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import streamlit as st
import math
import io
import uuid
import os
import sys
import boto3
import requests
from requests_aws4auth import AWS4Auth
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")
from boto3 import Session
from pathlib import Path
import botocore.session
import subprocess
#import os_index_df_sql
import json
import random
import string
from PIL import Image
import urllib.request
import base64
import shutil
import re
from requests.auth import HTTPBasicAuth
import utilities.re_ranker as re_ranker
# from nltk.stem import PorterStemmer
# from nltk.tokenize import word_tokenize
import query_rewrite
import amazon_rekognition
#from st_click_detector import click_detector
import llm_eval
import all_search_execute
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
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])
st.markdown("""
<style>
.block-container {
padding-top: 2.75rem;
padding-bottom: 0rem;
padding-left: 5rem;
padding-right: 5rem;
}
</style>
""", unsafe_allow_html=True)
#ps = PorterStemmer()
st.session_state.REGION = 'us-east-1'
#from langchain.callbacks.base import BaseCallbackHandler
USER_ICON = "images/user.png"
AI_ICON = "images/opensearch-twitter-card.png"
REGENERATE_ICON = "images/regenerate.png"
IMAGE_ICON = "images/Image_Icon.png"
TEXT_ICON = "images/text.png"
s3_bucket_ = "pdf-repo-uploads"
#"pdf-repo-uploads"
# 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
# dynamodb = boto3.resource('dynamodb')
# table = dynamodb.Table('ml-search')
if 'session_id' not in st.session_state:
st.session_state['session_id'] = ""
if 'input_reranker' not in st.session_state:
st.session_state['input_reranker'] = "None"#"Cross Encoder"
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 "clear_" not in st.session_state:
st.session_state.clear_ = False
if "input_clear_filter" not in st.session_state:
st.session_state.input_clear_filter = False
if "radio_disabled" not in st.session_state:
st.session_state.radio_disabled = True
if "input_rad_1" not in st.session_state:
st.session_state.input_rad_1 = ""
if "input_manual_filter" not in st.session_state:
st.session_state.input_manual_filter = ""
if "input_category" not in st.session_state:
st.session_state.input_category = None
if "input_gender" not in st.session_state:
st.session_state.input_gender = None
# if "input_price" not in st.session_state:
# st.session_state.input_price = (0,0)
if "input_sql_query" not in st.session_state:
st.session_state.input_sql_query = ""
if "input_rewritten_query" not in st.session_state:
st.session_state.input_rewritten_query = ""
if "input_hybridType" not in st.session_state:
st.session_state.input_hybridType = "OpenSearch Hybrid Query"
if "ndcg_increase" not in st.session_state:
st.session_state.ndcg_increase = " ~ "
if "inputs_" not in st.session_state:
st.session_state.inputs_ = {}
if "img_container" not in st.session_state:
st.session_state.img_container = ""
if "input_rekog_directoutput" not in st.session_state:
st.session_state.input_rekog_directoutput = {}
if "input_weightage" not in st.session_state:
st.session_state.input_weightage = {}
if "img_gen" not in st.session_state:
st.session_state.img_gen = []
if "answers" not in st.session_state:
st.session_state.answers = []
if "answers_none_rank" not in st.session_state:
st.session_state.answers_none_rank = []
if "input_text" not in st.session_state:
st.session_state.input_text="black jacket for men"#"black jacket for men under 120 dollars"
if "input_ndcg" not in st.session_state:
st.session_state.input_ndcg=0.0
if "gen_image_str" not in st.session_state:
st.session_state.gen_image_str=""
# if "input_searchType" not in st.session_state:
# st.session_state.input_searchType = ['Keyword Search']
# if "input_must" not in st.session_state:
# st.session_state.input_must = ["Category","Price","Gender","Style"]
if "input_NormType" not in st.session_state:
st.session_state.input_NormType = "min_max"
if "input_CombineType" not in st.session_state:
st.session_state.input_CombineType = "arithmetic_mean"
if "input_sparse" not in st.session_state:
st.session_state.input_sparse = "disabled"
if "input_evaluate" not in st.session_state:
st.session_state.input_evaluate = "disabled"
if "input_is_rewrite_query" not in st.session_state:
st.session_state.input_is_rewrite_query = "disabled"
if "input_rekog_label" not in st.session_state:
st.session_state.input_rekog_label = ""
if "input_sparse_filter" not in st.session_state:
st.session_state.input_sparse_filter = 0.5
if "input_modelType" not in st.session_state:
st.session_state.input_modelType = "Titan-Embed-Text-v1"
if "input_weight" not in st.session_state:
st.session_state.input_weight = 0.5
if "image_prompt2" not in st.session_state:
st.session_state.image_prompt2 = ""
if "image_prompt" not in st.session_state:
st.session_state.image_prompt = ""
if "bytes_for_rekog" not in st.session_state:
st.session_state.bytes_for_rekog = ""
if "OpenSearchDomainEndpoint" not in st.session_state:
