pages/Multimodal_Conversational_Search.py

# Streamlit app: Chat with PDFs using OpenSearch, RAG, and ColPali

import streamlit as st
import uuid
import os
import sys
import warnings
import boto3
import json
import random
import string
import pandas as pd
from PIL import Image
from requests.auth import HTTPBasicAuth

# Suppress Streamlit deprecation warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)

# Add necessary module paths
base_path = "/".join(os.path.realpath(__file__).split("/")[:-2])
sys.path.insert(1, f"{base_path}/semantic_search")
sys.path.insert(1, f"{base_path}/RAG")
sys.path.insert(1, f"{base_path}/utilities")

# Local modules
import rag_DocumentLoader
import rag_DocumentSearcher
import colpali

# AWS & OpenSearch setup
region = 'us-east-1'
s3_bucket_ = "pdf-repo-uploads"
bedrock_runtime_client = boto3.client('bedrock-runtime', region_name=region)
polly_client = boto3.client(
    'polly',
    aws_access_key_id=st.secrets['user_access_key'],
    aws_secret_access_key=st.secrets['user_secret_key'],
    region_name=region
)
credentials = boto3.Session().get_credentials()
awsauth = HTTPBasicAuth('master', st.secrets['ml_search_demo_api_access'])

# App configuration
st.set_page_config(layout="wide", page_icon="images/opensearch_mark_default.png")
parent_dirname = "/".join((os.path.dirname(__file__)).split("/")[:-1])
USER_ICON = "images/user.png"
AI_ICON = "images/opensearch-twitter-card.png"
REGENERATE_ICON = "images/regenerate.png"

# Session state setup
if 'user_id' not in st.session_state:
    st.session_state['user_id'] = str(uuid.uuid4())

st.session_state.setdefault('session_id', "")
st.session_state.setdefault('chats', [{'id': 0, 'question': '', 'answer': ''}])
st.session_state.setdefault('questions_', [])
st.session_state.setdefault('answers_', [])
st.session_state.setdefault('show_columns', False)
st.session_state.setdefault('input_index', "hpijan2024hometrack")
st.session_state.setdefault('input_is_rerank', True)
st.session_state.setdefault('input_is_colpali', False)
st.session_state.setdefault('input_copali_rerank', False)
st.session_state.setdefault('input_table_with_sql', False)
st.session_state.setdefault('input_query', "which city has the highest average housing price in UK ?")
st.session_state.setdefault('input_rag_searchType', ["Vector Search"])

# Custom styling
st.markdown("""
    <style>
    [data-testid=column]:nth-of-type(1) [data-testid=stVerticalBlock],
    [data-testid=column]:nth-of-type(2) [data-testid=stVerticalBlock] {
        gap: 0rem;
    }
    </style>
    """, unsafe_allow_html=True)

# Top bar with app logo and clear button
def write_top_bar():
    col1, col2 = st.columns([77, 23])
    with col1:
        st.header("Chat with your data", divider='rainbow')
    with col2:
        clear = st.button("Clear")
    st.write("")  # spacing
    return clear

# Reset inputs when Clear is clicked
if write_top_bar():
    st.session_state.questions_ = []
    st.session_state.answers_ = []
    st.session_state.input_query = ""

# Handle user query submission
def handle_input():
    if st.session_state.input_query == '':
        return

    # Extract all input values from session state
    inputs = {key.removeprefix('input_'): st.session_state[key] for key in st.session_state if key.startswith('input_')}
    st.session_state.inputs_ = inputs

    # Save the question
    st.session_state.questions_.append({
        'question': inputs["query"],
        'id': len(st.session_state.questions_)
    })

    # Choose retrieval method
    if st.session_state.input_is_colpali:
        out_ = colpali.colpali_search_rerank(st.session_state.input_query)
    else:
        out_ = rag_DocumentSearcher.query_(
            awsauth,
            inputs,
            st.session_state['session_id'],
            st.session_state.input_rag_searchType
        )

    # Save the answer and clear input
    st.session_state.answers_.append({
        'answer': out_['text'],
        'source': out_['source'],
        'id': len(st.session_state.questions_),
        'image': out_['image'],
        'table': out_['table']
    })
    st.session_state.input_query = ""

# Display user message block
def write_user_message(msg):
    col1, col2 = st.columns([3, 97])
    with col1:
        st.image(USER_ICON, use_container_width=True)
    with col2:
        st.markdown(
            f"<div style='color:#e28743;font-size:18px;padding:3px 7px;border-radius:10px;font-style:italic;'>{msg['question']}</div>",
            unsafe_allow_html=True
        )

# Render assistant answer block with optional images and tables
def write_chat_message(response, question, index):
    col1, col2, col3 = st.columns([4, 74, 22])

    with col1:
        st.image(AI_ICON, use_container_width=True)

    with col2:
        answer_text = response['answer']
        st.write(answer_text)

        # Add voice playback using AWS Polly
        polly_response = polly_client.synthesize_speech(
            VoiceId='Joanna', OutputFormat='ogg_vorbis', Text=answer_text, Engine='neural')
        st.audio(polly_response['AudioStream'].read(), format="audio/ogg")

        # Optionally show similarity map if enabled
        if st.session_state.input_is_colpali:
            if st.button("Show similarity map", key=f"simmap_{index}"):
                st.session_state.show_columns = True
                st.session_state.maxSimImages = colpali.img_highlight(
                    st.session_state.top_img,
                    st.session_state.query_token_vectors,
                    st.session_state.query_tokens
                )
                handle_input()
                with placeholder.container():
                    render_all()

        with st.expander("Relevant Sources"):
            # Render related images
            for img in response.get('image', []):
                if isinstance(img, dict) and 'file' in img:
                    st.image(img['file'])

            # Render related tables
            for tbl in response.get('table', []):
                try:
                    df = pd.read_csv(tbl['name'], skipinitialspace=True, on_bad_lines='skip', delimiter='`')
                    df.fillna(method='pad', inplace=True)
                    st.table(df)
                except Exception as e:
                    st.warning(f"Failed to load table: {e}")

            # Show source text
            st.write(response.get("source", ""))

# Render all Q&A pairs
def render_all():
    for index, (q, a) in enumerate(zip(st.session_state.questions_, st.session_state.answers_), start=1):
        write_user_message(q)
        write_chat_message(a, q, index)

# Placeholder for dynamic rendering
placeholder = st.empty()
with placeholder.container():
    render_all()

# Input field for user question
col_2, col_3 = st.columns([75, 20])
with col_2:
    st.text_input("Ask here", label_visibility="collapsed", key="input_query")
with col_3:
    st.button("GO", on_click=handle_input, key="play")

# Sidebar configuration
with st.sidebar:
    st.page_link("app.py", label=":orange[Home]", icon="🏠")
    st.subheader(":blue[Sample Data]")
    st.radio("Choose one index", ["UK Housing", "Global Warming stats", "Covid19 impacts on Ireland"], key="input_rad_index")
    st.subheader(":blue[Retriever]")
    st.multiselect("Select the Retriever(s)", ["Keyword Search", "Vector Search", "Sparse Search"], default=["Vector Search"], key="input_rag_searchType")
    st.checkbox("Re-rank results", key="input_is_rerank", value=True)
    st.subheader(":blue[Multi-vector retrieval]")
    st.checkbox("Try Colpali multi-vector retrieval", key="input_is_colpali")