Testing the LLM pipeline

app.py

@@ -5,34 +5,48 @@ import streamlit as st
 import os
 from trainer import train
 from tester import test
-from transformers_setup import pipeline  # Import the pipeline from the setup module
+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")
-        text = pipeline("Discuss this topic: Integrating LLMs to DRL-based anti-jamming.")
-        st.write(text)
-        test(agent, 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)
+    # 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):

transformers_setup.py

@@ -1,11 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-
-import transformers
-from transformers import TFAutoModelForCausalLM, AutoTokenizer
-
-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)