st.session_state.OpenSearchDomainEndpoint = "search-opensearchservi-shjckef2t7wo-iyv6rajdgxg6jas25aupuxev6i.us-west-2.es.amazonaws.com"
if "max_selections" not in st.session_state:
st.session_state.max_selections = "None"
if "re_ranker" not in st.session_state:
st.session_state.re_ranker = "true"
host = 'https://'+st.session_state.OpenSearchDomainEndpoint+'/'
service = 'es'
#credentials = boto3.Session().get_credentials()
awsauth = awsauth = HTTPBasicAuth('master',st.secrets['ml_search_demo_api_access'])
headers = {"Content-Type": "application/json"}
if "REGION" not in st.session_state:
st.session_state.REGION = ""
if "BEDROCK_MULTIMODAL_MODEL_ID" not in st.session_state:
st.session_state.BEDROCK_MULTIMODAL_MODEL_ID = "p_Qk-ZMBcuw9xT4ly3_B"
if "search_types" not in st.session_state:
st.session_state.search_types = 'Keyword Search,Vector Search,Multimodal Search,NeuralSparse Search',
if "KendraResourcePlanID" not in st.session_state:
st.session_state.KendraResourcePlanID= ""
if "SAGEMAKER_CrossEncoder_MODEL_ID" not in st.session_state:
st.session_state.SAGEMAKER_CrossEncoder_MODEL_ID = "eUoo-ZMBTp0efWqBQ-5g"
if "SAGEMAKER_SPARSE_MODEL_ID" not in st.session_state:
st.session_state.SAGEMAKER_SPARSE_MODEL_ID = "fkol-ZMBTp0efWqBcO2P"
if "BEDROCK_TEXT_MODEL_ID" not in st.session_state:
st.session_state.BEDROCK_TEXT_MODEL_ID = "usQk-ZMBkiQuoz1QFmXN"
#bytes_for_rekog = ""
bedrock_ = boto3.client('bedrock-runtime',
aws_access_key_id=st.secrets['user_access_key'],
aws_secret_access_key=st.secrets['user_secret_key'], region_name = 'us-east-1')
search_all_type = True
if(search_all_type==True):
search_types = ['Keyword Search',
'Vector Search',
'Multimodal Search',
'NeuralSparse Search',
]
from streamlit.components.v1 import html
# with st.container():
# html("""
# <script>
# // Locate elements
# var decoration = window.parent.document.querySelectorAll('[data-testid="stDecoration"]')[0];
# decoration.style.height = "3.0rem";
# decoration.style.right = "45px";
# // Adjust text decorations
# decoration.innerText = "Semantic Search with OpenSearch!"; // Replace with your desired text
# decoration.style.fontWeight = "bold";
# decoration.style.display = "flex";
# decoration.style.justifyContent = "center";
# decoration.style.alignItems = "center";
# decoration.style.fontWeight = "bold";
# decoration.style.backgroundImage = url('/home/ubuntu/AI-search-with-amazon-opensearch-service/OpenSearchApp/images/service_logo.png'); // Remove background image
# decoration.style.backgroundSize = "unset"; // Remove background size
# </script>
# """, width=0, height=0)
def generate_images(tab,inp_):
#write_top_bar()
seed = random.randint(1, 10)
request = json.dumps(
{
"taskType": "TEXT_IMAGE",
"textToImageParams": {"text": st.session_state.image_prompt},
"imageGenerationConfig": {
"numberOfImages": 3,
"quality": "standard",
"cfgScale": 8.0,
"height": 512,
"width": 512,
"seed": seed,
},
}
)
if(inp_!=st.session_state.image_prompt):
print("call bedrocck")
response = bedrock_.invoke_model(
modelId="amazon.titan-image-generator-v1", body=request
)
response_body = json.loads(response["body"].read())
st.session_state.img_gen = response_body["images"]
gen_images_dir = os.path.join(parent_dirname, "gen_images")
if os.path.exists(gen_images_dir):
shutil.rmtree(gen_images_dir)
os.mkdir(gen_images_dir)
width_ = 200
height_ = 200
index_ = 0
#if(inp_!=st.session_state.image_prompt):
if(len(st.session_state.img_gen)==0 and st.session_state.clear_ == True):
#write_top_bar()
placeholder1 = st.empty()
with tab:
with placeholder1.container():
st.empty()
images_dis = []
for image_ in st.session_state.img_gen:
st.session_state.radio_disabled = False
if(index_==0):
# with tab:
# rad1, rad2,rad3 = st.columns([98,1,1])
# if(st.session_state.input_rad_1 is None):
# rand_ = ""
# else:
# rand_ = st.session_state.input_rad_1
# if(inp_!=st.session_state.image_prompt+rand_):
# with rad1:
# sel_rad_1 = st.radio("Choose one image", ["1","2","3"],index=None, horizontal = True,key = 'input_rad_1')
with tab:
#sel_image = st.radio("", ["1","2","3"],index=None, horizontal = True)
if(st.session_state.img_container!=""):
st.session_state.img_container.empty()
place_ = st.empty()
img1, img2,img3 = place_.columns([30,30,30])
st.session_state.img_container = place_
img_arr = [img1, img2,img3]
base64_image_data = image_
#st.session_state.gen_image_str = base64_image_data
print("perform multimodal search")
Image.MAX_IMAGE_PIXELS = 100000000
filename = st.session_state.image_prompt+"_gen_"+str(index_)
photo = parent_dirname+"/gen_images/"+filename+'.jpg' # I assume you have a way of picking unique filenames
imgdata = base64.b64decode(base64_image_data)
with open(photo, 'wb') as f:
f.write(imgdata)
with Image.open(photo) as image:
file_type = 'jpg'
path = image.filename.rsplit(".", 1)[0]
image.thumbnail((width_, height_))
image.save(parent_dirname+"/gen_images/"+filename+"-resized_display."+file_type)
with img_arr[index_]:
placeholder_ = st.empty()
placeholder_.image(parent_dirname+"/gen_images/"+filename+"-resized_display."+file_type)
index_ = index_ + 1
def handle_input():
