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import streamlit as st
from utils import final_function
qa, memory = final_function()
import streamlit as st
import io
import re
import sys
from typing import Any, Callable
def capture_and_display_output(func: Callable[..., Any], args, **kwargs) -> Any:
# Capture the standard output
original_stdout = sys.stdout
sys.stdout = output_catcher = io.StringIO()
# Run the given function and capture its output
response = func(args, **kwargs)
# Reset the standard output to its original value
sys.stdout = original_stdout
# Clean the captured output
output_text = output_catcher.getvalue()
clean_text = re.sub(r"\x1b[.?[@-~]", "", output_text)
# Custom CSS for the response box
st.markdown("""
<style>
.response-value {
border: 2px solid #6c757d;
border-radius: 5px;
padding: 20px;
background-color: #f8f9fa;
color: #3d3d3d;
font-size: 20px; # Change this value to adjust the text size
font-family: monospace;
}
</style>
""", unsafe_allow_html=True)
# Create an expander titled "See Verbose"
with st.expander("See Langchain Thought Process"):
# Display the cleaned text in Streamlit as code
st.code(clean_text)
return response
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
def chat_ui(qa):
# Accept user input
if prompt := st.chat_input(
"Ask me questions: How can I retrieve data from Deep Lake in Langchain?"
):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
# Load the memory variables, which include the chat history
memory_variables = memory.load_memory_variables({})
# Predict the AI's response in the conversation
with st.spinner("Searching course material"):
response = capture_and_display_output(
qa, ({"question": prompt, "chat_history": memory_variables})
)
# Display chat response
full_response += response["answer"]
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
#Display top 2 retrieved sources
source = response["source_documents"][0].metadata
source2 = response["source_documents"][1].metadata
with st.expander("See Resources"):
st.write(f"Title: {source['title'].split('·')[0].strip()}")
st.write(f"Source: {source['source']}")
st.write(f"Relevance to Query: {source['relevance_score'] * 100}%")
st.write(f"Title: {source2['title'].split('·')[0].strip()}")
st.write(f"Source: {source2['source']}")
st.write(f"Relevance to Query: {source2['relevance_score'] * 100}%")
# Append message to session state
st.session_state.messages.append(
{"role": "assistant", "content": full_response}
)
# Run function passing the ConversationalRetrievalChain
chat_ui(qa)
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