RAG upfated

pages/Multimodal_Conversational_Search.py

@@ -1,232 +1,566 @@
-# Streamlit app: Chat with PDFs using OpenSearch, RAG, and ColPali
-
 import streamlit as st
 import uuid
 import os
+import re
 import sys
-import warnings
+sys.path.insert(1, "/".join(os.path.realpath(__file__).split("/")[0:-2])+"/semantic_search")
+sys.path.insert(1, "/".join(os.path.realpath(__file__).split("/")[0:-2])+"/RAG")
+sys.path.insert(1, "/".join(os.path.realpath(__file__).split("/")[0:-2])+"/utilities")
 import boto3
+import requests
+from boto3 import Session
+import botocore.session
 import json
 import random
 import string
+import rag_DocumentLoader 
+import rag_DocumentSearcher
 import pandas as pd
-from PIL import Image
+from PIL import Image 
+import shutil
+import base64
+import time
+import botocore
+from requests_aws4auth import AWS4Auth
+import colpali
 from requests.auth import HTTPBasicAuth
+import warnings
 
-# 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])
+st.set_page_config(
+    #page_title="Semantic Search using OpenSearch",
+    layout="wide",
+    page_icon="images/opensearch_mark_default.png"
+)
+parent_dirname = "/".join((os.path.dirname(__file__)).split("/")[0:-1])
 USER_ICON = "images/user.png"
 AI_ICON = "images/opensearch-twitter-card.png"
 REGENERATE_ICON = "images/regenerate.png"
+s3_bucket_ = "pdf-repo-uploads"
+            #"pdf-repo-uploads"
+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 = 'us-east-1')
+
+
+# Check if the user ID is already stored in the session state
+if 'user_id' in st.session_state:
+    user_id = st.session_state['user_id']
+    #print(f"User ID: {user_id}")
+
+# If the user ID is not yet stored in the session state, generate a random UUID
+else:
+    user_id = str(uuid.uuid4())
+    st.session_state['user_id'] = user_id
+
+
+if 'session_id' not in st.session_state:
+    st.session_state['session_id'] = ""
+    
+if "chats" not in st.session_state:
+    st.session_state.chats = [
+        {
+            'id': 0,
+            'question': '',
+            'answer': ''
+        }
+    ]
+
+if "questions_" not in st.session_state:
+    st.session_state.questions_ = []
+
+if "show_columns" not in st.session_state:
+    st.session_state.show_columns = False
+
+if "answers_" not in st.session_state:
+    st.session_state.answers_ = []
+
+if "input_index" not in st.session_state:
+    st.session_state.input_index = "hpijan2024hometrack"#"globalwarmingnew"#"hpijan2024hometrack_no_img_no_table"
+    
+if "input_is_rerank" not in st.session_state:
+    st.session_state.input_is_rerank = True
+
+if "input_is_colpali" not in st.session_state:
+    st.session_state.input_is_colpali = False
+
+if "input_copali_rerank" not in st.session_state:
+    st.session_state.input_copali_rerank = False
+    
+if "input_table_with_sql" not in st.session_state:
+    st.session_state.input_table_with_sql = False
+    
+if "input_query" not in st.session_state:
+    st.session_state.input_query="which city has the highest average housing price in UK ?"#"What is the projected energy percentage from renewable sources in future?"#"Which city in United Kingdom has the highest average housing price ?"#"How many aged above 85 years died due to covid ?"# What is the projected energy from renewable sources ?"
+
+if "input_rag_searchType" not in st.session_state:
+    st.session_state.input_rag_searchType = ["Vector Search"]  
+
+
+        
+region = 'us-east-1'
+bedrock_runtime_client = boto3.client('bedrock-runtime',region_name=region)
+output = []
+service = 'es'
 
-# 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] {
+    [data-testid=column]:nth-of-type(2) [data-testid=stVerticalBlock]{
+        gap: 0rem;
+    }
+    [data-testid=column]:nth-of-type(1) [data-testid=stVerticalBlock]{
         gap: 0rem;
     }
     </style>
-    """, unsafe_allow_html=True)
+    """,unsafe_allow_html=True)
+
+
+
+
+credentials = boto3.Session().get_credentials()
+awsauth  = HTTPBasicAuth('master',st.secrets['ml_search_demo_api_access'])
+service = 'es'
+
+
+
+
+# if "input_searchType" not in st.session_state:
+#     st.session_state.input_searchType = "Conversational Search (RAG)"
+
+# if "input_temperature" not in st.session_state:
+#     st.session_state.input_temperature = "0.001"
+
+# if "input_topK" not in st.session_state:
+#     st.session_state.input_topK = 200
+
+# if "input_topP" not in st.session_state:
+#     st.session_state.input_topP = 0.95
+
+# if "input_maxTokens" not in st.session_state:
+#     st.session_state.input_maxTokens = 1024
+
+
+def write_logo():
+    col1, col2, col3 = st.columns([5, 1, 5])
+    with col2:
+        st.image(AI_ICON, use_container_width='always') 
 
