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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import streamlit as st
import trainer
import tester
import os
def main():
st.title("Beyond the Anti-Jam: Integration of DRL with LLM")
st.header("Make Your Environment Configuration")
mode = st.radio("Choose Mode", ["Auto", "Manual"])
if mode == "Auto":
jammer_type = "dynamic"
channel_switching_cost = 0.1
else:
jammer_type = st.selectbox("Select Jammer Type", ["constant", "sweeping", "random", "dynamic"])
channel_switching_cost = st.selectbox("Select Channel Switching Cost", [0, 0.05, 0.1, 0.15, 0.2])
st.subheader("Configuration:")
st.write(f"Jammer Type: {jammer_type}")
st.write(f"Channel Switching Cost: {channel_switching_cost}")
if st.button('Train'):
st.write("==================================================")
st.write('Training Starting')
trainer.train(jammer_type, channel_switching_cost)
st.write("Training completed")
st.write("==================================================")
if st.button('Test'):
st.write("==================================================")
st.write('Testing Starting')
agentName = f'savedAgents/DDQNAgent_{jammer_type}_csc_{channel_switching_cost}'
if os.path.exists(agentName):
tester.test(jammer_type, channel_switching_cost)
st.write("Testing completed")
st.write("==================================================")
else:
st.write("Agent has not been trained yet. Click Train First!!!")
if __name__ == "__main__":
main()
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