app.py

import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from llava.model.builder import load_pretrained_model  # Import LLaVA model builder

# Model name
model_name = "MONAI/Llama3-VILA-M3-8B"

# Load LLaVA model
tokenizer, model, _ = load_pretrained_model(model_path=model_name, model_base=None, device="cuda" if torch.cuda.is_available() else "cpu")

def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
    with torch.no_grad():
        output = model.generate(**inputs, max_length=200)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Gradio Interface
iface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(lines=2, placeholder="Enter your prompt..."),
    outputs="text",
    title="LLaVA Llama3-VILA-M3-8B Chatbot",
    description="A chatbot powered by LLaVA and Llama3-VILA-M3-8B",
)

iface.launch()