-# Top bar with app logo and clear button
 def write_top_bar():
-    col1, col2 = st.columns([77, 23])
+    col1, col2 = st.columns([77,23])
     with col1:
-        st.header("Chat with your data", divider='rainbow')
+        st.write("")
+        st.header("Chat with your data",divider='rainbow')
+        
+        #st.image(AI_ICON, use_container_width='always')
+    
     with col2:
+        st.write("")
+        st.write("")
         clear = st.button("Clear")
-    st.write("")  # spacing
+    st.write("")
+    st.write("")
     return clear
 
-# Reset inputs when Clear is clicked
-if write_top_bar():
+clear = write_top_bar()
+
+if clear:
     st.session_state.questions_ = []
     st.session_state.answers_ = []
-    st.session_state.input_query = ""
+    st.session_state.input_query=""
+    # st.session_state.input_searchType="Conversational Search (RAG)"
+    # st.session_state.input_temperature = "0.001"
+    # st.session_state.input_topK = 200
+    # st.session_state.input_topP = 0.95
+    # st.session_state.input_maxTokens = 1024
 
-# Handle user query submission
-def handle_input():
-    if st.session_state.input_query == '':
-        return
 
-    inputs = {key.removeprefix('input_'): st.session_state[key] for key in st.session_state if key.startswith('input_')}
+def handle_input():
+    print("Question: "+st.session_state.input_query)
+    print("-----------")
+    print("\n\n")
+    if(st.session_state.input_query==''):
+        return ""
+    inputs = {}
+    for key in st.session_state:
+        if key.startswith('input_'):
+            inputs[key.removeprefix('input_')] = st.session_state[key]
     st.session_state.inputs_ = inputs
-
-    st.session_state.questions_.append({
+    
+    #######
+    
+    
+    #st.write(inputs) 
+    question_with_id = {
         'question': inputs["query"],
         'id': len(st.session_state.questions_)
-    })
-
-    if st.session_state.input_is_colpali:
+    }
+    st.session_state.questions_.append(question_with_id)
+    if(st.session_state.input_is_colpali):
         out_ = colpali.colpali_search_rerank(st.session_state.input_query)
+        #print(out_)
     else:
-        out_ = rag_DocumentSearcher.query_(
-            awsauth,
-            inputs,
-            st.session_state['session_id'],
-            st.session_state.input_rag_searchType
-        )
-
+        out_ = rag_DocumentSearcher.query_(awsauth, inputs, st.session_state['session_id'],st.session_state.input_rag_searchType)
     st.session_state.answers_.append({
         'answer': out_['text'],
-        'source': out_['source'],
+        'source':out_['source'],
         'id': len(st.session_state.questions_),
         'image': out_['image'],
-        'table': out_['table']
+        'table':out_['table']
     })
-    st.session_state.input_query = ""
+    st.session_state.input_query=""
+    
+
+    
+# search_type = st.selectbox('Select the Search type',
+#     ('Conversational Search (RAG)',
+#     'OpenSearch vector search', 
+#     'LLM Text Generation'
+#     ),
+   
+#     key = 'input_searchType',
+#     help = "Select the type of retriever\n1. Conversational Search (Recommended) - This will include both the OpenSearch and LLM in the retrieval pipeline \n (note: This will put opensearch response as context to LLM to answer) \n2. OpenSearch vector search - This will put only OpenSearch's vector search in the pipeline, \n(Warning: this will lead to unformatted results )\n3. LLM Text Generation - This will include only LLM in the pipeline, \n(Warning: This will give hallucinated and out of context answers_)"
+#     )
+
+# col1, col2, col3, col4 = st.columns(4)
+    
+# with col1:
+#     st.text_input('Temperature', value = "0.001", placeholder='LLM Temperature', key = 'input_temperature',help = "Set the temperature of the Large Language model. \n Note: 1. Set this to values lower to 1 in the order of 0.001, 0.0001, such low values reduces hallucination and creativity in the LLM response; 2. This applies only when LLM is a part of the retriever pipeline")
+# with col2:
+#     st.number_input('Top K', value = 200, placeholder='Top K', key = 'input_topK', step = 50, help = "This limits the LLM's predictions to the top k most probable tokens at each step of generation, this applies only when LLM is a prt of the retriever pipeline")
+# with col3:
+#     st.number_input('Top P', value = 0.95, placeholder='Top P', key = 'input_topP', step = 0.05, help = "This sets a threshold probability and selects the top tokens whose cumulative probability exceeds the threshold while the tokens are generated by the LLM")
+# with col4:
+#     st.number_input('Max Output Tokens', value = 500, placeholder='Max Output Tokens', key = 'input_maxTokens', step = 100, help = "This decides the total number of tokens generated as the final response. Note: Values greater than 1000 takes longer response time")
+
+# st.markdown('---')
+
 
