Update app.py

app.py

@@ -1,131 +1,130 @@
-import streamlit as st
-from langchain.chat_models import ChatOpenAI
-from langchain_openai import AzureChatOpenAI
-from langchain.schema import AIMessage, HumanMessage, SystemMessage
-from langchain.callbacks.base import BaseCallbackHandler
-from typing import Any
-from prompts import dental_system_message, welcome_message, update
-
-OPENAI_API_KEY = "86b631a9c0294e9698e327c59ff5ac2c"
-OPENAI_API_TYPE = "azure"
-OPENAI_API_BASE = "https://davidfearn-gpt4.openai.azure.com"
-OPENAI_API_VERSION = "2024-08-01-preview"
-OPENAI_MODEL = "gpt-4o"
-
-
-# --- Page Config ---
-st.set_page_config(page_title="Home Dental Expert", page_icon="🦷")
-st.subheader("🦷 Home Dental Expert")
-
-haleon_system_message = SystemMessage(content=dental_system_message)
-
-from langchain.schema import BaseMessage
-
-def get_llm_response_with_context(messages: list[BaseMessage], stream_container):
- 
-
-    llm = AzureChatOpenAI(
-
-        openai_api_version=OPENAI_API_VERSION,
-        openai_api_key=OPENAI_API_KEY,
-        azure_endpoint=OPENAI_API_BASE,
-        openai_api_type=OPENAI_API_TYPE,
-        deployment_name=OPENAI_MODEL,
-        temperature=0.7, 
-        streaming=True,
-        callbacks=[StreamlitCallbackHandler(stream_container)],
-    )
-
-    full_convo = [haleon_system_message] + messages
-    assert all(isinstance(m, BaseMessage) for m in full_convo), "One or more messages is not a BaseMessage type"
-    return llm(full_convo)
-
-# --- Streaming Output Handler ---
-class StreamlitCallbackHandler(BaseCallbackHandler):
-    def __init__(self, container):
-        self.container = container
-        self.text = ""
-
-    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        self.text += token
-        self.container.markdown(self.text + "▌")
-
-# --- Session State Init ---
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-if "greeted" not in st.session_state:
-    st.session_state.greeted = False
-
-if "welcome_response" not in st.session_state:
-    st.session_state.welcome_response = None
-
-# --- Display welcome message at top if it exists ---
-if st.session_state.welcome_response:
-    with st.chat_message("assistant"):
-        st.markdown(st.session_state.welcome_response)
-
-from datetime import datetime
-
-def get_time_of_day():
-    """Returns 'morning' if before 12pm, 'evening' if after 5pm, otherwise 'afternoon'."""
-    now = datetime.now().hour
-    if now < 12:
-        return "morning"
-    elif now < 17:
-        return "afternoon"
-    else:
-        return "evening"
-
-
-
-# --- Stream the welcome message only once ---
-if not st.session_state.greeted:
-    with st.chat_message("assistant"):
-        stream_container = st.empty()
-
-        time_of_day = get_time_of_day()
-        latest_update = update
-        
-        llm = AzureChatOpenAI(
-        openai_api_version=OPENAI_API_VERSION,
-        openai_api_key=OPENAI_API_KEY,
-        azure_endpoint=OPENAI_API_BASE,
-        openai_api_type=OPENAI_API_TYPE,
-        deployment_name=OPENAI_MODEL,
-        temperature=0.7, 
-        streaming=True,
-        callbacks=[StreamlitCallbackHandler(stream_container)]
-        )
-
-        messages = [
-                haleon_system_message,
-                HumanMessage(content=f"Give a warm, friendly greeting specific to the time of day being the {time_of_day} as if the user just opened the daily dental journaling app. They have used this app before, so you can assume they are familiar with it. This app is designed to be specically before or after they brush their teeth. Also comment on the latest update as this will be the focus for todays discussion: {latest_update}. Ask a follow up question. DON'T USE #Hash #Tags. Feel free to use emojis."),
-        ]
-
-        response = llm(messages)
-  
-        # Save it to show above later
-        st.session_state.welcome_response = response.content
-        st.session_state.greeted = True
-
-# --- Chat History Display ---
-for msg in st.session_state.messages:
-    with st.chat_message("user" if isinstance(msg, HumanMessage) else "assistant"):
-        st.markdown(msg.content)
-
-# --- Chat Input ---
-user_input = st.chat_input("Type your message...")
