app.py

import torch
import librosa
from transformers import pipeline, WhisperProcessor, WhisperForConditionalGeneration
from gtts import gTTS
import gradio as gr
import spaces
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


print("Using GPU for operations when available")

# 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"
        logger.info(f"Loading {model_name} on device: {device}")
        return pipeline(model=model_name, device=device, **kwargs)
    except Exception as e:
        logger.error(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"
        logger.info(f"Loading Whisper model on device: {device}")
        processor = WhisperProcessor.from_pretrained("openai/whisper-small")
        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to(device)
        return processor, model
    except Exception as e:
        logger.error(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():
    logger.info("Loading sarvam-2b model")
    return load_pipeline('sarvamai/sarvam-2b-v0.5')


# Global variables for models
whisper_processor, whisper_model = load_whisper()
sarvam_pipe = load_sarvam()

# Check if models are loaded
if whisper_processor is None or whisper_model is None:
    logger.error("Whisper model failed to load")
if sarvam_pipe is None:
    logger.error("Sarvam model failed to load")

# 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."

# 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:
        # Prepare the prompt
        prompt = f"Human: {transcription}\n\nAssistant:"
        
        # Generate response using the sarvam-2b model
        response = sarvam_pipe(prompt, max_length=200, num_return_sequences=1, do_sample=True, temperature=0.7)[0]['generated_text']
        
        # Extract the assistant's response
        assistant_response = response.split("Assistant:")[-1].strip()
        
        return assistant_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']:
            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

# 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_language_assistant(input_type, audio_input, text_input):
    try:
        if input_type == "audio" and audio_input is not None:
            if whisper_processor is None or whisper_model is None:
                return "Error: Speech recognition model is not available.", "", None
            transcription = process_audio_input(audio_input, whisper_processor, whisper_model)
        elif input_type == "text" and text_input:
            transcription = text_input
        else:
            return "Please provide either audio or text input.", "", None

        if sarvam_pipe is None:
            return transcription, "Error: Text generation model is not available.", 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:
        logger.error(f"An error occurred in indic_language_assistant: {str(e)}")
        return str(e), "An error occurred while processing your request.", None


# Updated 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>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 Assistant")
        with gr.Column(scale=1, min_width=100):
            gr.Button("Try Advanced Features", size="sm")
    
    input_type = gr.Radio(["audio", "text"], 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)")
    
    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_language_assistant,
        inputs=[input_type, audio_input, text_input],
        outputs=[output_transcription, output_response, output_audio]
    )

    gr.HTML("<footer>Powered by Indic Language AI</footer>")

# Launch the app
iface.launch()