app.py

# 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
from langdetect import detect
import subprocess

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 vision model within a GPU-decorated function
@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 all models
whisper_processor, whisper_model = load_whisper()
vision_model, vision_processor = load_vision_model()
sarvam_pipe = load_sarvam()

@spaces.GPU
def process_audio_input(audio):
    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."

@spaces.GPU
def process_image_input(image, text_prompt):
    if vision_model is None or vision_processor is None:
        return "Error: Vision model is not available."
    
    try:
        messages = [
            {"role": "user", "content": f"{text_prompt}\n<|image_1|>"},
        ]
        prompt = vision_processor.tokenizer.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True
        )
        inputs = vision_processor(prompt, image, return_tensors="pt").to(vision_model.device)
        generate_ids = vision_model.generate(**inputs, max_new_tokens=1000, temperature=0.2, do_sample=True)
        generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
        response = vision_processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        return response
    except Exception as e:
        return f"Error processing image: {str(e)}"

def generate_response(transcription):
    if sarvam_pipe is None:
        return "Error: Text generation model is not available."
    
    try:
        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)}"

def text_to_speech(text, lang='hi'):
    try:
        tts = gTTS(text=text, lang=lang, tld='co.in')
        tts.save("response.mp3")
        return "response.mp3"
    except Exception as e:
        print(f"Error in text-to-speech: {str(e)}")
        return None

@spaces.GPU
def indic_vision_assistant(input_type, audio_input, text_input, image_input):
    try:
        if input_type == "audio" and audio_input is not None:
            transcription = process_audio_input(audio_input)
        elif input_type == "text" and text_input:
            transcription = text_input
        elif input_type == "image" and image_input is not None:
            text_prompt = text_input if text_input else "Describe this image in detail."
            transcription = process_image_input(image_input, text_prompt)
        else:
            return "Please provide either audio, text, or image input.", "No input provided.", None

        response = generate_response(transcription)
        lang = detect(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>
"""

# 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 or image prompt")
    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()