if("text" in st.session_state.inputs_):
if(st.session_state.inputs_["text"] != st.session_state.input_text):
st.session_state.input_ndcg=0.0
st.session_state.bytes_for_rekog = ""
print("***")
if(st.session_state.img_doc is not None or (st.session_state.input_rad_1 is not None and st.session_state.input_rad_1!="") ):#and st.session_state.input_searchType == 'Multi-modal Search'):
print("perform multimodal search")
st.session_state.input_imageUpload = 'yes'
if(st.session_state.input_rad_1 is not None and st.session_state.input_rad_1!=""):
num_str = str(int(st.session_state.input_rad_1.strip())-1)
with open(parent_dirname+"/gen_images/"+st.session_state.image_prompt+"_gen_"+num_str+"-resized_display.jpg", "rb") as image_file:
input_image = base64.b64encode(image_file.read()).decode("utf8")
st.session_state.input_image = input_image
if(st.session_state.input_imageUpload == 'yes' and 'Keyword Search' in st.session_state.input_searchType):
st.session_state.bytes_for_rekog = Path(parent_dirname+"/gen_images/"+st.session_state.image_prompt+"_gen_"+num_str+".jpg").read_bytes()
else:
Image.MAX_IMAGE_PIXELS = 100000000
width = 2048
height = 2048
uploaded_images = os.path.join(parent_dirname, "uploaded_images")
if not os.path.exists(uploaded_images):
os.mkdir(uploaded_images)
with open(os.path.join(parent_dirname+"/uploaded_images",st.session_state.img_doc.name),"wb") as f:
f.write(st.session_state.img_doc.getbuffer())
photo = parent_dirname+"/uploaded_images/"+st.session_state.img_doc.name
with Image.open(photo) as image:
image.verify()
with Image.open(photo) as image:
width_ = 200
height_ = 200
if image.format.upper() in ["JPEG", "PNG","JPG"]:
path = image.filename.rsplit(".", 1)[0]
org_file_type = st.session_state.img_doc.name.split(".")[1]
image.thumbnail((width, height))
if(org_file_type.upper()=="PNG"):
file_type = "jpg"
image.convert('RGB').save(f"{path}-resized.{file_type}")
else:
file_type = org_file_type
image.save(f"{path}-resized.{file_type}")
image.thumbnail((width_, height_))
image.save(f"{path}-resized_display.{org_file_type}")
with open(photo.split(".")[0]+"-resized."+file_type, "rb") as image_file:
input_image = base64.b64encode(image_file.read()).decode("utf8")
st.session_state.input_image = input_image
if(st.session_state.input_imageUpload == 'yes' and 'Keyword Search' in st.session_state.input_searchType):
st.session_state.bytes_for_rekog = Path(parent_dirname+"/uploaded_images/"+st.session_state.img_doc.name).read_bytes()
else:
print("no image uploaded")
st.session_state.input_imageUpload = 'no'
st.session_state.input_image = ''
inputs = {}
# if(st.session_state.input_imageUpload == 'yes'):
# st.session_state.input_searchType = 'Multi-modal Search'
# if(st.session_state.input_sparse == 'enabled' or st.session_state.input_is_rewrite_query == 'enabled'):
# st.session_state.input_searchType = 'Keyword Search'
if(st.session_state.input_imageUpload == 'yes' and 'Keyword Search' in st.session_state.input_searchType):
old_rekog_label = st.session_state.input_rekog_label
st.session_state.input_rekog_label = amazon_rekognition.extract_image_metadata(st.session_state.bytes_for_rekog)
if(st.session_state.input_text == ""):
st.session_state.input_text = st.session_state.input_rekog_label
# if(st.session_state.input_imageUpload == 'yes'):
# if(st.session_state.input_searchType!='Multi-modal Search'):
# if(st.session_state.input_searchType=='Keyword Search'):
# if(st.session_state.input_rekognition != 'enabled'):
# st.error('For Keyword Search using images, enable "Enrich metadata for Images" in the left panel',icon = "🚨")
# #st.session_state.input_rekognition = 'enabled'
# st.switch_page('pages/1_Semantic_Search.py')
# #st.stop()
# else:
# st.error('Please set the search type as "Keyword Search (enabling Enrich metadata for Images) or Multi-modal Search"',icon = "🚨")
# #st.session_state.input_searchType='Multi-modal Search'
# st.switch_page('pages/1_Semantic_Search.py')
# #st.stop()
weightage = {}
st.session_state.weights_ = []
total_weight = 0.0
counter = 0
num_search = len(st.session_state.input_searchType)
any_weight_zero = False
for type in st.session_state.input_searchType:
key_weight = "input_"+type.split(" ")[0]+"-weight"
total_weight = total_weight + st.session_state[key_weight]
if(st.session_state[key_weight]==0):
any_weight_zero = True
print(total_weight)
for key in st.session_state:
if(key.startswith('input_')):
original_key = key.removeprefix('input_')
if('weight' not in key):
inputs[original_key] = st.session_state[key]
else:
if(original_key.split("-")[0] + " Search" in st.session_state.input_searchType):
counter = counter +1
if(total_weight!=100 or any_weight_zero == True):
extra_weight = 100%num_search
if(counter == num_search):
cal_weight = math.trunc(100/num_search)+extra_weight
else:
cal_weight = math.trunc(100/num_search)
st.session_state[key] = cal_weight
weightage[original_key] = cal_weight
st.session_state.weights_.append(cal_weight)
else:
weightage[original_key] = st.session_state[key]
st.session_state.weights_.append(st.session_state[key])
