Update app.py

app.py

@@ -1,133 +1,133 @@
-import streamlit as st
-from langchain_core.messages import AIMessage, HumanMessage
-from langchain_openai import ChatOpenAI
-
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate
-
-# from langchain.chat_models import AzureChatOpenAI
-from langchain_openai import AzureChatOpenAI
-from langchain.schema import HumanMessage, SystemMessage
-from langchain_core.prompts.chat import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
-from azure_openai import qt
-from retriver import search_and_reconstruct
-# Initialize an instance of AzureOpenAI using the specified settings
-import pandas as pd
-
-
-# LLM Langchain Definition
-OPENAI_API_KEY = "86b631a9c0294e9698e327c59ff5ac2c"
-OPENAI_API_TYPE = "azure"
-OPENAI_API_BASE = "https://davidfearn-gpt4.openai.azure.com"
-# OPENAI_API_VERSION = "2024-02-01"
-OPENAI_API_VERSION = "2024-08-01-preview"
-# OPENAI_MODEL = "gpt4-turbo-1106"
-OPENAI_MODEL = "gpt-4o"
-# Initialize an instance of AzureOpenAI using the specified settings
-
-
-def get_response(chat_history, qte, knowledge, temp1, temp2, tokens1, tokens2, persona2SystemMessage, persona2UserMessage):
-
-    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=temp2, 
-        max_tokens=tokens2
-            # Name of the deployment for identification
-    )
-
-
-    system_message_template = SystemMessagePromptTemplate.from_template("your are a helpful ai")
-    human_message_template = HumanMessagePromptTemplate.from_template("try and answer the questions {history}, knowledge {knowledge}")
-    
-    # Create a chat prompt template combining system and human messages
-    prompt = ChatPromptTemplate.from_messages([system_message_template, human_message_template])
-
-    chain = prompt | llm | StrOutputParser()
-    
-    return chain.stream({
-        "history": chat_history,
-        "knowledge": knowledge
-    })
-
-placeHolderPersona1 = "place holder"
-
-# app config
-st.set_page_config(page_title="Reg Intel Chatbot", page_icon="🤖")
-st.title("Reg Intel Toolbox :toolbox:")
-
-# Sidebar for inputting personas
-st.sidebar.title("RAG System Designer")
-# st.sidebar.subheader("Welcome Message")
-# welcomeMessage = st.sidebar.text_area("Define Intake Persona", value=welcomeMessage, height=300)
-st.sidebar.header("Query Designer Config")
-# numberOfQuestions = st.sidebar.slider("Number of Questions", min_value=0, max_value=10, step=1, value=5, key='persona1_questions')
-persona1SystemMessage = st.sidebar.text_area("Query Designer System Message", value=placeHolderPersona1, height=300)
-temp1 = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, step=0.1, value=0.6, key='persona1_temp')
-tokens1 = st.sidebar.slider("Tokens", min_value=0, max_value=4000, step=100, value=500, key='persona1_tokens')
-st.sidebar.subheader("Number of Search Results")
-k = st.sidebar.slider("Returned Docs", min_value=1, max_value=10, step=1, value=3, key='k')
-
-st.sidebar.header("Engineered Prompt Config")
-persona2SystemMessage = st.sidebar.text_area("Answer Creation System Message", value=placeHolderPersona1, height=300)
-persona2UserMessage = st.sidebar.text_area("Answer Creation User Message", value=placeHolderPersona1, height=300)
-temp2 = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, step=0.1, value=0.6, key='persona2_temp')
-tokens2 = st.sidebar.slider("Tokens", min_value=0, max_value=4000, step=100, value=500, key='persona2_tokens')
-
-
-# session state
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = [
-        AIMessage(content="Hello, I am the GSK Reg Intel Assistant. How can I help you?"),
-    ]
-
-
-# conversation
-for message in st.session_state.chat_history:
-    if isinstance(message, AIMessage):
-        with st.chat_message("AI"):
-            st.write(message.content)
-    elif isinstance(message, HumanMessage):
-        with st.chat_message("Human"):
-            st.write(message.content)
-
-
-# user input
-user_query = st.chat_input("Type your message here...")
