# Import spaces first to avoid CUDA initialization issues
import spaces

# Then import other libraries
import torch
import librosa
from transformers import pipeline, WhisperProcessor, WhisperForConditionalGeneration, AutoModelForCausalLM, AutoProcessor
from gtts import gTTS
import gradio as gr
from PIL import Image
import os
import io
import subprocess
from langdetect import detect

print("Using GPU for operations when available")

# Install flash-attn
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

# Function to safely load pipeline within a GPU-decorated function
@spaces.GPU
def load_pipeline(model_name, **kwargs):
    try:
        device = 0 if torch.cuda.is_available() else "cpu"
        return pipeline(model=model_name, device=device, **kwargs)
    except Exception as e:
        print(f"Error loading {model_name} pipeline: {e}")
        return None

# Load Whisper model for speech recognition within a GPU-decorated function
@spaces.GPU
def load_whisper():
    try:
        device = 0 if torch.cuda.is_available() else "cpu"
        processor = WhisperProcessor.from_pretrained("openai/whisper-small")
        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to(device)
        return processor, model
    except Exception as e:
        print(f"Error loading Whisper model: {e}")
        return None, None

# Load sarvam-2b for text generation within a GPU-decorated function
@spaces.GPU
def load_sarvam():
    return load_pipeline('sarvamai/sarvam-2b-v0.5')

# Load Phi-3.5-vision-instruct model
@spaces.GPU
def load_vision_model():
    try:
        model_id = "microsoft/Phi-3.5-vision-instruct"
        model = AutoModelForCausalLM.from_pretrained(
            model_id, trust_remote_code=True, torch_dtype=torch.float16, use_flash_attention_2=False
        )
        processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True, num_crops=16)
        return model, processor
    except Exception as e:
        print(f"Error loading vision model: {e}")
        return None, None


# Load sarvam-2b for text generation within a GPU-decorated function
@spaces.GPU
def load_sarvam():
    return load_pipeline('sarvamai/sarvam-2b-v0.5')

# Load Phi-3.5-vision-instruct model
@spaces.GPU
def load_vision_model():
    try:
        model_id = "microsoft/Phi-3.5-vision-instruct"
        model = AutoModelForCausalLM.from_pretrained(
            model_id, trust_remote_code=True, torch_dtype=torch.float16, use_flash_attention_2=False
        )
        processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True, num_crops=16)
        return model, processor
    except Exception as e:
        print(f"Error loading vision model: {e}")
        return None, None

# Process audio input within a GPU-decorated function
@spaces.GPU
def process_audio_input(audio, whisper_processor, whisper_model):
    if whisper_processor is None or whisper_model is None:
        return "Error: Speech recognition model is not available. Please type your message instead."
    
    try:
        audio, sr = librosa.load(audio, sr=16000)
        input_features = whisper_processor(audio, sampling_rate=sr, return_tensors="pt").input_features.to(whisper_model.device)
        predicted_ids = whisper_model.generate(input_features)
        transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
        return transcription
    except Exception as e:
        return f"Error processing audio: {str(e)}. Please type your message instead."

# Process image input
@spaces.GPU
def process_image_input(image, vision_model, vision_processor):
    if vision_model is None or vision_processor is None:
        return "Error: Vision model is not available."
    
    try:
        # Add a generic prompt for image description
        prompt = "Describe this image in detail."
        
        inputs = vision_processor(images=image, text=prompt, return_tensors="pt")
        inputs = {k: v.to(vision_model.device) for k, v in inputs.items()}
        
        with torch.no_grad():
            outputs = vision_model.generate(**inputs, max_new_tokens=512, do_sample=True, top_k=50, top_p=0.95)
        
        generated_text = vision_processor.batch_decode(outputs, skip_special_tokens=True)[0]
        return generated_text
    except Exception as e:
        return f"Error processing image: {str(e)}"
# Generate response within a GPU-decorated function
@spaces.GPU
def generate_response(transcription, sarvam_pipe):
    if sarvam_pipe is None:
        return "Error: Text generation model is not available."
    
    try:
        # Generate response using the sarvam-2b model
        response = sarvam_pipe(transcription, max_length=100, num_return_sequences=1)[0]['generated_text']
        return response
    except Exception as e:
        return f"Error generating response: {str(e)}"

# Text-to-speech function
def text_to_speech(text, lang='hi'):
    try:
        # Use a better TTS engine for Indic languages
        if lang in ['hi', 'bn', 'gu', 'kn', 'ml', 'mr', 'or', 'pa', 'ta', 'te']:
            # You might want to use a different TTS library here
            # For example, you could use the Google Cloud Text-to-Speech API
            # or a specialized Indic language TTS library
            
            # This is a placeholder for a better Indic TTS solution
            tts = gTTS(text=text, lang=lang, tld='co.in')  # Use Indian TLD
        else:
            tts = gTTS(text=text, lang=lang)
        
        tts.save("response.mp3")
        return "response.mp3"
    except Exception as e:
        print(f"Error in text-to-speech: {str(e)}")
        return None

