Upload app.py

app.py

@@ -1,173 +1,186 @@
-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 rag_chain.chain import get_rag_chain
-
-# Langsmith client for the feedback system
-client = Client()
-
-# Streamlit page configuration
-st.set_page_config(page_title="Tall Tree Health",
-                   page_icon="💬",
-                   layout="centered",
-                   initial_sidebar_state="expanded")
-
-# Streamlit CSS configuration
-
-with open("styles/styles.css") as css:
-    st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)
-
-# Error message template
-base_error_message = (
-    "Something went wrong while processing your request. "
-    "Please refresh the page or try again later.\n\n"
-    "If the error persists, please contact us at "
-    "[Tall Tree Health](https://www.talltreehealth.ca/contact-us)."
-)
-
-# Get chain and memory
-
-
-@st.cache_resource(ttl="5d", show_spinner=False)
-def get_chain_and_memory():
-    try:
-        # gpt-4 points to gpt-4-0613
-        # gpt-4-turbo-preview points to gpt-4-0125-preview
-        # Fine-tuned: ft:gpt-3.5-turbo-1106:tall-tree::8mAkOSED
-        # gpt-4-1106-preview
-        return get_rag_chain(model_name="gpt-4-turbo", temperature=0.2)
-
-    except Exception as e:
-        st.warning(base_error_message, icon="🙁")
-        st.stop()
-
-
-chain, memory = get_chain_and_memory()
-
-# Set up session state and 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()
-
-if "messages" not in st.session_state:
-    st.session_state["messages"] = []
-
-# Select locations element into a container
-with st.container(border=False):
-    # Set the welcome message
-    st.markdown(
-        "\n\nHello there! 👋 Need help finding the right service or practitioner? Let our AI-powered assistant give you a hand.\n\n"
-        "To get started, please select your preferred location and share details about your symptoms or needs. "
-    )
-    location = st.radio(
-        "**Our Locations**:",
-        ["Cordova Bay - Victoria", "James Bay - Victoria",
-            "Commercial Drive - Vancouver"],
-        index=None, horizontal=False,
-    )
-    st.write("\n")
-
-# Get user input only if a location is selected
-prompt = ""
-if location:
-    user_input = st.chat_input("Enter your message...")
-    if user_input:
-        st.session_state["messages"].append(
-            ChatMessage(role="user", content=user_input))
-        prompt = f"{user_input}\nLocation: {location}"
-
-
-# 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(base_error_message, icon="🙁")
-            st.stop()
-        except Exception as e:
-            st.warning(base_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))
-
-        # Add the full response to the message history
-        st.session_state["messages"].append(ChatMessage(
-            role="assistant", content=partial_message))
-
-
-# Feedback system using streamlit feedback and Langsmith
-
-# Get the feedback option
-feedback_option = "thumbs"
-
-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},
-    }
-
-    # Get the score mapping based on the selected feedback option
-    scores = score_mappings[feedback_option]
-
-    if feedback:
-        # Get the score from the selected feedback option's score mapping
-        score = scores.get(feedback["score"])
-
-        if score is not None:
-            # Formulate feedback type string incorporating the feedback option
-            # and score value
-            feedback_type_str = f"{feedback_option} {feedback['score']}"
-
-            # Record the feedback with the formulated feedback type string
-            feedback_record = 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.")
+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 rag.runnable import get_runnable
+from utils.error_message_template import ERROR_MESSAGE
+
+# Streamlit page configuration
+st.set_page_config(
+    page_title="ELLA AI Assistant",
+    page_icon="💬",
+    layout="centered",
+    initial_sidebar_state="collapsed",
+)
+
+# Streamlit CSS configuration
+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-4o", 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()
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+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
+
+
+# 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. "
+    )
+
+
+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))
+
+        # Add the full response to the message history
+        st.session_state["messages"].append(
+            ChatMessage(role="assistant", content=partial_message)
+        )
+
+
+# Feedback system using streamlit-feedback and Langsmith
+
+# Langsmith client for the feedback system
+ls_client = Client()
+
+# Feedback option
+feedback_option = "thumbs"
+
+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},
+    }
+
+    # Get the score mapping based on the selected feedback option
+    scores = score_mappings[feedback_option]
+
+    if feedback:
+        # Get the score from the selected feedback option's score mapping
+        score = scores.get(feedback["score"])
+
+        if score is not None:
+            # Formulate feedback type string incorporating the feedback option
+            # and score value
+            feedback_type_str = f"{feedback_option} {feedback['score']}"
+
+            # 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.")