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Refactoring app.py
Browse files- app.py +43 -29
- tester.py +1 -3
- trainer.py +1 -1
app.py
CHANGED
@@ -1,49 +1,63 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import streamlit as st
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import trainer
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import tester
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import os
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def main():
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st.title("Beyond the Anti-Jam: Integration of DRL with LLM")
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if mode == "Auto":
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jammer_type = "dynamic"
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channel_switching_cost = 0.1
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else:
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jammer_type = st.selectbox("Select Jammer Type", ["constant", "sweeping", "random", "dynamic"])
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channel_switching_cost = st.selectbox("Select Channel Switching Cost", [0, 0.05, 0.1, 0.15, 0.2])
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st.subheader("Configuration:")
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st.write(f"Jammer Type: {jammer_type}")
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st.write(f"Channel Switching Cost: {channel_switching_cost}")
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st.write("==================================================")
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st.write('Testing Starting')
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tester.test(jammer_type, channel_switching_cost)
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st.write("Testing completed")
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st.write("==================================================")
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else:
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if __name__ == "__main__":
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import streamlit as st
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import os
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from trainer import train
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from tester import test
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def main():
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st.title("Beyond the Anti-Jam: Integration of DRL with LLM")
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st.sidebar.header("Make Your Environment Configuration")
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mode = st.sidebar.radio("Choose Mode", ["Auto", "Manual"])
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if mode == "Auto":
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jammer_type = "dynamic"
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channel_switching_cost = 0.1
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else:
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jammer_type = st.sidebar.selectbox("Select Jammer Type", ["constant", "sweeping", "random", "dynamic"])
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channel_switching_cost = st.sidebar.selectbox("Select Channel Switching Cost", [0, 0.05, 0.1, 0.15, 0.2])
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st.sidebar.subheader("Configuration:")
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st.sidebar.write(f"Jammer Type: {jammer_type}")
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st.sidebar.write(f"Channel Switching Cost: {channel_switching_cost}")
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train_button = st.sidebar.button('Train')
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test_button = st.sidebar.button('Test')
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if train_button or test_button:
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agent_name = f'DDQNAgent_{jammer_type}_csc_{channel_switching_cost}'
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if os.path.exists(agent_name):
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if train_button:
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st.warning("Agent has been trained already! Do you want to retrain?")
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retrain = st.sidebar.button('Yes')
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if retrain:
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perform_training(jammer_type, channel_switching_cost)
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elif test_button:
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perform_testing(jammer_type, channel_switching_cost)
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else:
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if train_button:
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perform_training(jammer_type, channel_switching_cost)
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elif test_button:
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st.warning("Agent has not been trained yet. Click Train First!!!")
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def perform_training(jammer_type, channel_switching_cost):
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st.sidebar.write("==================================================")
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st.sidebar.write('Training Starting')
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train(jammer_type, channel_switching_cost)
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st.sidebar.write("Training completed")
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st.sidebar.write("==================================================")
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def perform_testing(jammer_type, channel_switching_cost):
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st.sidebar.write("==================================================")
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st.sidebar.write('Testing Starting')
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test(jammer_type, channel_switching_cost)
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st.sidebar.write("Testing completed")
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st.sidebar.write("==================================================")
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if __name__ == "__main__":
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tester.py
CHANGED
@@ -13,13 +13,11 @@ def test(jammer_type, channel_switching_cost):
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env = AntiJamEnv(jammer_type, channel_switching_cost)
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ob_space = env.observation_space
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ac_space = env.action_space
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st.write(f"Observation space: , {ob_space}")
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st.write(f"Action space: {ac_space}")
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s_size = ob_space.shape[0]
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a_size = ac_space.n
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max_env_steps = 100
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TEST_Episodes =
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env._max_episode_steps = max_env_steps
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epsilon = 1.0 # exploration rate
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env = AntiJamEnv(jammer_type, channel_switching_cost)
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ob_space = env.observation_space
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ac_space = env.action_space
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s_size = ob_space.shape[0]
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a_size = ac_space.n
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max_env_steps = 100
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TEST_Episodes = 10
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env._max_episode_steps = max_env_steps
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epsilon = 1.0 # exploration rate
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trainer.py
CHANGED
@@ -19,7 +19,7 @@ def train(jammer_type, channel_switching_cost):
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s_size = ob_space.shape[0]
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a_size = ac_space.n
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max_env_steps = 100
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TRAIN_Episodes =
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env._max_episode_steps = max_env_steps
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epsilon = 1.0 # exploration rate
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s_size = ob_space.shape[0]
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a_size = ac_space.n
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max_env_steps = 100
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TRAIN_Episodes = 10
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env._max_episode_steps = max_env_steps
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epsilon = 1.0 # exploration rate
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