working

.pre-commit-config.yaml

@@ -48,10 +48,10 @@ repos:
     rev: v3.1.0
     hooks:
     -   id: add-trailing-comma
--   repo: https://github.com/dannysepler/rm_unneeded_f_str
-    rev: v0.2.0
-    hooks:
-    -   id: rm-unneeded-f-str
+#-   repo: https://github.com/dannysepler/rm_unneeded_f_str
+#    rev: v0.2.0
+#    hooks:
+#    -   id: rm-unneeded-f-str
 -   repo: https://github.com/psf/black
     rev: 23.9.1
     hooks:

.streamlit/config.toml

@@ -0,0 +1,6 @@
+[theme]
+primaryColor="#F63366"
+backgroundColor="#FFFFFF"
+secondaryBackgroundColor="#F0F2F6"
+textColor="#262730"
+font="sans serif"

AI_chatbot/app.py

@@ -0,0 +1,203 @@
+from datetime import datetime
+
+import streamlit as st
+from langchain import LLMChain
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.callbacks.tracers.langchain import wait_for_all_tracers
+from langchain.callbacks.tracers.run_collector import RunCollectorCallbackHandler
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import StreamlitChatMessageHistory, ConversationBufferMemory
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.schema.runnable import RunnableConfig
+from langsmith import Client
+from streamlit_feedback import streamlit_feedback
+
+st.set_page_config(
+    page_title="Chat LangSmith",
+    page_icon="🦜",
+)
+
+
+def get_llm_chain(system_prompt: str, memory: ConversationBufferMemory) -> LLMChain:
+    """Return a basic LLMChain with memory."""
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                system_prompt + "\nIt's currently {time}.",
+            ),
+            MessagesPlaceholder(variable_name="chat_history"),
+            ("human", "{input}"),
+        ],
+    ).partial(time=lambda: str(datetime.now()))
+    llm = ChatOpenAI(temperature=0.7, streaming=True)
+    return LLMChain(prompt=prompt, llm=llm, memory=memory)
+
+
+client = Client()
+
+
+# "# Chat🦜🛠️"
+# Initialize State
+if "trace_link" not in st.session_state:
+    st.session_state.trace_link = None
+if "run_id" not in st.session_state:
+    st.session_state.run_id = None
+st.sidebar.markdown(
+    """
+# Menu
+""",
+)
+
+_DEFAULT_SYSTEM_PROMPT = "You are a helpful chatbot."
+
+system_prompt = st.sidebar.text_area(
+    "Custom Instructions",
+    _DEFAULT_SYSTEM_PROMPT,
+    help="Custom instructions to provide the language model to determine style, personality, etc.",
+)
+system_prompt = system_prompt.strip().replace("{", "{{").replace("}", "}}")
+memory = ConversationBufferMemory(
+    chat_memory=StreamlitChatMessageHistory(key="langchain_messages"),
+    return_messages=True,
+    memory_key="chat_history",
+)
+
+chain = get_llm_chain(system_prompt, memory)
+
+if st.sidebar.button("Clear message history"):
+    print("Clearing message history")
+    memory.clear()
+    st.session_state.trace_link = None
+    st.session_state.run_id = None
+
+
+# Display chat messages from history on app rerun
+# NOTE: This won't be necessary for Streamlit 1.26+, you can just pass the type directly
+# https://github.com/streamlit/streamlit/pull/7094
+def _get_openai_type(msg):
+    if msg.type == "human":
+        return "user"
+    if msg.type == "ai":
+        return "assistant"
+    return msg.role if msg.type == "chat" else msg.type
+
+
+for msg in st.session_state.langchain_messages:
+    streamlit_type = _get_openai_type(msg)
+    avatar = "🦜" if streamlit_type == "assistant" else None
+    with st.chat_message(streamlit_type, avatar=avatar):
+        st.markdown(msg.content)
+
+if st.session_state.trace_link:
+    st.sidebar.markdown(
+        f'<a href="{st.session_state.trace_link}" target="_blank"><button>Latest Trace: 🛠️</button></a>',
+        unsafe_allow_html=True,
+    )
+
+
+class StreamHandler(BaseCallbackHandler):
+    def __init__(self, container, initial_text=""):
+        self.container = container
+        self.text = initial_text
+
+    def on_llm_new_token(self, token: str, **kwargs) -> None:
+        self.text += token
+        self.container.markdown(self.text)
+
+
+run_collector = RunCollectorCallbackHandler()
+
+
+def _reset_feedback():
+    st.session_state.feedback_update = None
+    st.session_state.feedback = None
+
+
