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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 TFAutoModelForCausalLM, AutoTokenizer | |
| def main(): | |
| st.subheader("Generating Insights of the DRL-Training") | |
| model_name = "tiiuae/falcon-7b-instruct" | |
| model = TFAutoModelForCausalLM.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) | |
| text = pipeline("Discuss this topic: Integrating LLMs to DRL-based anti-jamming.") | |
| st.write(text) | |
| # 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") | |
| # model_name = "tiiuae/falcon-7b-instruct" | |
| # model = TFAutoModelForCausalLM.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) | |
| # text = pipeline("Discuss this topic: Integrating LLMs to DRL-based anti-jamming.") | |
| # st.write(text) | |
| # test(agent, jammer_type, channel_switching_cost) | |
| 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) | |
| if __name__ == "__main__": | |
| main() | |