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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load the model and tokenizer
model_path = 'LLM4Binary/llm4decompile-1.3b-v1.5' # V1.5 Model
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).cuda()
# Define the inference function
def generate_response(input_text, temperature, top_k, top_p):
before = f"# This is the assembly code:\n"#prompt
after = "\n# What is the source code?\n"#prompt
input_func = before+input_text.strip()+after
inputs = tokenizer(input_func, return_tensors="pt")
outputs = model.generate(
**inputs,
max_length=512, # Adjust this if needed
do_sample=True,
top_k=int(top_k),
top_p=float(top_p),
temperature=float(temperature)
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Create a Gradio interface with sliders
interface = gr.Interface(
fn=generate_response,
inputs=[
gr.Textbox(lines=5, placeholder="Enter your input text here...", label="Input Text"),
gr.Slider(0.1, 2.0, value=0.0, step=0.1, label="Temperature"),
gr.Slider(1, 100, value=10, step=1, label="Top-k"),
gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
],
outputs=gr.Textbox(label="Generated Response"),
title="LLM4Binary Interactive Demo",
description="Adjust the sliders for temperature, top-k, and top-p to customize the model's response."
)
# Launch the Gradio app
interface.launch()
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