-# Display user message block
-def write_user_message(msg):
-    col1, col2 = st.columns([3, 97])
+def write_user_message(md):
+    col1, col2 = st.columns([3,97])
+    
     with col1:
-        st.image(USER_ICON, use_container_width=True)
+        st.image(USER_ICON, use_container_width='always')
     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
-        )
+        #st.warning(md['question'])
 
-# Render assistant answer block with optional images and tables
-def write_chat_message(response, question, index):
-    col1, col2, col3 = st.columns([4, 74, 22])
+        st.markdown("<div style='color:#e28743';font-size:18px;padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;width: fit-content;height: fit-content;border-radius: 10px;font-style: italic;'>"+md['question']+"</div>", unsafe_allow_html = True)
+       
 
-    with col1:
-        st.image(AI_ICON, use_container_width=True)
 
+def render_answer(question,answer,index,res_img):
+    
+    
+    col1, col2, col_3 = st.columns([4,74,22])
+    with col1:
+        st.image(AI_ICON, use_container_width='always')
     with col2:
-        answer_text = response['answer']
-        st.write(answer_text)
-
-        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")
-
-        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"):
-            for img in response.get('image', []):
-                if isinstance(img, dict) and 'file' in img:
-                    st.image(img['file'])
-
-            for tbl in response.get('table', []):
-                try:
-                    df = pd.read_csv(tbl['name'], skipinitialspace=True, on_bad_lines='skip', delimiter='`')
+        ans_ = answer['answer']
+        st.write(ans_)
+       
+        
+        
+        # def stream_():
+        #     #use for streaming response on the client side
+        #     for word in ans_.split(" "):
+        #         yield word + " "
+        #         time.sleep(0.04)
+        #     #use for streaming response from Llm directly
+        #     if(isinstance(ans_,botocore.eventstream.EventStream)):
+        #         for event in ans_:
+        #             chunk = event.get('chunk')
+                    
+        #             if chunk:
+                        
+        #                 chunk_obj = json.loads(chunk.get('bytes').decode())
+                        
+        #                 if('content_block' in chunk_obj or ('delta' in chunk_obj and 'text' in chunk_obj['delta'])):
+        #                     key_ = list(chunk_obj.keys())[2]
+        #                     text = chunk_obj[key_]['text']
+                            
+        #                     clear_output(wait=True)
+        #                     output.append(text)
+        #                     yield text
+        #                     time.sleep(0.04)
+            
+                
+        
+        # if(index == len(st.session_state.questions_)):
+        #     st.write_stream(stream_)
+        #     if(isinstance(st.session_state.answers_[index-1]['answer'],botocore.eventstream.EventStream)):
+        #         st.session_state.answers_[index-1]['answer'] = "".join(output)
+        # else:
+        #     st.write(ans_)
+        
+
+        polly_response = polly_client.synthesize_speech(VoiceId='Joanna',
+                        OutputFormat='ogg_vorbis', 
+                        Text = ans_,
+                        Engine = 'neural')
+
+        audio_col1, audio_col2 = st.columns([50,50])
+        with audio_col1:
+            st.audio(polly_response['AudioStream'].read(), format="audio/ogg")
+        rdn_key_1 = ''.join([random.choice(string.ascii_letters)
+                              for _ in range(10)])
+        def show_maxsim():
+            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()    
+        if(st.session_state.input_is_colpali):
+            st.button("Show similarity map",key=rdn_key_1,on_click = show_maxsim)
+                
+        
+        
+        #st.markdown("<div style='font-size:18px;padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;border-radius: 10px;'>"+ans_+"</div>", unsafe_allow_html = True)
+    #st.markdown("<div style='color:#e28743';padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;width: fit-content;height: fit-content;border-radius: 10px;'><b>Relevant images from the document :</b></div>", unsafe_allow_html = True)
+    #st.write("")
+    colu1,colu2,colu3 = st.columns([4,82,20])
+    with colu2:
+        with st.expander("Relevant Sources:"):
+            with st.container():
+                if(len(res_img)>0):
+                    #with st.expander("Images:"):
+                        
+                    idx = 0
+                    print(res_img)
+                    for i in range(0,len(res_img)):
+                        
+                        if(st.session_state.input_is_colpali):
+                            if(st.session_state.show_columns == True):