else:
weightage[original_key] = 0.0
st.session_state[key] = 0.0
inputs['weightage']=weightage
st.session_state.input_weightage = weightage
print("====================")
print(st.session_state.weights_)
print(st.session_state.input_weightage )
print("====================")
#print("***************************")
#print(sum(weights_))
# if(sum(st.session_state.weights_)!=100):
# st.warning('The total weight of selected search type(s) should be equal to 100',icon = "🚨")
# refresh = st.button("Re-Enter")
# if(refresh):
# st.switch_page('pages/1_Semantic_Search.py')
# st.stop()
# #st.session_state.input_rekognition = 'enabled'
# st.rerun()
st.session_state.inputs_ = inputs
#st.write(inputs)
question_with_id = {
'question': inputs["text"],
'id': len(st.session_state.questions)
}
st.session_state.questions = []
st.session_state.questions.append(question_with_id)
st.session_state.answers = []
if(st.session_state.input_is_sql_query == 'enabled'):
os_index_df_sql.sql_process(st.session_state.input_text)
print(st.session_state.input_sql_query)
else:
st.session_state.input_sql_query = ""
if(st.session_state.input_is_rewrite_query == 'enabled' or (st.session_state.input_imageUpload == 'yes' and 'Keyword Search' in st.session_state.input_searchType)):
query_rewrite.get_new_query_res(st.session_state.input_text)
print("-------------------")
print(st.session_state.input_rewritten_query)
print("-------------------")
else:
st.session_state.input_rewritten_query = ""
# elif(st.session_state.input_rekog_label!="" and st.session_state.input_rekognition == 'enabled'):
# ans__ = amazon_rekognition.call(st.session_state.input_text,st.session_state.input_rekog_label)
# else:
ans__ = all_search_execute.handler(inputs, st.session_state['session_id'])
st.session_state.answers.append({
'answer': ans__,#all_search_api.call(json.dumps(inputs), st.session_state['session_id']),
'search_type':inputs['searchType'],
'id': len(st.session_state.questions)
})
st.session_state.answers_none_rank = st.session_state.answers
if(st.session_state.input_reranker == "None"):
st.session_state.answers = st.session_state.answers_none_rank
else:
if(st.session_state.input_reranker == 'Kendra Rescore'):
st.session_state.answers = re_ranker.re_rank("search",st.session_state.input_reranker,st.session_state.input_searchType,st.session_state.questions, st.session_state.answers)
if(st.session_state.input_evaluate == "enabled"):
llm_eval.eval(st.session_state.questions, st.session_state.answers)
#st.session_state.input_text=""
#st.session_state.input_searchType=st.session_state.input_searchType
def write_top_bar():
# st.markdown("""
# <style>
# [data-testid=column]:nth-of-type(1) [data-testid=stVerticalBlock]{
# gap: 0rem;
# }
# </style>
# """,unsafe_allow_html=True)
#print("top bar")
# st.title(':mag: AI powered OpenSearch')
# st.write("")
# st.write("")
col1, col2,col3,col4 = st.columns([2.5,35,8,7])
with col1:
st.image(TEXT_ICON, use_container_width='always')
with col2:
#st.markdown("")
input = st.text_input( "Ask here",label_visibility = "collapsed",key="input_text",placeholder = "Type your query")
with col3:
play = st.button("Search",on_click=handle_input,key = "play")
with col4:
clear = st.button("Clear")
col5, col6 = st.columns([4.5,95])
with col5:
st.image(IMAGE_ICON, use_container_width='always')
with col6:
with st.expander(':green[Search by using an image]'):
tab2, tab1 = st.tabs(["Upload Image","Generate Image by AI"])
with tab1:
c1,c2 = st.columns([80,20])
with c1:
gen_images=st.text_area("Text2Image:",placeholder = "Enter the text prompt to generate images",height = 68, key = "image_prompt")
with c2:
st.markdown("<div style = 'height:43px'></div>",unsafe_allow_html=True)
st.button("Generate",disabled=False,key = "generate",on_click = generate_images, args=(tab1,"default_img"))
# image_select = st.select_slider(
# "Select a image",
# options=["Image 1","Image 2","Image 3"], value = None, disabled = st.session_state.radio_disabled,key = "image_select")
image_select = st.radio("Choose one image", ["Image 1","Image 2","Image 3"],index=None, horizontal = True,key = 'image_select',disabled = st.session_state.radio_disabled)
st.markdown("""
<style>
[role=radiogroup]{
gap: 6rem;
}
</style>
""",unsafe_allow_html=True)
if(st.session_state.image_select is not None and st.session_state.image_select !="" and len(st.session_state.img_gen)!=0):
print("image_select")
print("------------")
print(st.session_state.image_select)
st.session_state.input_rad_1 = st.session_state.image_select.split(" ")[1]
else:
st.session_state.input_rad_1 = ""
# rad1, rad2,rad3 = st.columns([33,33,33])
# with rad1:
# btn1 = st.button("choose image 1", disabled = st.session_state.radio_disabled)
# with rad2:
# btn2 = st.button("choose image 2", disabled = st.session_state.radio_disabled)
# with rad3:
# btn3 = st.button("choose image 3", disabled = st.session_state.radio_disabled)
# if(btn1):
# st.session_state.input_rad_1 = "1"
# if(btn2):
# st.session_state.input_rad_1 = "2"
# if(btn3):
# st.session_state.input_rad_1 = "3"
generate_images(tab1,gen_images)
