app.py

import gradio as gr
import os
import torch, torchvision, einops
import spaces
from transformers import AutoModelForCausalLM
from huggingface_hub import login
import subprocess

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

hf_token = os.getenv("HF_TOKEN")
login(token=hf_token, add_to_git_credential=True)

@spaces.GPU
def get_model_summary(model_name):
    # Check if CUDA is available and set the device accordingly
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    # Load the model and move it to the selected device
    model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device)
    
    # Return the model's architecture as a string
    return str(model)

# Create the Gradio interface
interface = gr.Interface(
    fn=get_model_summary,
    inputs="text",
    outputs="text",
    examples=[
        ["google/gemma-7b"],
        ["microsoft/Phi-3-mini-4k-instruct"],
        ["meta-llama/Meta-Llama-3-8B"],
        ["mistralai/Mistral-7B-Instruct-v0.3"],        
        ["vikhyatk/moondream2"],
        ["microsoft/Phi-3-vision-128k-instruct"],
        ["openbmb/MiniCPM-Llama3-V-2_5"],
        ["google/paligemma-3b-mix-224"],        
        ["HuggingFaceM4/idefics2-8b-chatty"],
        ["mistralai/Codestral-22B-v0.1"]
    ]    
)

interface.launch()