+                                cols_per_row = 3
+                                st.session_state.image_placeholder=st.empty()
+                                with st.session_state.image_placeholder.container():
+                                    row = st.columns(cols_per_row)
+                                for j, item in enumerate(res_img[i:i+cols_per_row]):
+                                    with row[j]:
+                                        st.image(item['file'])
+                                
+                            else:
+                                st.session_state.image_placeholder = st.empty()
+                                with st.session_state.image_placeholder.container():
+                                    col3_,col4_,col5_ = st.columns([33,33,33])
+                                    with col3_:
+                                        st.image(res_img[i]['file'])
+                                
+                                
+
+                            
+                            
+                        else:
+                            if(res_img[i]['file'].lower()!='none' and idx < 1):
+                                col3,col4,col5 = st.columns([33,33,33])
+                                cols = [col3,col4]
+                                img = res_img[i]['file'].split(".")[0]
+                                caption = res_img[i]['caption']
+                            
+                                with cols[idx]:
+                                    
+                                    st.image(parent_dirname+"/figures/"+st.session_state.input_index+"/"+img+".jpg")
+                            #st.write(caption)
+                            idx = idx+1
+                    if(st.session_state.show_columns == True):
+                        st.session_state.show_columns = False
+                #st.markdown("<div style='color:#e28743';padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;width: fit-content;height: fit-content;border-radius: 10px;'><b>Sources from the document:</b></div>", unsafe_allow_html = True)
+                if(len(answer["table"] )>0):
+                    #with st.expander("Table:"):
+                    df = pd.read_csv(answer["table"][0]['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}")
-
-            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):
+                #with st.expander("Raw sources:"):
+                st.write(answer["source"])
+               
+        
+    with col_3:
+        if(index == len(st.session_state.questions_)):
+
+            rdn_key = ''.join([random.choice(string.ascii_letters)
+                              for _ in range(10)])
+            # rdn_key_1 = ''.join([random.choice(string.ascii_letters)
+            #                   for _ in range(10)])
+            currentValue = ''.join(st.session_state.input_rag_searchType)+str(st.session_state.input_is_rerank)+str(st.session_state.input_table_with_sql)+st.session_state.input_index
+            oldValue = ''.join(st.session_state.inputs_["rag_searchType"])+str(st.session_state.inputs_["is_rerank"])+str(st.session_state.inputs_["table_with_sql"])+str(st.session_state.inputs_["index"])
+            def on_button_click():
+                if(currentValue!=oldValue or 1==1): 
+                    st.session_state.input_query = st.session_state.questions_[-1]["question"]
+                    st.session_state.answers_.pop()
+                    st.session_state.questions_.pop()
+                    
+                    handle_input()
+                    with placeholder.container():
+                        render_all()
+            # def show_maxsim():
+            #     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()
+            if("currentValue"  in st.session_state):
+                del st.session_state["currentValue"]
+
+            try:
+                del regenerate
+            except:
+                pass  
+            placeholder__ = st.empty()
+            placeholder__.button("🔄",key=rdn_key,on_click=on_button_click)
+            
+        
+#Each answer will have context of the question asked in order to associate the provided feedback with the respective question
+def write_chat_message(md, q,index):
+    if(st.session_state.show_columns):
+        res_img = st.session_state.maxSimImages
+    else:
+        res_img = md['image']
+    chat = st.container()
+    with chat:
+        render_answer(q,md,index,res_img)
+    
+def render_all():  
+    index = 0
+    for (q, a) in zip(st.session_state.questions_, st.session_state.answers_):
+        index = index +1
+        
         write_user_message(q)
-        write_chat_message(a, q, index)
+        write_chat_message(a, q,index)
 
-# Placeholder for dynamic rendering
 placeholder = st.empty()
 with placeholder.container():
-    render_all()
+  render_all()
 