+if prompt := st.chat_input(placeholder="Ask me a question!"):
+    st.chat_message("user").write(prompt)
+    _reset_feedback()
+
+    with st.chat_message("assistant", avatar="🦜"):
+        message_placeholder = st.empty()
+        stream_handler = StreamHandler(message_placeholder)
+        runnable_config = RunnableConfig(
+            callbacks=[run_collector, stream_handler],
+            tags=["Streamlit Chat"],
+        )
+        full_response = chain.invoke({"input": prompt}, config=runnable_config)["text"]
+        message_placeholder.markdown(full_response)
+
+        run = run_collector.traced_runs[0]
+        run_collector.traced_runs = []
+        st.session_state.run_id = run.id
+        wait_for_all_tracers()
+        url = client.read_run(run.id).url
+        st.session_state.trace_link = url
+
+# Simple feedback section
+# Optionally add a thumbs up/down button for feedback
+if st.session_state.get("run_id"):
+    feedback = streamlit_feedback(
+        feedback_type="thumbs",
+        key=f"feedback_{st.session_state.run_id}",
+    )
+    scores = {"👍": 1, "👎": 0}
+    if feedback:
+        score = scores[feedback["score"]]
+        feedback = client.create_feedback(
+            st.session_state.run_id,
+            "user_score",
+            score=score,
+        )
+        st.session_state.feedback = {"feedback_id": str(feedback.id), "score": score}
+
+# Prompt for more information, if feedback was submitted
+if st.session_state.get("feedback"):
+    feedback = st.session_state.get("feedback")
+    feedback_id = feedback["feedback_id"]
+    score = feedback["score"]
+    if score == 0:
+        if correction := st.text_input(
+            label="What would the correct or preferred response have been?",
+            key=f"correction_{feedback_id}",
+        ):
+            st.session_state.feedback_update = {
+                "correction": {"desired": correction},
+                "feedback_id": feedback_id,
+            }
+    elif score == 1:
+        if comment := st.text_input(
+            label="Anything else you'd like to add about this response?",
+            key=f"comment_{feedback_id}",
+        ):
+            st.session_state.feedback_update = {
+                "comment": comment,
+                "feedback_id": feedback_id,
+            }
+# Update the feedback if additional information was provided
+if st.session_state.get("feedback_update"):
+    feedback_update = st.session_state.get("feedback_update")
+    feedback_id = feedback_update.pop("feedback_id")
+    client.update_feedback(feedback_id, **feedback_update)
+    # Clear the comment or correction box
+    _reset_feedback()
+
+# # Feedback section
+# if st.session_state.get("last_run"):
+#     run_url = client.read_run(st.session_state.last_run).url
+#     st.sidebar.markdown(f"[Latest Trace: 🛠️]({run_url})")
+#     feedback = streamlit_feedback(
+#         feedback_type="faces",
+#         optional_text_label="[Optional] Please provide an explanation",
+#         key=f"feedback_{st.session_state.last_run}",
+#     )
+#     if feedback:
+#         scores = {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0}
+#         client.create_feedback(
+#             st.session_state.last_run,
+#             feedback["type"],
+#             score=scores[feedback["score"]],
+#             comment=feedback.get("text", None),
+#         )
+#         st.toast("Feedback recorded!", icon="📝")

Dockerfile

@@ -4,7 +4,8 @@ RUN adduser --uid 1001 --disabled-password --gecos '' appuser
 USER 1001
 
 ENV PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1
+    PYTHONUNBUFFERED=1 \
+    PATH="/home/appuser/.local/bin:$PATH"
 
 RUN pip install --user --upgrade pip
 COPY ./requirements.txt /home/appuser/app/requirements.txt

docker-compose.yml

@@ -11,4 +11,4 @@ services:
     volumes:
         - .:/home/appuser/app:rw
     working_dir: /home/appuser/app/
-    entrypoint: ["streamlit", "run", "${APP}", "-w"]
+    entrypoint: ["python", "-m", "streamlit", "run", "${APP}", "--server.port", "8000", "--server.enableCORS", "false", "--server.address", "0.0.0.0"]

requirements.txt

@@ -3,4 +3,5 @@ langchain
 langsmith
 openai
 streamlit
+streamlit-feedback
 tiktoken