with tab2:
st.session_state.img_doc = st.file_uploader(
"Upload image", accept_multiple_files=False,type = ['png', 'jpg'])
return clear,tab1
clear,tab_ = write_top_bar()
if clear:
print("clear1")
st.session_state.questions = []
st.session_state.answers = []
st.session_state.clear_ = True
st.session_state.image_prompt2 = ""
st.session_state.input_rekog_label = ""
st.session_state.radio_disabled = True
if(len(st.session_state.img_gen)!=0):
st.session_state.img_container.empty()
st.session_state.img_gen = []
st.session_state.input_rad_1 = ""
# placeholder1 = st.empty()
# with placeholder1.container():
# generate_images(tab_,st.session_state.image_prompt)
#st.session_state.input_text=""
# 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
col1, col3, col4 = st.columns([70,18,12])
with col1:
if(st.session_state.max_selections == "" or st.session_state.max_selections == "1"):
st.session_state.max_selections = 1
if(st.session_state.max_selections == "None"):
st.session_state.max_selections = None
search_type = st.multiselect('Select the Search type(s)',
search_types,['Keyword Search'],
max_selections = st.session_state.max_selections,
key = 'input_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)"
)
with col3:
st.number_input("No. of docs", min_value=1, max_value=50, value=5, step=5, key='input_K', help=None)
with col4:
st.markdown("<div style='fontSize:14.5px'>Evaluate</div>",unsafe_allow_html=True)
evaluate = st.toggle(' ', key = 'evaluate', disabled = False) #help = "Checking this box will use LLM to evaluate results as relevant and irrelevant. \n\n This option increases the latency")
if(evaluate):
st.session_state.input_evaluate = "enabled"
#llm_eval.eval(st.session_state.questions, st.session_state.answers)
else:
st.session_state.input_evaluate = "disabled"
if(search_all_type == True or 1==1):
with st.sidebar:
st.page_link("app.py", label=":orange[Home]", icon="🏠")
#st.image('/home/ubuntu/AI-search-with-amazon-opensearch-service/OpenSearchApp/images/service_logo.png', width = 300)
#st.warning('Note: After changing any of the below settings, click "SEARCH" button or 🔄 to apply the changes', icon="⚠️")
#st.header(' :gear: :orange[Fine-tune Search]')
#st.write("Note: After changing any of the below settings, click 'SEARCH' button or '🔄' to apply the changes")
#st.subheader(':blue[Keyword Search]')
########################## enable for query_rewrite ########################
rewrite_query = st.checkbox('Auto-apply filters', key = 'query_rewrite', disabled = False, help = "Checking this box will use LLM to rewrite your query. \n\n Here your natural language query is transformed into OpenSearch query with added filters and attributes")
st.multiselect('Fields for "MUST" filter',
('Price','Gender', 'Color', 'Category', 'Style'),['Category'],
key = 'input_must',
)
########################## enable for query_rewrite ########################
####### Filters #########
st.subheader(':blue[Filters]')
def clear_filter():
st.session_state.input_manual_filter="False"
st.session_state.input_category=None
st.session_state.input_gender=None
st.session_state.input_price=(0,0)
handle_input()
filter_place_holder = st.container()
with filter_place_holder:
st.selectbox("Select one Category", ("accessories", "books","floral","furniture","hot_dispensed","jewelry","tools","apparel","cold_dispensed","food_service","groceries","housewares","outdoors","salty_snacks","videos","beauty","electronics","footwear","homedecor","instruments","seasonal"),index = None,key = "input_category")
st.selectbox("Select one Gender", ("male","female"),index = None,key = "input_gender")
st.slider("Select a range of price", 0, 2000, (0, 0),50, key = "input_price")
if(st.session_state.input_category!=None or st.session_state.input_gender!=None or st.session_state.input_price!=(0,0)):
st.session_state.input_manual_filter="True"
else:
st.session_state.input_manual_filter="False"
clear_filter = st.button("Clear Filters",on_click=clear_filter)
# filter_place_holder = st.container()
# with filter_place_holder:
# st.selectbox("Select one Category", ("accessories", "books","floral","furniture","hot_dispensed","jewelry","tools","apparel","cold_dispensed","food_service","groceries","housewares","outdoors","salty_snacks","videos","beauty","electronics","footwear","homedecor","instruments","seasonal"),index = None,key = "input_category")
# st.selectbox("Select one Gender", ("male","female"),index = None,key = "input_gender")
# st.slider("Select a range of price", 0, 2000, (0, 0),50, key = "input_price")
# st.session_state.input_category=None
# st.session_state.input_gender=None
# st.session_state.input_price=(0,0)
print("--------------------filters---------------")
print(st.session_state.input_gender)
print(st.session_state.input_manual_filter)
print("--------------------filters---------------")
####### Filters #########
if('NeuralSparse Search' in st.session_state.search_types):
st.subheader(':blue[Neural Sparse Search]')
