Addressed NameError.

app.py

@@ -1,23 +1,54 @@
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
+def build_prompt(conversation, agent_name):
+    """
+    Construct a single prompt that includes the entire conversation so far,
+    labeling each line with speaker, and ends with the new agent's label.
+    """
+    text_blocks = []
+    for speaker, text in conversation:
+        text_blocks.append(f"{speaker}: {text}")
+
+    # Now add the new agent's label at the end, so the model continues from there
+    text_blocks.append(f"{agent_name}:")
+    return "\n".join(text_blocks)
+
+def generate_response(agent_name, model, tokenizer, conversation):
+    """
+    Takes the entire conversation as context, plus the agent name, 
+    and runs a single inference call for that agent.
+    """
+    prompt_text = build_prompt(conversation, agent_name)
+    inputs = tokenizer.encode(prompt_text, return_tensors="pt")
+    outputs = model.generate(
+        inputs,
+        max_length=200,
+        temperature=0.7,
+        do_sample=True,
+        top_p=0.9
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
 @st.cache_resource
 def load_agentA():
-    # e.g., DistilGPT2
+    """Loads the DistilGPT2 model/tokenizer for Agent A."""
     tokenizerA = AutoTokenizer.from_pretrained("distilgpt2")
     modelA = AutoModelForCausalLM.from_pretrained("distilgpt2")
     return tokenizerA, modelA
 
 @st.cache_resource
 def load_agentB():
-    # e.g., GPT-Neo 125M
+    """Loads the GPT-Neo-125M model/tokenizer for Agent B."""
     tokenizerB = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
     modelB = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")
     return tokenizerB, modelB
 
+# Load agents
 tokenizerA, modelA = load_agentA()
 tokenizerB, modelB = load_agentB()
 
+# Streamlit app starts here
 st.title("True Multi-Agent Conversation")
 
 # We store the conversation as a list of (speaker, text).
@@ -27,12 +58,11 @@ if "conversation" not in st.session_state:
 user_input = st.text_input("Enter a question or scenario:")
 
 if st.button("Start/Continue Conversation"):
-    # 1) The user’s prompt goes to Agent A first.
+    # 1) If this is the first message, add the user input
     if len(st.session_state.conversation) == 0:
         st.session_state.conversation.append(("User", user_input))
     else:
-        # If conversation is ongoing, you can treat this user_input differently, 
-        # or ignore if you want to keep the user out after the initial scenario.
+        # If conversation is ongoing, append user’s new input
         st.session_state.conversation.append(("User", user_input))
 
     # --- AGENT A Step ---
@@ -55,39 +85,4 @@ if st.button("Start/Continue Conversation"):
 
 # Display the entire conversation so far
 for speaker, text in st.session_state.conversation:
-    st.markdown(f"**{speaker}:** {text}")
-
-
-def generate_response(agent_name, model, tokenizer, conversation):
-    """
-    Takes the entire conversation as context, plus the agent name, 
-    and runs a single inference call for that agent.
-    """
-    # 1) Build a textual prompt from conversation
-    # e.g. A simple approach: just concatenate everything
-    #     focusing on the last few messages to avoid token limit issues
-    prompt_text = build_prompt(conversation, agent_name)
-
-    inputs = tokenizer.encode(prompt_text, return_tensors="pt")
-    outputs = model.generate(
-        inputs,
-        max_length=200,
-        temperature=0.7,
-        do_sample=True,
-        top_p=0.9
-    )
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-
-def build_prompt(conversation, agent_name):
-    """
-    Construct a single prompt that includes the entire conversation so far,
-    labeling each line with speaker, and ends with the new agent's label.
-    """
-    text_blocks = []
-    for speaker, text in conversation:
-        text_blocks.append(f"{speaker}: {text}")
-
-    # Now add the new agent's label at the end, so the model continues from there
-    text_blocks.append(f"{agent_name}:")
-    return "\n".join(text_blocks)
+    st.markdown(f"**{speaker}:** {text}")