(elem_classes="radio"):
                voice_type = gr.Radio(
                    ["male", "female"],
                    label="Voice Type",
                    value="male"
                )
                
            speed = gr.Slider(
                minimum=0.5,
                maximum=2.0,
                value=1.0,
                step=0.1,
                label="Speech Speed",
                elem_classes="slider"
            )
            
            generate_btn = gr.Button("Generate Speech", variant="primary", elem_classes="button")
            
            gr.HTML(
                """
                <div class="example-header">Try These Phrases:</div>
                <ul>
                    <li>"Ana Nadi Bezzaaf hhh"</li>
                    <li>"Lyoum ajwaa zwina bezzaaf."</li>
                    <li>"Lmaghrib ahssan blad fi l3alam"</li>
                    <li>"Chukran bzzaf 3la lmosanada!"</li>
                </ul>
                """
            )
            
        with gr.Column(elem_classes="column"):
            audio_output = gr.Audio(label="Generated Speech", elem_classes="audio")
            error_output = gr.Textbox(label="Error (if any)", visible=False)
            
            gr.Examples(
                examples=[
                    ["Ana Nadi Bezzaaf hhh", "male", 1.0],
                    ["Lyoum ajwaa zwina bezzaaf.", "female", 1.0],
                    ["Lmaghrib ahssan blad fi l3alam", "male", 1.0],
                    ["Filistine horaa mina lbari ila lbarri", "female", 0.8],
                ],
                inputs=[text_input, voice_type, speed],
                outputs=[audio_output, error_output],
                fn=synthesize_speech
            )
    
    gr.HTML(
        """
        <footer>
            <p>Developed  by HAMMALE | Data: DODa Audio Dataset</p>
        </footer>
        """
    )
    
    # Set button click action
    generate_btn.click(
        fn=synthesize_speech,
        inputs=[text_input, voice_type, speed],
        outputs=[audio_output, error_output]
    )

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