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import streamlit as st
from pathlib import Path
from data_preprocessing import process_docs
from rag import create_rag_chain
import time
import pandas as pd
import os
from datetime import datetime
# Feedback storage setup
FEEDBACK_FILE = "feedback.csv"
if not os.path.exists(FEEDBACK_FILE):
pd.DataFrame(columns=["timestamp", "query", "response", "rating"]).to_csv(FEEDBACK_FILE, index=False)
def save_feedback(query, response, rating):
feedback = {
"timestamp": datetime.now().isoformat(),
"query": query,
"response": response,
"rating": rating
}
pd.DataFrame([feedback]).to_csv(FEEDBACK_FILE, mode="a", header=False, index=False)
def response_generator(prompt, chain):
response = chain.invoke(prompt)
for word in response.split():
yield word + " "
time.sleep(0.05)
# File handling setup
save_directory = "docs"
save_path = "docs/file.pdf"
Path(save_directory).mkdir(parents=True, exist_ok=True)
st.title("📝 InsureAgent")
with st.sidebar:
uploaded_file = st.file_uploader("Upload a document", type=("pdf"))
if uploaded_file is not None:
with open(save_path, "wb") as f:
f.write(uploaded_file.getbuffer())
st.success(f"File saved successfully: {save_path}")
# Show feedback data toggle
show_feedback = st.checkbox("Show feedback data")
# Process documents
retriever = process_docs(save_path)
chain, chain_with_sources = create_rag_chain(retriever)
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages
for idx, message in enumerate(st.session_state.messages):
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Add rating buttons for assistant messages
if message["role"] == "assistant":
if "rating" not in message:
col1, col2 = st.columns(2)
with col1:
if st.button("👍 Good", key=f"good_{idx}"):
message["rating"] = "good"
query = st.session_state.messages[idx-1]["content"]
save_feedback(query, message["content"], "good")
st.rerun()
with col2:
if st.button("👎 Bad", key=f"bad_{idx}"):
message["rating"] = "bad"
query = st.session_state.messages[idx-1]["content"]
save_feedback(query, message["content"], "bad")
st.rerun()
else:
st.write(f"Rated: {message['rating'].capitalize()}")
# Show feedback data in sidebar if enabled
if show_feedback:
st.sidebar.subheader("User Feedback")
try:
feedback_df = pd.read_csv(FEEDBACK_FILE)
st.sidebar.dataframe(feedback_df)
# Download button for feedback data
csv = feedback_df.to_csv(index=False).encode('utf-8')
st.sidebar.download_button(
label="Download feedback as CSV",
data=csv,
file_name="feedback_data.csv",
mime="text/csv"
)
except FileNotFoundError:
st.sidebar.warning("No feedback data yet")
# Chat input and processing
if prompt := st.chat_input("Ask about your insurance document:"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message
with st.chat_message("user"):
st.markdown(prompt)
# Generate and display assistant response
with st.chat_message("assistant"):
response = st.write_stream(response_generator(prompt, chain))
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response}) |