refactor

app.py

@@ -10,9 +10,6 @@ from prompts import templates
 from typing import Any
 from string import Template
 
-st.header(" Scientific Claim Verification ")
-st.caption("Team UMBC-SBU-UT")
-
 def safe_parse_json(model_answer):
     """.."""
     try:
@@ -65,11 +62,9 @@ def check_password():
         st.error("😕 User not known or password incorrect")
     return False
 
-
 def select_models():
     """Returns only when a valid option is selected from both dropdowns."""
 
-    #placeholders
     retriever_options = ["Choose one...", "BM25 Retriever", "Off-the-shelf Retriever", "Finetuned Retriever", "No Retriever"]
     reasoner_options = ["Choose one...", "Claude Sonnet", "GPT-4o", "o3-mini"]
 
@@ -87,7 +82,7 @@ def select_models():
         key="reasoner"
     )
 
-    #next button
+    # next button
 
     if st.button("Next"):
         # Check that both selections are not the placeholder.
@@ -102,44 +97,6 @@ def select_models():
         st.info("Click 'Next' once you have made your selections.")
         return None, None
 
-if not check_password():
-    st.stop()
-
-
-if "selected_models" not in st.session_state:
-    selected_retriever, selected_reasoner = select_models()
-    # If valid selections are returned, store them and reset the change flag.
-    if selected_retriever is not None and selected_reasoner is not None:
-        st.session_state.selected_models = (selected_retriever, selected_reasoner)
-        st.rerun()
-    else:
-        st.stop()  # Halt further execution until valid selections are made.
-else:
-    selected_retriever, selected_reasoner = st.session_state.selected_models
-
-#START OF AGENTIC DEMO
-
-column1, column2 = st.columns(2)
-column1.caption(f"Retriever Selected: {selected_retriever}")
-column2.caption(f"Reasoner Selected: {selected_reasoner}")
-
-
-if st.button("Change Selection", key="change_selection_btn"):
-    st.session_state.pop("selected_models", None)
-    st.session_state.pop("retriever", None)
-    st.session_state.pop("reasoner", None)
-    st.session_state.messages = [{"role": "assistant", "content": "Let's start verifying the claims here! 👇"}]
-    st.rerun()
-
-# Initialize chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = [{"role": "assistant", "content": "Let's start verifying the claims here! 👇"}]
-
-# Display chat messages from history on app rerun
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
 def retriever(query: str, selected_retriever: str):
     """Simulate a 'retriever' step, searching for relevant information."""
     with st.chat_message("assistant"):
@@ -232,15 +189,47 @@ def reasoner(query: str, documents: list[str], llm_client: Any):
     # You could return reasoning info here.
     return reasoning, decision
 
-# Accept user input
-if prompt := st.chat_input("Type here"):
-    # Add user message to chat history
-    display_message = prompt + "  \n"+ "  \n"+ f"Retriever: {selected_retriever}, Reasoner: {selected_reasoner}"
-    st.session_state.messages.append({"role": "user", "content": display_message})
-    # Display user message in chat message container
-    with st.chat_message("user"):
-        st.markdown(display_message)
+def main():
+    st.header(" Scientific Claim Verification ")
+    st.caption("Team UMBC-SBU-UT")
+
+    if not check_password():
+        st.stop()
 
+    if "selected_models" not in st.session_state:
+        selected_retriever, selected_reasoner = select_models()
+        # If valid selections are returned, store them and reset the change flag.
+        if selected_retriever is not None and selected_reasoner is not None:
+            st.session_state.selected_models = (selected_retriever, selected_reasoner)
+            st.rerun()
+        else:
+            st.stop()  # Halt further execution until valid selections are made.
+    else:
+        selected_retriever, selected_reasoner = st.session_state.selected_models
+
+    # START OF AGENTIC DEMO
+
+    column1, column2 = st.columns(2)
+    column1.caption(f"Retriever Selected: {selected_retriever}")
+    column2.caption(f"Reasoner Selected: {selected_reasoner}")
+
+    if st.button("Change Selection", key="change_selection_btn"):
+        st.session_state.pop("selected_models", None)
+        st.session_state.pop("retriever", None)
+        st.session_state.pop("reasoner", None)
+        st.session_state.messages = [{"role": "assistant", "content": "Let's start verifying the claims here! 👇"}]
+        st.rerun()
+
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = [{"role": "assistant", "content": "Let's start verifying the claims here! 👇"}]
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Model configurations on agentic demo page
     options = {}
     options["max_tokens"] = 500
     options["temperature"] = 0.0
@@ -264,26 +253,38 @@ if prompt := st.chat_input("Type here"):
 
     llm_client = LLMReasoner(options)
 
-    retrieved_documents = retriever(prompt, selected_retriever)
-    reasoning, decision = reasoner(prompt, retrieved_documents, llm_client)
-
-    # Display assistant response in chat message container
-    with st.chat_message("assistant"):
-        message_placeholder = st.empty()
-        full_response = ""
-        if decision.lower() == 'support':
-            assistant_response = f'The claim is CORRECT because {reasoning}'
-        elif decision.lower() == 'contradict':
-            assistant_response = f'The claim is INCORRECT because {reasoning}'
-        else:
-            assistant_response = f'Sorry, the query failed due to an issue with connecting to the LLM service.'
-
-        # Simulate stream of response with milliseconds delay
-        for chunk in assistant_response.split():
-            full_response += chunk + " "
-            time.sleep(0.05)
-            # Add a blinking cursor to simulate typing
-            message_placeholder.markdown(full_response + "▌")
-        message_placeholder.markdown(full_response)
-    # Add assistant response to chat history
-    st.session_state.messages.append({"role": "assistant", "content": full_response})
+    # Accept user input
+    if prompt := st.chat_input("Type here"):
+        # Add user message to chat history
+        display_message = prompt + "  \n"+ "  \n"+ f"Retriever: {selected_retriever}, Reasoner: {selected_reasoner}"
+        st.session_state.messages.append({"role": "user", "content": display_message})
+        # Display user message in chat message container
+        with st.chat_message("user"):
+            st.markdown(display_message)
+
+        retrieved_documents = retriever(prompt, selected_retriever)
+        reasoning, decision = reasoner(prompt, retrieved_documents, llm_client)
+
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            full_response = ""
+            if decision.lower() == 'support':
+                assistant_response = f'The claim is CORRECT because {reasoning}'
+            elif decision.lower() == 'contradict':
+                assistant_response = f'The claim is INCORRECT because {reasoning}'
+            else:
+                assistant_response = f'Sorry, the query failed due to an issue with connecting to the LLM service.'
+
+            # Simulate stream of response with milliseconds delay
+            for chunk in assistant_response.split():
+                full_response += chunk + " "
+                time.sleep(0.05)
+                # Add a blinking cursor to simulate typing
+                message_placeholder.markdown(full_response + "▌")
+            message_placeholder.markdown(full_response)
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+if __name__ == '__main__':
+    main()