# Improved language detection function
def detect_language(text):
    lang_codes = {
        'bn': 'Bengali', 'gu': 'Gujarati', 'hi': 'Hindi', 'kn': 'Kannada',
        'ml': 'Malayalam', 'mr': 'Marathi', 'or': 'Oriya', 'pa': 'Punjabi',
        'ta': 'Tamil', 'te': 'Telugu', 'en': 'English'
    }
    
    try:
        detected_lang = detect(text)
        return detected_lang if detected_lang in lang_codes else 'en'
    except:
        # Fallback to simple script-based detection
        for code, lang in lang_codes.items():
            if any(ord(char) >= 0x0900 and ord(char) <= 0x097F for char in text):  # Devanagari script
                return 'hi'
        return 'en'  # Default to English if no Indic script is detected

@spaces.GPU
def indic_vision_assistant(input_type, audio_input, text_input, image_input):
    try:
        # Load models within the GPU-decorated function
        whisper_processor, whisper_model = load_whisper()
        sarvam_pipe = load_sarvam()
        vision_model, vision_processor = load_vision_model()

        if input_type == "audio" and audio_input is not None:
            transcription = process_audio_input(audio_input, whisper_processor, whisper_model)
        elif input_type == "text" and text_input:
            transcription = text_input
        elif input_type == "image" and image_input is not None:
            transcription = process_image_input(image_input, vision_model, vision_processor)
        else:
            return "Please provide either audio, text, or image input.", "No input provided.", None

        response = generate_response(transcription, sarvam_pipe)
        lang = detect_language(response)
        audio_response = text_to_speech(response, lang)
        
        return transcription, response, audio_response
    except Exception as e:
        error_message = f"An error occurred: {str(e)}"
        return error_message, error_message, None

# Custom CSS
custom_css = """
body {
    background-color: #0b0f19;
    color: #e2e8f0;
    font-family: 'Arial', sans-serif;
}
#custom-header {
    text-align: center;
    padding: 20px 0;
    background-color: #1a202c;
    margin-bottom: 20px;
    border-radius: 10px;
}
#custom-header h1 {
    font-size: 2.5rem;
    margin-bottom: 0.5rem;
}
#custom-header h1 .blue {
    color: #60a5fa;
}
#custom-header h1 .pink {
    color: #f472b6;
}
#custom-header h2 {
    font-size: 1.5rem;
    color: #94a3b8;
}
.suggestions {
    display: flex;
    justify-content: center;
    flex-wrap: wrap;
    gap: 1rem;
    margin: 20px 0;
}
.suggestion {
    background-color: #1e293b;
    border-radius: 0.5rem;
    padding: 1rem;
    display: flex;
    align-items: center;
    transition: transform 0.3s ease;
    width: 200px;
}
.suggestion:hover {
    transform: translateY(-5px);
}
.suggestion-icon {
    font-size: 1.5rem;
    margin-right: 1rem;
    background-color: #2d3748;
    padding: 0.5rem;
    border-radius: 50%;
}
.gradio-container {
    max-width: 100% !important;
}
#component-0, #component-1, #component-2 {
    max-width: 100% !important;
}
footer {
    text-align: center;
    margin-top: 2rem;
    color: #64748b;
}
"""

# Custom HTML for the header
custom_header = """
<div id="custom-header">
    <h1>
        <span class="blue">Hello,</span>
        <span class="pink">User</span>
    </h1>
    <h2>How can I help you today?</h2>
</div>
"""

# Custom HTML for suggestions
custom_suggestions = """
<div class="suggestions">
    <div class="suggestion">
        <span class="suggestion-icon">🎤</span>
        <p>Speak in any Indic language</p>
    </div>
    <div class="suggestion">
        <span class="suggestion-icon">⌨️</span>
        <p>Type in any Indic language</p>
    </div>
    <div class="suggestion">
        <span class="suggestion-icon">🖼️</span>
        <p>Upload an image for analysis</p>
    </div>
    <div class="suggestion">
        <span class="suggestion-icon">🤖</span>
        <p>Get AI-generated responses</p>
    </div>
    <div class="suggestion">
        <span class="suggestion-icon">🔊</span>
        <p>Listen to audio responses</p>
    </div>
</div>
"""
# Create Gradio interface
with gr.Blocks(css=custom_css, theme=gr.themes.Base().set(
    body_background_fill="#0b0f19",
    body_text_color="#e2e8f0",
    button_primary_background_fill="#3b82f6",
    button_primary_background_fill_hover="#2563eb",
    button_primary_text_color="white",
    block_title_text_color="#94a3b8",
    block_label_text_color="#94a3b8",
)) as iface:
    gr.HTML(custom_header)
    gr.HTML(custom_suggestions)
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Indic Vision Assistant")
    
    input_type = gr.Radio(["audio", "text", "image"], label="Input Type", value="audio")
    audio_input = gr.Audio(type="filepath", label="Speak (if audio input selected)")
    text_input = gr.Textbox(label="Type your message (if text input selected)")
    image_input = gr.Image(type="pil", label="Upload an image (if image input selected)")
    
    submit_btn = gr.Button("Submit")
    
    output_transcription = gr.Textbox(label="Transcription/Input")
    output_response = gr.Textbox(label="Generated Response")
    output_audio = gr.Audio(label="Audio Response")
    
    submit_btn.click(
        fn=indic_vision_assistant,
        inputs=[input_type, audio_input, text_input, image_input],
        outputs=[output_transcription, output_response, output_audio]
    )
    gr.HTML("<footer>Powered by Indic Language AI with Vision Capabilities</footer>")

# Launch the app
iface.launch()