app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import os

# Set model and tokenizer
model_name = "Qwen/Qwen2.5-Omni-3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")

# Function to process inputs and generate response
def process_input(text_input, image_input=None, audio_input=None):
    inputs = {"text": text_input}
    if image_input:
        inputs["image"] = image_input
    if audio_input:
        inputs["audio"] = audio_input
    
    # Tokenize inputs (simplified for demo)
    input_ids = tokenizer.encode(inputs["text"], return_tensors="pt").to(model.device)
    
    # Generate response
    outputs = model.generate(input_ids, max_length=200)
    response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Placeholder for speech generation (requires additional setup)
    response_audio = None  # Implement speech generation if needed
    
    return response_text, response_audio

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Qwen2.5-Omni-3B Demo")
    with gr.Row():
        text_input = gr.Textbox(label="Text Input")
        image_input = gr.Image(label="Upload Image")
        audio_input = gr.Audio(label="Upload Audio")
    submit_button = gr.Button("Submit")
    text_output = gr.Textbox(label="Text Response")
    audio_output = gr.Audio(label="Audio Response")
    
    submit_button.click(
        fn=process_input,
        inputs=[text_input, image_input, audio_input],
        outputs=[text_output, audio_output]
    )

# Launch the app
demo.launch()