-
-# --- On User Message ---
-if user_input:
-    st.chat_message("user").markdown(user_input)
-    st.session_state.messages.append(HumanMessage(content=user_input))
-
-    with st.chat_message("assistant"):
-        stream_container = st.empty()
-
-        response = get_llm_response_with_context(st.session_state.messages, stream_container)
-
-        st.session_state.messages.append(AIMessage(content=response.content))
-        stream_container.markdown(response.content)
+import streamlit as st
+from langchain_openai import AzureChatOpenAI
+from langchain.schema import AIMessage, HumanMessage, SystemMessage
+from langchain.callbacks.base import BaseCallbackHandler
+from typing import Any
+from prompts import dental_system_message, welcome_message, update
+
+OPENAI_API_KEY = "86b631a9c0294e9698e327c59ff5ac2c"
+OPENAI_API_TYPE = "azure"
+OPENAI_API_BASE = "https://davidfearn-gpt4.openai.azure.com"
+OPENAI_API_VERSION = "2024-08-01-preview"
+OPENAI_MODEL = "gpt-4o"
+
+
+# --- Page Config ---
+st.set_page_config(page_title="Home Dental Expert", page_icon="🦷")
+st.subheader("🦷 Home Dental Expert")
+
+haleon_system_message = SystemMessage(content=dental_system_message)
+
+from langchain.schema import BaseMessage
+
+def get_llm_response_with_context(messages: list[BaseMessage], stream_container):
+ 
+
+    llm = AzureChatOpenAI(
+
+        openai_api_version=OPENAI_API_VERSION,
+        openai_api_key=OPENAI_API_KEY,
+        azure_endpoint=OPENAI_API_BASE,
+        openai_api_type=OPENAI_API_TYPE,
+        deployment_name=OPENAI_MODEL,
+        temperature=0.7, 
+        streaming=True,
+        callbacks=[StreamlitCallbackHandler(stream_container)],
+    )
+
+    full_convo = [haleon_system_message] + messages
+    assert all(isinstance(m, BaseMessage) for m in full_convo), "One or more messages is not a BaseMessage type"
+    return llm(full_convo)
+
+# --- Streaming Output Handler ---
+class StreamlitCallbackHandler(BaseCallbackHandler):
+    def __init__(self, container):
+        self.container = container
+        self.text = ""
+
+    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        self.text += token
+        self.container.markdown(self.text + "▌")
+
+# --- Session State Init ---
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "greeted" not in st.session_state:
+    st.session_state.greeted = False
+
+if "welcome_response" not in st.session_state:
+    st.session_state.welcome_response = None
+
+# --- Display welcome message at top if it exists ---
+if st.session_state.welcome_response:
+    with st.chat_message("assistant"):
+        st.markdown(st.session_state.welcome_response)
+
+from datetime import datetime
+
+def get_time_of_day():
+    """Returns 'morning' if before 12pm, 'evening' if after 5pm, otherwise 'afternoon'."""
+    now = datetime.now().hour
+    if now < 12:
+        return "morning"
+    elif now < 17:
+        return "afternoon"
+    else:
+        return "evening"
+
+
+
+# --- Stream the welcome message only once ---
+if not st.session_state.greeted:
+    with st.chat_message("assistant"):
+        stream_container = st.empty()
+
+        time_of_day = get_time_of_day()
+        latest_update = update
+        
+        llm = AzureChatOpenAI(
+        openai_api_version=OPENAI_API_VERSION,
+        openai_api_key=OPENAI_API_KEY,
+        azure_endpoint=OPENAI_API_BASE,
+        openai_api_type=OPENAI_API_TYPE,
+        deployment_name=OPENAI_MODEL,
+        temperature=0.7, 
+        streaming=True,
+        callbacks=[StreamlitCallbackHandler(stream_container)]
+        )
+
+        messages = [
+                haleon_system_message,
+                HumanMessage(content=f"Give a warm, friendly greeting specific to the time of day being the {time_of_day} as if the user just opened the daily dental journaling app. They have used this app before, so you can assume they are familiar with it. This app is designed to be specically before or after they brush their teeth. Also comment on the latest update as this will be the focus for todays discussion: {latest_update}. Ask a follow up question. DON'T USE #Hash #Tags. Feel free to use emojis."),
+        ]
+
+        response = llm(messages)
+  
+        # Save it to show above later
+        st.session_state.welcome_response = response.content
+        st.session_state.greeted = True
+
+# --- Chat History Display ---
+for msg in st.session_state.messages:
+    with st.chat_message("user" if isinstance(msg, HumanMessage) else "assistant"):
+        st.markdown(msg.content)
+
+# --- Chat Input ---
+user_input = st.chat_input("Type your message...")
+
+# --- On User Message ---
+if user_input:
+    st.chat_message("user").markdown(user_input)
+    st.session_state.messages.append(HumanMessage(content=user_input))
+
+    with st.chat_message("assistant"):
+        stream_container = st.empty()
+
+        response = get_llm_response_with_context(st.session_state.messages, stream_container)
+
+        st.session_state.messages.append(AIMessage(content=response.content))
+        stream_container.markdown(response.content)