import gradio as gr
import torch
import torchaudio # Shayad zaroorat na pare, agar transformers khud handle kar le.
from transformers import AutoProcessor, AutoModelForTextToSpeech # Ye hai asal naya import
import os
import tempfile
import soundfile as sf # Generated audio ko save karne ke liye

# --- OpenVoiceV2 Model aur Processor ko Load karna ---
# Yahan aapko asal OpenVoiceV2 model ka ID dalna hoga jo Hugging Face Hub par available ho.
# EXAMPLE: "myshell-ai/OpenVoiceV2-base" (Yeh ek misali ID hai, asal ID confirm karni hogi)
# Aapko Hugging Face Hub par 'OpenVoiceV2' search karke sahi model ID dhoondhna hoga.
# Agar official OpenVoiceV2 model Transformers mein abhi tak available nahi hai,
# to phir yeh tareeqa kaam nahi karega, aur humein koi aur hal dekhna hoga.

# NOTE: Main yahan ek placeholder model (Bark) istemal kar raha hoon,
# aapko isay OpenVoiceV2 ke asal model ID se badalna hoga.
# Agar OpenVoiceV2 seedha AutoModelForTextToSpeech se load nahi hota,
# to hamein uski specific library ke tareeqe par jana hoga, lekin pehle yeh try karein.
try:
    # Model ID ko OpenVoiceV2 ke asal ID se badlein
    # Agar OpenVoiceV2 Hugging Face Transformers par 'text-to-speech' model ke tor par hai
    model_id = "suno/bark-small" # <--- Yahan OpenVoiceV2 ka asal model ID daalein!
                                #      Misal ke tor par "myshell-ai/open-voice-v2" ya similar.
                                #      Agar ye Bark model chal gaya, to OpenVoiceV2 ka
                                #      sahi model ID dhoondh kar yahan lagayen.

    processor = AutoProcessor.from_pretrained(model_id)
    model = AutoModelForTextToSpeech.from_pretrained(model_id)
    print(f"Model '{model_id}' loaded successfully using Transformers.")
    # Agar OpenVoiceV2 ke liye alag se speaker encoder ki zaroorat ho,
    # to woh bhi yahan load karna padega. Masalan:
    # speaker_encoder = AutoModel.from_pretrained("path/to/speaker-encoder")
    # Ye OpenVoiceV2 ke documentation par depend karta hai.
except Exception as e:
    print(f"Error loading model using Transformers: {e}")
    print("Please ensure OpenVoiceV2 is available on Hugging Face Hub for AutoProcessor/AutoModelForTextToSpeech, or check the correct model ID.")
    processor, model = None, None

def clone_voice(reference_audio, text_to_speak, language="en"):
    if processor is None or model is None:
        return None, "Error: Model could not be loaded at startup. Check logs."

    if reference_audio is None:
        return None, "Error: Please upload a reference audio file."
    if not text_to_speak:
        return None, "Error: Please enter text to speak."

    try:
        # --- Reference Audio se Speaker Embedding Hasil Karna (OpenVoiceV2 Specific) ---
        # Ye hissa OpenVoiceV2 ki khas functionality hai.
        # Aapko OpenVoiceV2 ke documentation se pata karna hoga ke
        # reference audio se speaker embedding kaise nikali jati hai.
        # Bark (jo abhi placeholder hai) is tareeqe se speaker embedding nahi nikalta.
        #
        # Misal ke tor par (conceptual code, asal OpenVoiceV2 ke hisab se badle ga):
        # ref_audio_waveform, ref_sample_rate = torchaudio.load(reference_audio)
        # speaker_embedding = some_openvoice_speaker_encoder(ref_audio_waveform, ref_sample_rate)
        # Is waqt, main Bark ke liye placeholder logic istemal kar raha hoon.

        # Text ko process karein
        inputs = processor(text=text_to_speak, return_tensors="pt")

        # Voice generate karein
        # Agar OpenVoiceV2 mein speaker embedding pass karna ho, to generate method mein add hoga:
        # generated_audio_array = model.generate(**inputs, speaker_embedding=speaker_embedding)
        
        # Abhi Bark placeholder ke liye:
        generated_audio_array = model.generate(**inputs, do_sample=True, generation_config=model.generation_config)

        # Output audio ko temporary file mein save karein
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_output_file:
            output_audio_path = tmp_output_file.name
            sf.write(output_audio_path, generated_audio_array[0].cpu().numpy(), samplerate=model.generation_config.sample_rate)

        print(f"Generated audio saved at: {output_audio_path}")
        return output_audio_path, "Voice generated successfully!"

    except Exception as e:
        print(f"Error during voice cloning: {e}")
        return None, f"Error: {str(e)}. Make sure OpenVoiceV2 model ID is correct and it supports voice cloning with provided inputs."

# Gradio Interface Banana
with gr.Blocks() as demo:
    gr.Markdown(
        """
        # OpenVoice V2 Voice Cloning Space 🎤
        Ek example voice audio file upload karein aur text likhein. Yeh tool us text ko aapki upload ki hui voice mein convert kar dega.
        """
    )
    with gr.Row():
        with gr.Column():
            reference_audio_input = gr.Audio(type="filepath", label="Reference Voice (WAV/MP3)")
            text_input = gr.Textbox(label="Text to Synthesize", placeholder="Enter text here...")
            # Language dropdown bhi rehne dein agar OpenVoiceV2 support karta ho
            language_input = gr.Dropdown(label="Language (for text accent, optional)", choices=["en", "es", "fr", "zh", "jp", "kr"], value="en")
            clone_button = gr.Button("Clone Voice ✨")
        with gr.Column():
            output_audio = gr.Audio(label="Cloned Voice Output", type="filepath")
            error_message = gr.Textbox(label="Status/Error", interactive=False)

    clone_button.click(
        clone_voice,
        inputs=[reference_audio_input, text_input, language_input],
        outputs=[output_audio, error_message]
    )

    gr.Markdown(
        """
        **Note:**
        - Behtareen results ke liye, reference audio saaf (clear) aur kam se kam 5 second ki honi chahiye.
        - Background noise kam se kam ho.
        - Yeh model research purposes ke liye hai. Iska ghalat istemal na karein.
        **IMPORTANT:** Is app ko OpenVoiceV2 ke sahi Hugging Face model ID se update karna hoga.
        """
    )

if __name__ == "__main__":
    if processor is None or model is None:
        print("Model failed to load. Gradio app might not function correctly.")
    else:
        print("Launching Gradio app...")
    demo.launch()