app.py

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

model_path = 'LLM4Binary/llm4decompile-6.7b-v2' # V2 Model
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).cuda()

@spaces.GPU
def predict(input_asm):

    before = f"# This is the assembly code:\n"#prompt
    after = "\n# What is the source code?\n"#prompt
    input_prompt = before+input_asm.strip()+after
    
    inputs = tokenizer(input_prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=2048)### max length to 4096, max new tokens should be below the range
    c_func_decompile = tokenizer.decode(outputs[0][len(inputs[0]):-1])
    return c_func_decompile

demo = gr.Interface(fn=predict,
                    examples=["void ioabs_tcp_pre_select(connection c, int *n, struct pollfd *pfds) { struct ioabs_tcp *io; io = (struct ioabs_tcp*)c->io; c->s_index = *n; (*n)++; pfds[c->s_index].fd = c->s; pfds[c->s_index].events |= 0x0001; if (((size_t)(((c->wrb)->put + (c->wrb)->len - (c->wrb)->get) % (c->wrb)->len)) > 0) pfds[c->s_index].events |= 0x0004; }"],
                    inputs="text", outputs="text")
demo.queue()
demo.launch()