-# Input field for user question
-col_2, col_3 = st.columns([75, 20])
+st.markdown("")
+col_2, col_3 = st.columns([75,20])  
 with col_2:
-    st.text_input("Ask here", label_visibility="collapsed", key="input_query")
+    #st.markdown("")
+    input = 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
+    #hidden = st.button("RUN",disabled=True,key = "hidden")
+    play = st.button("GO",on_click=handle_input,key = "play")
 with st.sidebar:
     st.page_link("app.py", label=":orange[Home]", icon="🏠")
     st.subheader(":blue[Sample Data]")
-    coln_1, coln_2 = st.columns([70, 30])
+    coln_1,coln_2 = st.columns([70,30])
     with coln_1:
-        st.radio("Choose one index", ["UK Housing", "Global Warming stats", "Covid19 impacts on Ireland"], key="input_rad_index")
+        index_select = st.radio("Choose one index",["UK Housing","Global Warming stats","Covid19 impacts on Ireland"],key="input_rad_index")
     with coln_2:
-        st.markdown("<p style='font-size:15px'>Preview file</p>", unsafe_allow_html=True)
+        st.markdown("<p style='font-size:15px'>Preview file</p>",unsafe_allow_html=True)
         st.write("[:eyes:](https://github.com/aws-samples/AI-search-with-amazon-opensearch-service/blob/b559f82c07dfcca973f457c0a15d6444752553ab/rag/sample_pdfs/HPI-Jan-2024-Hometrack.pdf)")
         st.write("[:eyes:](https://github.com/aws-samples/AI-search-with-amazon-opensearch-service/blob/b559f82c07dfcca973f457c0a15d6444752553ab/rag/sample_pdfs/global_warming.pdf)")
         st.write("[:eyes:](https://github.com/aws-samples/AI-search-with-amazon-opensearch-service/blob/b559f82c07dfcca973f457c0a15d6444752553ab/rag/sample_pdfs/covid19_ie.pdf)")
+    st.markdown("""
+    <style>
+    [data-testid=column]:nth-of-type(2) [data-testid=stVerticalBlock]{
+        gap: 0rem;
+    }
+    [data-testid=column]:nth-of-type(1) [data-testid=stVerticalBlock]{
+        gap: 0rem;
+    }
+    </style>
+    """,unsafe_allow_html=True)   
+    with st.expander("Sample questions:"):
+        st.markdown("<span style = 'color:#FF9900;'>UK Housing</span> - which city has the highest average housing price in UK ?",unsafe_allow_html=True)
+        st.markdown("<span style = 'color:#FF9900;'>Global Warming stats</span> - What is the projected energy percentage from renewable sources in future?",unsafe_allow_html=True)
+        st.markdown("<span style = 'color:#FF9900;'>Covid19 impacts</span> - How many aged above 85 years died due to covid ?",unsafe_allow_html=True)
+        
+    
+    #st.subheader(":blue[Your multi-modal documents]")
+    # pdf_doc_ = st.file_uploader(
+    #     "Upload your PDFs here and click on 'Process'", accept_multiple_files=False)
+                    
+                
+    # pdf_docs = [pdf_doc_]
+    # if st.button("Process"):
+    #     with st.spinner("Processing"):
+    #         if os.path.isdir(parent_dirname+"/pdfs") == False:
+    #             os.mkdir(parent_dirname+"/pdfs")
+            
+    #         for pdf_doc in pdf_docs:
+    #             print(type(pdf_doc))
+    #             pdf_doc_name = (pdf_doc.name).replace(" ","_")
+    #             with open(os.path.join(parent_dirname+"/pdfs",pdf_doc_name),"wb") as f: 
+    #                 f.write(pdf_doc.getbuffer())  
+                    
+    #             request_ = { "bucket": s3_bucket_,"key": pdf_doc_name}
+    #             # if(st.session_state.input_copali_rerank):
+    #             #     copali.process_doc(request_)
+    #             # else:
+    #             rag_DocumentLoader.load_docs(request_)
+    #             print('lambda done')
+    #     st.success('you can start searching on your PDF')
+        
+    ############## haystach demo temporary addition ############    
+    # st.subheader(":blue[Multimodality]")
+    # colu1,colu2 = st.columns([50,50])
+    # with colu1:
+    #     in_images = st.toggle('Images', key = 'in_images', disabled = False)
+    # with colu2:
+    #     in_tables = st.toggle('Tables', key = 'in_tables', disabled = False)   
+    # if(in_tables):
+    #     st.session_state.input_table_with_sql = True
+    # else:
+    #     st.session_state.input_table_with_sql = False
+        
+     ############## haystach demo temporary addition ############       
+    #if(pdf_doc_ is None or pdf_doc_ == ""):
+    if(index_select == "Global Warming stats"):
+        st.session_state.input_index = "globalwarming"