sparse_filter = st.slider('Keep only sparse tokens with weight >=', 0.0, 1.0, 0.5,0.1,key = 'input_sparse_filter', help = 'Use this slider to set the minimum weight that the sparse vector token weights should meet, rest are filtered out')
#sql_query = st.checkbox('Re-write as SQL query', key = 'sql_rewrite', disabled = True, help = "In Progress")
st.session_state.input_is_rewrite_query = 'disabled'
st.session_state.input_is_sql_query = 'disabled'
########################## enable for query_rewrite ########################
if rewrite_query:
#st.write(st.session_state.inputs_)
st.session_state.input_is_rewrite_query = 'enabled'
# if sql_query:
# #st.write(st.session_state.inputs_)
# st.session_state.input_is_sql_query = 'enabled'
########################## enable for sql conversion ########################
#st.markdown('---')
#st.header('Fine-tune keyword Search', divider='rainbow')
#st.subheader('Note: The below selection applies only when the Search type is set to Keyword Search')
# st.markdown("<u>Enrich metadata for :</u>",unsafe_allow_html=True)
# c3,c4 = st.columns([10,90])
# with c4:
# rekognition = st.checkbox('Images', key = 'rekognition', help = "Checking this box will use AI to extract metadata for images that are present in query and documents")
# if rekognition:
# #st.write(st.session_state.inputs_)
# st.session_state.input_rekognition = 'enabled'
# else:
# st.session_state.input_rekognition = "disabled"
#st.markdown('---')
#st.header('Fine-tune Hybrid Search', divider='rainbow')
#st.subheader('Note: The below parameters apply only when the Search type is set to Hybrid Search')
#st.write("---")
#if(st.session_state.max_selections == "None"):
st.subheader(':blue[Hybrid Search]')
# st.selectbox('Select the Hybrid Search type',
# ("OpenSearch Hybrid Query","Reciprocal Rank Fusion"),key = 'input_hybridType')
# equal_weight = st.button("Give equal weights to selected searches")
#st.warning('Weight of each of the selected search type should be greater than 0 and the total weight of all the selected search type(s) should be equal to 100',icon = "⚠️")
#st.markdown("<p style = 'font-size:14.5px;font-style:italic;'>Set Weights</p>",unsafe_allow_html=True)
with st.expander("Set query Weightage:"):
st.number_input("Keyword %", min_value=0, max_value=100, value=100, step=5, key='input_Keyword-weight', help=None)
st.number_input("Vector %", min_value=0, max_value=100, value=0, step=5, key='input_Vector-weight', help=None)
st.number_input("Multimodal %", min_value=0, max_value=100, value=0, step=5, key='input_Multimodal-weight', help=None)
st.number_input("NeuralSparse %", min_value=0, max_value=100, value=0, step=5, key='input_NeuralSparse-weight', help=None)
# if(equal_weight):
# counter = 0
# num_search = len(st.session_state.input_searchType)
# weight_type = ["input_Keyword-weight","input_Vector-weight","input_Multimodal-weight","input_NeuralSparse-weight"]
# for type in weight_type:
# if(type.split("-")[0].replace("input_","")+ " Search" in st.session_state.input_searchType):
# print("ssssssssssss")
# counter = counter +1
# extra_weight = 100%num_search
# if(counter == num_search):
# cal_weight = math.trunc(100/num_search)+extra_weight
# else:
# cal_weight = math.trunc(100/num_search)
# st.session_state[weight_type] = cal_weight
# else:
# st.session_state[weight_type] = 0
#weight = st.slider('Weight for Vector Search', 0.0, 1.0, 0.5,0.1,key = 'input_weight', help = 'Use this slider to set the weightage for keyword and vector search, higher values of the slider indicate the increased weightage for semantic search.\n\n This applies only when the search type is set to Hybrid Search')
# st.selectbox('Select the Normalisation type',
# ('min_max',
# 'l2'
# ),
#st.write("---")
# key = 'input_NormType',
# disabled = True,
# help = "Select the type of Normalisation to be applied on the two sets of scores"
# )
# st.selectbox('Select the Score Combination type',
# ('arithmetic_mean','geometric_mean','harmonic_mean'
# ),
# key = 'input_CombineType',
# disabled = True,
# help = "Select the Combination strategy to be used while combining the two scores of the two search queries for every document"
# )
#st.markdown('---')
#st.header('Select the ML Model for text embedding', divider='rainbow')
#st.subheader('Note: The below selection applies only when the Search type is set to Vector or Hybrid Search')
if(st.session_state.re_ranker == "true"):
st.subheader(':blue[Re-ranking]')
reranker = st.selectbox('Choose a Re-Ranker',
('None','Cross Encoder'#'Kendra Rescore'
),
key = 'input_reranker',
help = 'Select the Re-Ranker type, select "None" to apply no re-ranking of the results',
#on_change = re_ranker.re_rank,
args=(st.session_state.questions, st.session_state.answers)
)
# st.write("---")
# st.subheader('Text Embeddings Model')
# model_type = st.selectbox('Select the Text Embeddings Model',
# ('Titan-Embed-Text-v1','GPT-J-6B'
# ),
# key = 'input_modelType',
# help = "Select the Text embedding model, this applies only for the vector and hybrid search"
# )
#st.markdown('---')
#st.markdown('---')
def write_user_message(md,ans):
#print(ans)
