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=["""undefined4 func0(float param_1,long param_2,int param_3)
{
  int local_28;
  int local_24;
  
  local_24 = 0;
  do {
    local_28 = local_24;
    if (param_3 <= local_24) {
      return 0;
    }
    while (local_28 = local_28 + 1, local_28 < param_3) {
      if ((double)((ulong)(double)(*(float *)(param_2 + (long)local_24 * 4) -
                                  *(float *)(param_2 + (long)local_28 * 4)) &
                  SUB168(_DAT_00402010,0)) < (double)param_1) {
        return 1;
      }
    }
    local_24 = local_24 + 1;
  } while( true );
}"""],
                    inputs="text", outputs="text")
demo.queue()
demo.launch()