Testing the LLM pipeline

app.py

@@ -8,55 +8,55 @@ 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()
+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)
+# 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()