Upload 2 files

app.py

@@ -1,13 +1,16 @@
+import logging
+from typing import Optional
+
 import openai
 import streamlit as st
-from langchain_core.messages import AIMessage, ChatMessage, HumanMessage
-from langchain_core.tracers.context import collect_runs
-from langsmith import Client
-from streamlit_feedback import streamlit_feedback
+from langchain_core.messages import AIMessage, HumanMessage
 
-from rag.runnable import get_runnable
+from rag.runnable_and_memory import get_runnable_and_memory
 from utils.error_message_template import ERROR_MESSAGE
 
+logging.basicConfig(level=logging.ERROR)
+
+
 # Streamlit page configuration
 st.set_page_config(
     page_title="ELLA AI Assistant",
@@ -21,166 +24,127 @@ with open("styles/styles.css") as css:
     st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)
 
 
-# Get runnable and memory
-@st.cache_resource(show_spinner=False)
-def get_runnable_and_memory():
-    try:
-        return get_runnable(model="gpt-4-turbo", temperature=0)
-    except Exception:
-        st.warning(ERROR_MESSAGE, icon="🙁")
-        st.stop()
-
-
-chain, memory = get_runnable_and_memory()
-
-
-# Set up session state variables
-# Clean memory (important! to clean the memory at the end of each session)
-if "history" not in st.session_state:
-    st.session_state["history"] = []
-    memory.clear()
+def initialize_session_state():
+    # Initialize the runnable and memory
+    if "runnable" not in st.session_state:
+        try:
+            st.session_state["runnable"], st.session_state["memory"] = (
+                get_runnable_and_memory(model="gpt-4o", temperature=0)
+            )
+            # Clear the memory
+            st.session_state["memory"].clear()
+        except Exception:
+            handle_errors()
 
-if "messages" not in st.session_state:
-    st.session_state["messages"] = []
+    # Other session state variables
+    if "chat_history" not in st.session_state:
+        st.session_state["chat_history"] = []
 
-if "selected_location" not in st.session_state:
-    st.session_state["selected_location"] = None
+    if "selected_location" not in st.session_state:
+        st.session_state["selected_location"] = None
 
-if "disable_chat_input" not in st.session_state:
-    st.session_state["disable_chat_input"] = True
+    if "disable_chat_input" not in st.session_state:
+        st.session_state["disable_chat_input"] = True
 
 
-# Welcome message and Selectbox for location preferences
-def welcome_message():
-    st.markdown(
-        "Hello there! 👋 Need help finding the right service or practitioner? Let our AI assistant give you a hand.\n\n"
-        "To get started, please select your preferred location and share details about your symptoms or needs. "
-    )
+@st.cache_resource
+def load_avatars():
+    return {
+        "Human": "images/user.png",
+        "AI": "images/tall-tree-logo.png",
+    }
 
 
+# Disable chat input if no location is selected
 def on_change_location():
     st.session_state["disable_chat_input"] = (
         False if st.session_state["selected_location"] else True
     )
 
 
-with st.container():
-    welcome_message()
-    location = st.radio(
-        "**Our Locations**:",
-        (
-            "Cordova Bay - Victoria",
-            "James Bay - Victoria",
-            "Commercial Drive - Vancouver",
-        ),
-        index=None,
-        label_visibility="visible",
-        key="selected_location",
-        on_change=on_change_location,
-    )
-    st.markdown("<br>", unsafe_allow_html=True)
-
-# Get user input only if a location is selected
-user_input = st.chat_input(
-    "Ask ELLA...", disabled=st.session_state["disable_chat_input"]
-)
-
-if user_input:
-    st.session_state["messages"].append(ChatMessage(role="user", content=user_input))
-    prompt = f"{user_input}\nLocation preference: {st.session_state.selected_location}."
-
-else:
-    prompt = None
-
-# Display previous messages
-user_avatar = "images/user.png"
-ai_avatar = "images/tall-tree-logo.png"
-for msg in st.session_state["messages"]:
-    avatar = user_avatar if msg.role == "user" else ai_avatar
-    with st.chat_message(msg.role, avatar=avatar):
-        st.markdown(msg.content)
-
-# Chat interface
-if prompt:
-    # Add all previous messages to memory
-    for human, ai in st.session_state["history"]:
-        memory.chat_memory.add_user_message(HumanMessage(content=human))
-        memory.chat_memory.add_ai_message(AIMessage(content=ai))
-
-    # render the assistant's response
-    with st.chat_message("assistant", avatar=ai_avatar):
-        message_placeholder = st.empty()
-
-        try:
-            partial_message = ""
-            # Collect runs for feedback using Langsmith.
-            with st.spinner(" "), collect_runs() as cb:
-                for chunk in chain.stream({"message": prompt}):
-                    partial_message += chunk
-                    message_placeholder.markdown(partial_message + "|")
-                st.session_state.run_id = cb.traced_runs[0].id
-            message_placeholder.markdown(partial_message)
-        except openai.BadRequestError:
-            st.warning(ERROR_MESSAGE, icon="🙁")
-            st.stop()
-        except Exception:
-            st.warning(ERROR_MESSAGE, icon="🙁")
-            st.stop()
-
-        # Add the full response to the history
-        st.session_state["history"].append((prompt, partial_message))
-
-        # Add AI message to memory after the response is generated
-        memory.chat_memory.add_ai_message(AIMessage(content=partial_message))
+def app_layout():
+    with st.container():
+        # Welcome message
+        st.markdown(
+            "Hello there! 👋 Need help finding the right service or practitioner? Let our AI assistant give you a hand.\n\n"
+            "To get started, please select your preferred location and share details about your symptoms or needs. "
+        )
 