ans = ans["answer"][0]
col1, col2, col3 = st.columns([3,40,20])
with col1:
st.image(USER_ICON, use_container_width='always')
with col2:
#st.warning(md['question'])
st.markdown("<div style='fontSize:15px;padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;width: fit-content;height: fit-content;border-radius: 10px;'>Input Text: </div><div style='fontSize:25px;padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;width: fit-content;height: fit-content;border-radius: 10px;font-style: italic;color:#e28743'>"+md['question']+"</div>", unsafe_allow_html = True)
if('query_sparse' in ans):
with st.expander("Expanded Query:"):
query_sparse = dict(sorted(ans['query_sparse'].items(), key=lambda item: item[1],reverse=True))
filtered_query_sparse = dict()
for key in query_sparse:
filtered_query_sparse[key] = round(query_sparse[key], 2)
st.write(filtered_query_sparse)
if(st.session_state.input_is_rewrite_query == "enabled" and st.session_state.input_rewritten_query !=""):
with st.expander("Re-written Query:"):
st.json(st.session_state.input_rewritten_query,expanded = True)
with col3:
st.markdown("<div style='fontSize:15px;padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;width: fit-content;height: fit-content;border-radius: 10px;'>Input Image: </div>", unsafe_allow_html = True)
if(st.session_state.input_imageUpload == 'yes'):
if(st.session_state.input_rad_1 is not None and st.session_state.input_rad_1!=""):
num_str = str(int(st.session_state.input_rad_1.strip())-1)
img_file = parent_dirname+"/gen_images/"+st.session_state.image_prompt+"_gen_"+num_str+"-resized_display.jpg"
else:
img_file = parent_dirname+"/uploaded_images/"+st.session_state.img_doc.name.split(".")[0]+"-resized_display."+st.session_state.img_doc.name.split(".")[1]
st.image(img_file)
if(st.session_state.input_rekog_label !=""):
with st.expander("Enriched Query Metadata:"):
st.markdown('<p>'+json.dumps(st.session_state.input_rekog_directoutput)+'<p>',unsafe_allow_html=True)
else:
st.markdown("<div style='fontSize:15px;padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;width: fit-content;height: fit-content;border-radius: 10px;'>None</div>", unsafe_allow_html = True)
st.markdown('---')
# def stem_(sentence):
# words = word_tokenize(sentence)
# words_stem = []
# for w in words:
# words_stem.append( ps.stem(w))
# return words_stem
def render_answer(answer,index):
column1, column2 = st.columns([6,90])
with column1:
st.image(AI_ICON, use_container_width='always')
with column2:
st.markdown("<div style='fontSize:25px;padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;width: fit-content;height: fit-content;border-radius: 10px;'>Results </div>", unsafe_allow_html = True)
if(st.session_state.input_evaluate == "enabled" and st.session_state.input_ndcg > 0):
span_color = "white"
if("↑" in st.session_state.ndcg_increase):
span_color = "green"
if("↓" in st.session_state.ndcg_increase):
span_color = "red"
st.markdown("<span style='fontSize:20px;padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;width: fit-content;height: fit-content;border-radius: 20px;font-family:Courier New;color:#e28743'>Relevance:" +str('%.3f'%(st.session_state.input_ndcg)) + "</span><span style='font-size:30px;font-weight:bold;color:"+span_color+"'>"+st.session_state.ndcg_increase.split("~")[0] +"</span><span style='font-size:15px;font-weight:bold;font-family:Courier New;color:"+span_color+"'> "+st.session_state.ndcg_increase.split("~")[1]+"</span>", unsafe_allow_html = True)
#st.markdown("<span style='font-size:30px;color:"+span_color+"'>"+st.session_state.ndcg_increase.split("~")[0] +"</span><span style='font-size:15px;font-family:Courier New;color:"+span_color+"'>"+st.session_state.ndcg_increase.split("~")[1]+"</span>",unsafe_allow_html = True)
placeholder_no_results = st.empty()
col_1, col_2,col_3 = st.columns([70,10,20])
i = 0
filter_out = 0
for ans in answer:
if('b5/b5319e00' in ans['image_url'] ):
filter_out+=1
continue
# imgdata = base64.b64decode(ans['image_binary'])
format_ = ans['image_url'].split(".")[-1]
#urllib.request.urlretrieve(ans['image_url'], "/home/ubuntu/res_images/"+str(i)+"_."+format_)
Image.MAX_IMAGE_PIXELS = 100000000
width = 500
height = 500
with col_1:
inner_col_1,inner_col_2 = st.columns([8,92])
with inner_col_2:
st.image(ans['image_url'].replace("/home/ec2-user/SageMaker/","/home/user/"))
if("highlight" in ans and 'Keyword Search' in st.session_state.input_searchType):
test_strs = ans["highlight"]
tag = "em"
res__ = []
for test_str in test_strs:
start_idx = test_str.find("<" + tag + ">")
while start_idx != -1:
end_idx = test_str.find("</" + tag + ">", start_idx)
if end_idx == -1:
break
res__.append(test_str[start_idx+len(tag)+2:end_idx])
start_idx = test_str.find("<" + tag + ">", end_idx)
desc__ = ans['desc'].split(" ")
final_desc = "<p>"
###### stemming and highlighting
# ans_text = ans['desc']
# query_text = st.session_state.input_text
# ans_text_stemmed = set(stem_(ans_text))
# query_text_stemmed = set(stem_(query_text))
# common = ans_text_stemmed.intersection( query_text_stemmed)
# #unique = set(document_1_words).symmetric_difference( )
# desc__stemmed = stem_(desc__)
# for word_ in desc__stemmed:
# if(word_ in common):
for word in desc__:
