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"""
This module handles the chat display components and rendering.
"""
import streamlit as st
import os
import re
from utils import has_meaningful_content, remove_reasoning_and_sources, clean_explanation, get_image_base64
from session_state import get_full_history
def get_avatars():
"""
Get avatar images for the user and assistant.
Returns:
tuple: (user_avatar, assistant_avatar) - URLs or emoji fallbacks
"""
# Define paths to your company logo images
user_logo_path = "src/assets/icon3.png"
assistant_logo_path = "src/assets/logo.png"
# Get base64 encoded images or use default emoji as fallback
user_logo_base64 = get_image_base64(user_logo_path)
assistant_logo_base64 = get_image_base64(assistant_logo_path)
# Create avatar URLs with base64 data
user_avatar = f"data:image/png;base64,{user_logo_base64}" if user_logo_base64 else "👤"
assistant_avatar = f"data:image/png;base64,{assistant_logo_base64}" if assistant_logo_base64 else "🤖"
return user_avatar, assistant_avatar
def extract_follow_up_questions(content):
"""
Extract follow-up questions from the main content.
Args:
content (str): The message content
Returns:
str: Extracted follow-up questions or empty string if none found
"""
# Look for various forms of follow-up questions sections
patterns = [
r'(?i)follow[ -]?up questions:?\s*(.*?)(?=\n+\s*(?:#{1,3}|reasoning:|sources:|\Z))',
r'(?i)#{1,3}\s*follow[ -]?up questions:?\s*(.*?)(?=\n+\s*(?:#{1,3}|reasoning:|sources:|\Z))',
r'(?i)important questions to ask:?\s*(.*?)(?=\n+\s*(?:#{1,3}|reasoning:|sources:|\Z))',
r'(?i)clarifying questions:?\s*(.*?)(?=\n+\s*(?:#{1,3}|reasoning:|sources:|\Z))',
r'(?i)additional questions:?\s*(.*?)(?=\n+\s*(?:#{1,3}|reasoning:|sources:|\Z))',
r'(?i)please clarify(?:[ a-z]+)?:?\s*(.*?)(?=\n+\s*(?:#{1,3}|reasoning:|sources:|\Z))',
r'(?i)additional information needed:?\s*(.*?)(?=\n+\s*(?:#{1,3}|reasoning:|sources:|\Z))'
]
# Try each pattern until we find a match
for pattern in patterns:
follow_up_match = re.search(pattern, content, re.DOTALL)
if follow_up_match:
follow_up_text = follow_up_match.group(1).strip()
# Check if there's introductory text before a list
intro_text = ""
questions = []
# Look for introductory text before bullet points or numbered list
intro_match = re.match(r'(.*?)(?=\n\s*[-•*]|\n\s*\d+\.)', follow_up_text, re.DOTALL)
if intro_match and intro_match.group(1).strip():
intro_text = intro_match.group(1).strip()
# Handle different list formats
if re.search(r'\n\s*[-•*]', follow_up_text):
# Extract bullet points
bullet_points = re.findall(r'(?:\n|\A)\s*[-•*]\s*(.*?)(?=\n\s*[-•*]|\Z)', follow_up_text, re.DOTALL)
if bullet_points:
questions = [point.strip() for point in bullet_points if point.strip()]
elif re.search(r'\n\s*\d+\.', follow_up_text):
# Extract numbered points
numbered_points = re.findall(r'(?:\n|\A)\s*\d+\.\s*(.*?)(?=\n\s*\d+\.|\Z)', follow_up_text, re.DOTALL)
if numbered_points:
questions = [point.strip() for point in numbered_points if point.strip()]
else:
# If no specific formatting, try to split by lines
lines = follow_up_text.split('\n')
questions = [line.strip() for line in lines if line.strip()]
# If intro text was found, remove it from questions
if intro_text and questions and questions[0] == intro_text:
questions = questions[1:]
# Format output, preserving intro text
result = ""
if intro_text:
result += f"{intro_text}\n\n"
# Format the questions as a numbered list
if questions:
for i, question in enumerate(questions):
# Clean up any existing numbers or bullets
clean_question = re.sub(r'^\s*(?:\d+\.|\-|\•|\*)\s*', '', question)
result += f"{i+1}. {clean_question}\n"
else:
# If no structured questions found, just use the whole text
result += follow_up_text
return result.strip()
return ""
def display_chat_history():
"""
Display the chat history from the database.