-        # Add the full response to the message history
-        st.session_state["messages"].append(
-            ChatMessage(role="assistant", content=partial_message)
+        # Selectbox for location preferences
+        st.radio(
+            "**Our Locations**:",
+            (
+                "Cordova Bay - Victoria",
+                "James Bay - Victoria",
+                "Commercial Drive - Vancouver",
+            ),
+            index=None,
+            label_visibility="visible",
+            key="selected_location",
+            on_change=on_change_location,
         )
+        st.markdown("<br>", unsafe_allow_html=True)
 
 
-# Feedback system using streamlit-feedback and Langsmith
+def handle_errors(error: Optional[Exception] = None):
+    st.warning(error if error else ERROR_MESSAGE, icon="🙁")
+    st.stop()
 
-# Langsmith client for the feedback system
-ls_client = Client()
 
-# Feedback option
-feedback_option = "thumbs"
+# Chat app logic
+if __name__ == "__main__":
+    initialize_session_state()
+    app_layout()
 
-if st.session_state.get("run_id"):
-    run_id = st.session_state.run_id
-    feedback = streamlit_feedback(
-        feedback_type=feedback_option,
-        optional_text_label="[Optional] Please provide an explanation",
-        key=f"feedback_{run_id}",
-    )
-    score_mappings = {
-        "thumbs": {"👍": 1, "👎": 0},
-        "faces": {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0},
-    }
+    # Render conversation
+    avatars = load_avatars()
+    for message in st.session_state["chat_history"]:
+        if isinstance(message, AIMessage):
+            with st.chat_message("AI", avatar=avatars["AI"]):
+                st.write(message.content)
+        elif isinstance(message, HumanMessage):
+            with st.chat_message("Human", avatar=avatars["Human"]):
+                st.write(message.content)
 
-    # Get the score mapping based on the selected feedback option
-    scores = score_mappings[feedback_option]
+    # Get user input only if a location is selected
+    user_input = st.chat_input(
+        "Ask ELLA...", disabled=st.session_state["disable_chat_input"]
+    )
 
-    if feedback:
-        # Get the score from the selected feedback option's score mapping
-        score = scores.get(feedback["score"])
+    # Chat interface
+    if user_input and user_input.strip():
+        st.session_state["chat_history"].append(HumanMessage(content=user_input))
 
-        if score is not None:
-            # Formulate feedback type string incorporating the feedback option
-            # and score value
-            feedback_type_str = f"{feedback_option} {feedback['score']}"
+        # Append the location to the user input (important!)
+        user_query_location = (
+            f"{user_input}\nLocation: {st.session_state.selected_location}."
+        )
 
-            # Record the feedback with the formulated feedback type string
-            feedback_record = ls_client.create_feedback(
-                run_id,
-                feedback_type_str,
-                score=score,
-                comment=feedback.get("text"),
-            )
-            st.session_state.feedback = {
-                "feedback_id": str(feedback_record.id),
-                "score": score,
-            }
-        else:
-            st.warning("Invalid feedback score.")
+        # Render the user input
+        with st.chat_message("Human", avatar=avatars["Human"]):
+            st.write(user_input)
+
+        # Render the AI response
+        with st.chat_message("AI", avatar=avatars["AI"]):
+            try:
+                with st.spinner(" "):
+                    response = st.write_stream(
+                        st.session_state["runnable"].stream(
+                            {"user_query": user_query_location}
+                        )
+                    )
+            except openai.BadRequestError:
+                handle_errors()
+            except Exception:
+                handle_errors()
+
+        # Add AI response to the message history
+        st.session_state["chat_history"].append(AIMessage(content=response))
+
+        # Update runnable memory
+        st.session_state["memory"].chat_memory.add_user_message(
+            HumanMessage(content=user_query_location)
+        )
+        st.session_state["memory"].chat_memory.add_ai_message(
+            AIMessage(content=response)
+        )

requirements.txt

@@ -4,4 +4,5 @@ langchain-openai==0.1.19
 langchain-qdrant==0.1.3
 fastembed==0.3.4
 streamlit==1.32.2
-streamlit-feedback==0.1.3
+streamlit-feedback==0.1.3
+flashrank==0.2.8