app.py

#!/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, rewards = 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)

# model = "tiiuae/falcon-7b-instruct"
#
# tokenizer = AutoTokenizer.from_pretrained(model)
# pipeline = transformers.pipeline(
#     "text-generation",
#     model=model,
#     tokenizer=tokenizer,
#     torch_dtype=torch.bfloat16,
#     trust_remote_code=True,
#     device_map="auto",
# )
# sequences = pipeline(
#    "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
#     max_length=200,
#     do_sample=True,
#     top_k=10,
#     num_return_sequences=1,
#     eos_token_id=tokenizer.eos_token_id,
# )
# st.title("Beyond the Anti-Jam: Integration of DRL with LLM")
# for seq in sequences:
#     st.write(f"Result: {seq['generated_text']}")