Integrating the DRL and LLM

README.md

@@ -46,7 +46,7 @@ For specific library versions, please refer to the `requirements.txt` file.
 3. Execute the script:
 
    ```bash
-   python3 appy.py
+   python3 app.py
    ```
 
 ### Usage

app.py

@@ -1,27 +1,51 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
+#!/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
-import streamlit as st
 
-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,
-)
+
+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")
-for seq in sequences:
-    st.write(f"Result: {seq['generated_text']}")
+
+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)

appy.py

@@ -1,54 +0,0 @@
-#!/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.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()

appyy.py

@@ -0,0 +1,27 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+import streamlit as st
+
+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']}")