add: weave link to response

app.py

@@ -7,30 +7,39 @@ from guardrails_genie.llm import OpenAIModel
 load_dotenv()
 weave.init(project_name="guardrails-genie")
 
-st.title("Echo Bot")
-
-# Initialize chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-llm_model = OpenAIModel(model_name="gpt-4o-mini")
-
-# 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"])
-
-# React to user input
-if prompt := st.chat_input("What is up?"):
-    # Display user message in chat message container
-    st.chat_message("user").markdown(prompt)
-    # Add user message to chat history
-    st.session_state.messages.append({"role": "user", "content": prompt})
-
-    response = llm_model.predict(prompt, messages=st.session_state.messages)
-    response = response.choices[0].message.content
-    # Display assistant response in chat message container
-    with st.chat_message("assistant"):
-        st.markdown(response)
-    # Add assistant response to chat history
-    st.session_state.messages.append({"role": "assistant", "content": response})
+
+openai_model = st.sidebar.selectbox("OpenAI LLM", ["", "gpt-4o-mini", "gpt-4o"])
+chat_condition = openai_model != ""
+
+
+if chat_condition:
+    st.title("Guardrails Genie")
+
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    llm_model = OpenAIModel(model_name="gpt-4o-mini")
+
+    # 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"])
+
+    # React to user input
+    if prompt := st.chat_input("What is up?"):
+        # Display user message in chat message container
+        st.chat_message("user").markdown(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+        response, call = llm_model.predict.call(
+            llm_model, user_prompts=prompt, messages=st.session_state.messages
+        )
+        response = response.choices[0].message.content
+        response += f"\n\n---\n[Explore in Weave]({call.ui_url})"
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            st.markdown(response)
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": response})