+    if(index_select == "Covid19 impacts on Ireland"):
+        st.session_state.input_index = "covid19ie"#"choosetheknnalgorithmforyourbillionscaleusecasewithopensearchawsbigdatablog"
+    if(index_select == "BEIR"):
+        st.session_state.input_index = "2104"
+    if(index_select == "UK Housing"):
+        st.session_state.input_index = "hpijan2024hometrack"
+          
+    # custom_index = st.text_input("If uploaded the file already, enter the original file name", value = "")
+    # if(custom_index!=""):
+    #     st.session_state.input_index = re.sub('[^A-Za-z0-9]+', '', (custom_index.lower().replace(".pdf","").split("/")[-1].split(".")[0]).lower())
+    
+    
+    
     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)
+    search_type = st.multiselect('Select the Retriever(s)',
+    ['Keyword Search',
+    'Vector Search', 
+    'Sparse Search',
+    ],
+    ['Vector Search'],
+
+    key = 'input_rag_searchType',
+    help = "Select the type of Search, adding more than one search type will activate hybrid search"#\n1. Conversational Search (Recommended) - This will include both the OpenSearch and LLM in the retrieval pipeline \n (note: This will put opensearch response as context to LLM to answer) \n2. OpenSearch vector search - This will put only OpenSearch's vector search in the pipeline, \n(Warning: this will lead to unformatted results )\n3. LLM Text Generation - This will include only LLM in the pipeline, \n(Warning: This will give hallucinated and out of context answers)"
+    )
+    
+    re_rank = st.checkbox('Re-rank results', key = 'input_re_rank', disabled = False, value = True, help = "Checking this box will re-rank the results using a cross-encoder model")
+     
+    if(re_rank):
+        st.session_state.input_is_rerank = True
+    else:
+        st.session_state.input_is_rerank = False
+    
     st.subheader(":blue[Multi-vector retrieval]")
-
-    colpali_search_rerank = st.checkbox('Try Colpali multi-vector retrieval on the [sample dataset](https://huggingface.co/datasets/vespa-engine/gpfg-QA)',
-                                          key='input_colpali',
-                                          disabled=False,
-                                          value=False,
-                                          help="Checking this box will use colpali as the embedding model and retrieval is performed using multi-vectors followed by re-ranking using MaxSim")
-
-    if colpali_search_rerank:
+    
+    #st.write("Dataset indexed: https://huggingface.co/datasets/vespa-engine/gpfg-QA")
+    colpali_search_rerank =  st.checkbox('Try Colpali multi-vector retrieval on the [sample dataset](https://huggingface.co/datasets/vespa-engine/gpfg-QA)', key = 'input_colpali', disabled = False, value = False, help = "Checking this box will use colpali as the embedding model and retrieval is performed using multi-vectors followed by re-ranking using MaxSim")
+        
+    if(colpali_search_rerank):
         st.session_state.input_is_colpali = True
+        #st.session_state.input_query = ""
     else:
         st.session_state.input_is_colpali = False
-
+    
     with st.expander("Sample questions for Colpali retriever:"):
-        st.write("""
-        1. Proportion of female new hires 2021-2023?
-        2. First-half 2021 return on unlisted real estate investments?
-        3. Trend of the fund's expected absolute volatility between January 2014 and January 2016?
-        4. Fund return percentage in 2017?
-        5. Annualized gross return of the fund from 1997 to 2008?
-        """)
+        st.write("1. Proportion of female new hires 2021-2023? \n\n 2. First-half 2021 return on unlisted real estate investments? \n\n 3. Trend of the fund's expected absolute volatility between January 2014 and January 2016? \n\n 4. Fund return percentage in 2017? \n\n 5. Annualized gross return of the fund from 1997 to 2008?")
+    
+        
+    # copali_rerank = st.checkbox("Search and Re-rank with Token level vectors",key = 'copali_rerank',help = "Enabling this option uses 'Copali' model's page level image embeddings to retrieve documents and MaxSim to re-rank the pages.\n\n Hugging Face Model: https://huggingface.co/vidore/colpali")
+        
+    # if(copali_rerank):
+    #     st.session_state.input_copali_rerank = True
+    # else:
+    #     st.session_state.input_copali_rerank = False
+    
     
+