-if user_query is not None and user_query != "":
-    st.session_state.chat_history.append(HumanMessage(content=user_query))
-
-    with st.chat_message("Human"):
-        st.markdown(user_query)
-
-    with st.chat_message("AI"):
-        qte = qt(persona1SystemMessage, st.session_state.chat_history, temp1, tokens1)
-        knowledge = search_and_reconstruct(qte, k)
-        response = st.write_stream(get_response(st.session_state.chat_history, qte, knowledge, temp1, temp2, tokens1, tokens2, persona2SystemMessage, persona2UserMessage))
-
-    st.session_state.chat_history.append(AIMessage(content=response))
-    st.sidebar.header("QTE and Knowledge Results")
-    st.sidebar.header("QTE")
-    st.sidebar.text(qte)
-
-    if knowledge:
-        # Prepare the data for the table
-        table_data = {
-            "Title": [entry['Title'] for entry in knowledge],
-            "Score": [entry.get('Score', 'N/A') for entry in knowledge],
-            "Page Number": [entry['PageNumber'] for entry in knowledge],
-            # "Grounding Text": [entry['ReconstructedText'] for entry in knowledge]
-        }
-
-        # Create a dataframe for displaying as a table
-    
-        df = pd.DataFrame(table_data)
-
-        # Display the table in the sidebar
-        st.sidebar.write("### Knowledge Base Results")
-        st.sidebar.dataframe(df)  # Adjust height as needed
-    else:
+import streamlit as st
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_openai import ChatOpenAI
+
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+
+# from langchain.chat_models import AzureChatOpenAI
+from langchain_openai import AzureChatOpenAI
+from langchain.schema import HumanMessage, SystemMessage
+from langchain_core.prompts.chat import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
+from azure_openai import qt
+from retriver import search_and_reconstruct
+# Initialize an instance of AzureOpenAI using the specified settings
+import pandas as pd
+
+
+# LLM Langchain Definition
+OPENAI_API_KEY = "86b631a9c0294e9698e327c59ff5ac2c"
+OPENAI_API_TYPE = "azure"
+OPENAI_API_BASE = "https://davidfearn-gpt4.openai.azure.com"
+# OPENAI_API_VERSION = "2024-02-01"
+OPENAI_API_VERSION = "2024-08-01-preview"
+# OPENAI_MODEL = "gpt4-turbo-1106"
+OPENAI_MODEL = "gpt-4o"
+# Initialize an instance of AzureOpenAI using the specified settings
+
+
+def get_response(chat_history, qte, knowledge, temp1, temp2, tokens1, tokens2, persona2SystemMessage, persona2UserMessage):
+
+    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=temp2, 
+        max_tokens=tokens2
+            # Name of the deployment for identification
+    )
+
+
+    system_message_template = SystemMessagePromptTemplate.from_template("your are a helpful ai")
+    human_message_template = HumanMessagePromptTemplate.from_template("try and answer the questions {history}, knowledge {knowledge}")
+    
+    # Create a chat prompt template combining system and human messages
+    prompt = ChatPromptTemplate.from_messages([system_message_template, human_message_template])
+
+    chain = prompt | llm | StrOutputParser()
+    
+    return chain.stream({
+        "history": chat_history,
+        "knowledge": knowledge
+    })
+
+placeHolderPersona1 = "place holder"
+
+# app config
+st.set_page_config(page_title="Reg Intel Chatbot", page_icon="🤖")
+st.title("Reg Intel Toolbox :toolbox:")
+
+# Sidebar for inputting personas
+st.sidebar.title("RAG System Designer")
+# st.sidebar.subheader("Welcome Message")
+# welcomeMessage = st.sidebar.text_area("Define Intake Persona", value=welcomeMessage, height=300)
+st.sidebar.header("Query Designer Config")
+# numberOfQuestions = st.sidebar.slider("Number of Questions", min_value=0, max_value=10, step=1, value=5, key='persona1_questions')
+persona1SystemMessage = st.sidebar.text_area("Query Designer System Message", value=placeHolderPersona1, height=300)
+temp1 = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, step=0.1, value=0.6, key='persona1_temp')
+tokens1 = st.sidebar.slider("Tokens", min_value=0, max_value=4000, step=100, value=500, key='persona1_tokens')
+st.sidebar.subheader("Number of Search Results")
+k = st.sidebar.slider("Returned Docs", min_value=1, max_value=10, step=1, value=3, key='k')
+
+st.sidebar.header("Engineered Prompt Config")
+persona2SystemMessage = st.sidebar.text_area("Answer Creation System Message", value=placeHolderPersona1, height=300)
+persona2UserMessage = st.sidebar.text_area("Answer Creation User Message", value=placeHolderPersona1, height=300)
+temp2 = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, step=0.1, value=0.6, key='persona2_temp')
+tokens2 = st.sidebar.slider("Tokens", min_value=0, max_value=4000, step=100, value=500, key='persona2_tokens')
+
+
+# session state
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = [
+        AIMessage(content="Hello, I am the GSK Reg Intel Assistant. How can I help you?"),
+    ]
+
+
+# conversation
+for message in st.session_state.chat_history:
+    if isinstance(message, AIMessage):
+        with st.chat_message("AI"):
+            st.write(message.content)
+    elif isinstance(message, HumanMessage):
+        with st.chat_message("Human"):
+            st.write(message.content)
+
+
+# user input
+user_query = st.chat_input("Type your message here...")
+if user_query is not None and user_query != "":
+    st.session_state.chat_history.append(HumanMessage(content=user_query))
+
+    with st.chat_message("Human"):
+        st.markdown(user_query)
+
+    with st.chat_message("AI"):
+        qte = qt(persona1SystemMessage, st.session_state.chat_history, temp1, tokens1)
+        knowledge = search_and_reconstruct(qte, k)
+        response = st.write_stream(get_response(st.session_state.chat_history, qte, knowledge, temp1, temp2, tokens1, tokens2, persona2SystemMessage, persona2UserMessage))
+
+    st.session_state.chat_history.append(AIMessage(content=response))
+    st.sidebar.header("QTE and Knowledge Results")
+    st.sidebar.header("QTE")
+    st.sidebar.text(qte)
+
+    if knowledge:
+        # Prepare the data for the table
+        table_data = {
+            "Title": [entry['Title'] for entry in knowledge],
+            "Score (%)": [f"{int(entry.get('Score', 0) * 100)}%" for entry in knowledge],  # Convert to percentage and remove decimals
+            "Page": [entry['PageNumber'] for entry in knowledge]
+            # "Grounding Text": [entry['ReconstructedText'] for entry in knowledge]
+        }
+
+        # Create a dataframe for displaying as a table
+    
+        df = pd.DataFrame(table_data)
+
+        # Display the table in the sidebar
+        st.sidebar.write("### Knowledge Base Results")
+        st.sidebar.dataframe(df)  # Adjust height as needed
+    else:
         st.sidebar.write("No relevant knowledge base results found.")