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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import streamlit as st
import os
from trainer import train
from tester import test
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
def perform_training(jammer_type, channel_switching_cost):
agent = train(jammer_type, channel_switching_cost)
return agent
def perform_testing(agent, jammer_type, channel_switching_cost):
test(agent, jammer_type, channel_switching_cost)
model_name = "tiiuae/falcon-7b-instruct"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=100,
temperature=0.7)
st.title("Beyond the Anti-Jam: Integration of DRL with LLM")
st.sidebar.header("Make Your Environment Configuration")
mode = st.sidebar.radio("Choose Mode", ["Auto", "Manual"])
if mode == "Auto":
jammer_type = "dynamic"
channel_switching_cost = 0.1
else:
jammer_type = st.sidebar.selectbox("Select Jammer Type", ["constant", "sweeping", "random", "dynamic"])
channel_switching_cost = st.sidebar.selectbox("Select Channel Switching Cost", [0, 0.05, 0.1, 0.15, 0.2])
st.sidebar.subheader("Configuration:")
st.sidebar.write(f"Jammer Type: {jammer_type}")
st.sidebar.write(f"Channel Switching Cost: {channel_switching_cost}")
start_button = st.sidebar.button('Start')
if start_button:
agent = perform_training(jammer_type, channel_switching_cost)
st.subheader("Generating Insights of the DRL-Training")
text = pipeline("Discuss this topic: Integrating LLMs to DRL-based anti-jamming.")
st.write(text)
test(agent, jammer_type, channel_switching_cost)
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