if(re.sub('[^A-Za-z0-9]+', '', word) in res__):
final_desc += "<span style='color:#e28743;font-weight:bold'>"+word+"</span> "
else:
final_desc += word + " "
final_desc += "</p>"
st.markdown(final_desc,unsafe_allow_html = True)
else:
st.write(ans['desc'])
if("sparse" in ans):
with st.expander("Expanded document:"):
sparse_ = dict(sorted(ans['sparse'].items(), key=lambda item: item[1],reverse=True))
filtered_sparse = dict()
for key in sparse_:
if(sparse_[key]>=1.0):
filtered_sparse[key] = round(sparse_[key], 2)
st.write(filtered_sparse)
with st.expander("Document Metadata:",expanded = False):
# if("rekog" in ans):
# div_size = [50,50]
# else:
# div_size = [99,1]
# div1,div2 = st.columns(div_size)
# with div1:
st.write(":green[default:]")
st.json({"category:":ans['category'],"price":str(ans['price']),"gender_affinity":ans['gender_affinity'],"style":ans['style']},expanded = True)
#with div2:
if("rekog" in ans):
st.write(":green[enriched:]")
st.json(ans['rekog'],expanded = True)
with inner_col_1:
if(st.session_state.input_evaluate == "enabled"):
with st.container(border = False):
if("relevant" in ans.keys()):
if(ans['relevant']==True):
st.write(":white_check_mark:")
else:
st.write(":x:")
i = i+1
# with col_2:
# if(st.session_state.input_evaluate == "enabled"):
# st.markdown("<div style='fontSize:12px;padding:3px 7px 3px 7px;borderWidth: 0px;borderColor: red;borderStyle: solid;width: fit-content;font-weight:bold;height: fit-content;border-radius: 20px;font-family:Courier New;color:#e28743'>DCG: " +str('%.3f'%(st.session_state.input_ndcg)) + "</div>", unsafe_allow_html = True)
# with col_2_b:
# span_color = "white"
# if("↑" in st.session_state.ndcg_increase):
# span_color = "green"
# if("↓" in st.session_state.ndcg_increase):
# span_color = "red"
# st.markdown("<span style='font-size:30px;color:"+span_color+"'>"+st.session_state.ndcg_increase.split("~")[0] +"</span><span style='font-size:15px;font-family:Courier New;color:"+span_color+"'>"+st.session_state.ndcg_increase.split("~")[1]+"</span>",unsafe_allow_html = True)
with col_3:
if(index == len(st.session_state.questions)):
rdn_key = ''.join([random.choice(string.ascii_letters)
for _ in range(10)])
currentValue = "".join(st.session_state.input_searchType)+st.session_state.input_imageUpload+json.dumps(st.session_state.input_weightage)+st.session_state.input_NormType+st.session_state.input_CombineType+str(st.session_state.input_K)+st.session_state.input_sparse+st.session_state.input_reranker+st.session_state.input_is_rewrite_query+st.session_state.input_evaluate+st.session_state.input_image+st.session_state.input_rad_1+st.session_state.input_reranker+st.session_state.input_hybridType+st.session_state.input_manual_filter
oldValue = "".join(st.session_state.inputs_["searchType"])+st.session_state.inputs_["imageUpload"]+str(st.session_state.inputs_["weightage"])+st.session_state.inputs_["NormType"]+st.session_state.inputs_["CombineType"]+str(st.session_state.inputs_["K"])+st.session_state.inputs_["sparse"]+st.session_state.inputs_["reranker"]+st.session_state.inputs_["is_rewrite_query"]+st.session_state.inputs_["evaluate"]+st.session_state.inputs_["image"]+st.session_state.inputs_["rad_1"]+st.session_state.inputs_["reranker"]+st.session_state.inputs_["hybridType"]+st.session_state.inputs_["manual_filter"]
def on_button_click():
if(currentValue!=oldValue):
st.session_state.input_text = st.session_state.questions[-1]["question"]
st.session_state.answers.pop()
st.session_state.questions.pop()
handle_input()
#re_ranker.re_rank(st.session_state.questions, st.session_state.answers)
with placeholder.container():
render_all()
if("currentValue" in st.session_state):
del st.session_state["currentValue"]
try:
del regenerate
except:
pass
print("------------------------")
#print(st.session_state)
placeholder__ = st.empty()
placeholder__.button("🔄",key=rdn_key,on_click=on_button_click, help = "This will regenerate the responses with new settings that you entered, Note: To see difference in responses, you should change any of the applicable settings")#,type="primary",use_container_width=True)
if(filter_out > 0):
placeholder_no_results.text(str(filter_out)+" result(s) removed due to missing or in-appropriate content")
#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('body' in md['answer']):
res = json.loads(md['answer']['body'])
else:
res = md['answer']
st.session_state['session_id'] = "1234"
chat = st.container()
with chat:
render_answer(res,index)
def render_all():
index = 0
for (q, a) in zip(st.session_state.questions, st.session_state.answers):
index = index +1
#print("answers----")
#print(a)
ans_ = st.session_state.answers[0]
write_user_message(q,ans_)
write_chat_message(a, q,index)
placeholder = st.empty()
with placeholder.container():
render_all()
#generate_images("",st.session_state.image_prompt)
st.markdown("")
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