"""
user_avatar, assistant_avatar = get_avatars()
# Get full history from database
history = get_full_history()
# Display all messages instead of limiting to a fixed number
display_history = history
for message in display_history:
if message["role"] == "user":
# Right-aligned container for user messages
with st.container():
col1, col2 = st.columns([2, 10])
with col2:
with st.chat_message("user", avatar=user_avatar):
st.write(message["content"])
else:
# Left-aligned container for assistant messages
with st.container():
col1, col2 = st.columns([10, 2])
with col1:
with st.chat_message("assistant", avatar=assistant_avatar):
# Display the response text without reasoning or sources sections
cleaned_response = remove_reasoning_and_sources(message["content"])
st.markdown(cleaned_response)
# Use follow-up questions from the message object directly
if message.get("follow_up_questions") and message["follow_up_questions"].strip():
print(f"Found follow-up questions in message: {message['follow_up_questions']}")
with st.expander("Additional Questions"):
st.markdown(message["follow_up_questions"])
else:
print(f"No follow-up questions found in message keys: {list(message.keys())}")
# Only display the explanation in an expander if it exists AND has actual content
if message.get("explanation") and has_meaningful_content(message.get("explanation")):
# Clean up the explanation text
cleaned_explanation = clean_explanation(message["explanation"])
# Additional cleaning to remove any source information from reasoning
# Remove any sources/references sections
cleaned_explanation = re.sub(r'(?i)(\n+\s*sources:|\n+\s*references:|\n+\s*\*{0,2}sources\*{0,2}:?|\n+\s*\*{0,2}references\*{0,2}:?|\n+\s*#{1,3}\s*sources|\n+\s*#{1,3}\s*references).*', '', cleaned_explanation, flags=re.DOTALL)
# Remove any ## Sources heading and content
cleaned_explanation = re.sub(r'#{1,3}\s+Sources.*', '', cleaned_explanation, flags=re.DOTALL)
with st.expander("Show Reasoning"):
st.markdown(cleaned_explanation)
# Display sources in a separate expander if evidence is available
if message.get("evidence") and len(message.get("evidence", [])) > 0:
with st.expander("Show Sources"):
st.markdown("**Medical sources used:**")
for i, source in enumerate(message.get("evidence", [])):
title = source.get("title", "No title") # Fallback if citation is missing
url = source.get("url", "#")
source_type = source.get("source_type", "Unknown Source")
is_open_access = source.get("is_open_access", False)
access_icon = "🔓 " if is_open_access else ""
citation_text = source.get('citation', title).strip()
markdown_string = (
f"{i+1}. {access_icon}{citation_text}\n"
f" [[Access Article]]({url}) - *{source_type}*"
)
st.markdown(markdown_string)
st.markdown("---")
st.markdown("**Legend:** 🔓 = Open Access (full text available)")
def show_typing_indicator():
"""
Display a typing indicator when the system is processing a response.
"""
if st.session_state.processing:
user_avatar, assistant_avatar = get_avatars()
with st.container():
col1, col2 = st.columns([10, 2])
with col1:
with st.chat_message("assistant", avatar=assistant_avatar):
st.markdown("""
<div class="typing-indicator">
<span></span>
<span></span>
<span></span>
</div>
<p style="margin: 0; color: rgba(255,255,255,0.6) !important; font-size: 0.8rem;">Analyzing your query...</p>
""", unsafe_allow_html=True)
def display_legal_disclaimer():
"""
Display the legal disclaimer at the bottom of the page.
"""
st.markdown("""
<div class="footer-text">
For informational purposes only. Not a substitute for professional medical advice.
</div>
""", unsafe_allow_html=True)
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