Update app.py

app.py

@@ -1,14 +1,19 @@
 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
 
+client = Client()
+
 # Streamlit page configuration
-st.set_page_config(page_title="Tall Tree Integrated Health",
+st.set_page_config(page_title="Tall Tree Health",
                    page_icon="💬",
                    layout="centered",
-                   initial_sidebar_state="collapsed")
+                   initial_sidebar_state="expanded")
 
 # Streamlit CSS configuration
 
@@ -37,7 +42,6 @@ def get_chain_and_memory():
         # 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-4-1106-preview", temperature=0.2)
 
     except Exception as e:
@@ -55,6 +59,27 @@ if "history" not in st.session_state:
 if "messages" not in st.session_state:
     st.session_state["messages"] = []
 
+# Add a sidebar
+
+st.sidebar.markdown(
+    """
+    ### Your Feedback Matters!
+    Help us enhance our AI-powered assistant by sharing your feedback.\n\n
+    **Rate the Response Quality:**
+
+    - 😀 **Excellent (Score: 1):** Complete and clear answer.
+    - 🙂 **Good (Score: 0.75):** Helpful, but could be clearer or more detailed.
+    - 😐 **Neutral (Score: 0.5):** Somewhat helpful, partially correct.
+    - 🙁 **Poor (Score: 0.25):** Mostly incorrect or unhelpful.
+    - 😞 **Very Poor (Score: 0):** Completely incorrect or not helpful.
+    
+    Thank you! Let's get started. 🚀
+    """
+)
+
+# Add delimiter between sidebar expander and the welcome message
+st.markdown("###")
+
 # Select locations element into a container
 with st.container(border=False):
     # Set the welcome message
@@ -69,9 +94,8 @@ with st.container(border=False):
         index=None, horizontal=False,
     )
 
-# Add some space between the container and the chat interface
-for _ in range(2):
-    st.markdown("\n\n")
+# Add delimiter between the container and the chat interface
+st.markdown("###")
 
 # Get user input only if a location is selected
 prompt = ""
@@ -108,9 +132,11 @@ if prompt:
         try:
             partial_message = ""
             with st.spinner(" "):
-                for chunk in chain.stream({"message": prompt}):
-                    partial_message += chunk
-                    message_placeholder.markdown(partial_message + "|")
+                with collect_runs() as cb:  # Collect runs for feedback in langsmith
+                    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
         except openai.BadRequestError:
             st.warning(openai_api_error_message, icon="🙁")
             st.stop()
@@ -128,3 +154,45 @@ if prompt:
         # 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
+feedback_option = "faces"
+
+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.")