doczyai-pipelines/streamlit/interface_3_rag.py

import json
import boto3
from langchain.prompts import PromptTemplate
from langchain.embeddings.bedrock import BedrockEmbeddings
from langchain.llms.bedrock import Bedrock
from langchain_community.vectorstores import Chroma
from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME, SOURCE_DIRECTORY
from langchain.chains import RetrievalQA

import streamlit as st
from streamlit_extras.add_vertical_space import add_vertical_space

import pandas as pd
from datetime import datetime
import random
import os
import dateutil
import util

REDIRECT_URI = 'http://172.29.20.126:8503'
user_list = ['maamseek@aarete.com', 'smahdavian@aarete.com', 'ahinge@aarete.com', 'akadam@aarete.com', 'pkatariya@aarete.com'
, 'piragavarapu@aarete.com', 'umistry@aarete.com', 'ahutchison@aarete.com', 'bgrunst@aarete.com', 'ddimeglio@aarete.com'
, 'vnair@aarete.com', 'kminhas@aarete.com', 'fmohiuddin@aarete.com', 'slitewka@aarete.com', 'qdoest@aarete.com', 'bkoryga@aarete.com', 'bcielecki@aarete.com', 'mszymanski@aarete.com']

st.set_page_config(layout = "wide")
# Sidebar contents
with st.sidebar:
    st.title("Doczy.AI ™")
    st.markdown(
        """
    ## About
    This app extracts data from contracts
 
    """
    )
    add_vertical_space(15)
    # st.write("Doczy")

# util.setup_page(REDIRECT_URI)
# if st.session_state.user_info['mail'] in user_list: 
if 'maamseek@aarete.com' in user_list:

    fields = pd.read_csv('contract_fields.csv', encoding='unicode_escape', skipinitialspace=True)
    fields = fields[fields['PRIORITY'].isin(['A','C'])]
    field_values = pd.read_csv('contract_field_values.csv', encoding='unicode_escape', skipinitialspace=True)
    fields = fields.drop_duplicates(subset='Field Name', keep="first").sort_values('Field Name')
    fields = fields[~fields['Field Name'].isnull()]
    field_prompt_mapping = dict(zip(fields['Field Name'], fields['Interrogation Question?']))

    field_row = st.columns([0.15, 0.45, 0.4])
    with field_row[0]:
        st.write("**Field Name**")
    with field_row[1]:
        field = st.selectbox('Field Name',sorted(set(field_prompt_mapping.keys())), index=0, label_visibility = "collapsed")

    contract_count_row = st.columns([0.15, 0.45, 0.4])
    with contract_count_row[0]:
        st.write("**# of Contracts**")
    with contract_count_row[1]:
        contract_count = st.selectbox('Contract count',('1', '10', '20', '30', '50', 'All'), index=1, label_visibility = "collapsed")

    seed_row = st.columns([0.15, 0.45, 0.4])


    contract_list = sorted(os.listdir(SOURCE_DIRECTORY))

    # to be deleted later
    contract_list = [contract for contract in contract_list if contract.replace(' MU','').replace('_MU','').replace('.txt','') in list(field_values['(internal) Document Name'])]

    with seed_row[0]:
        if contract_count in ['10', '20', '30', '50']:
            st.write("**Seed Value**")
        elif contract_count == '1':
            st.write("**Contract Name**")
    with seed_row[1]:
        if contract_count in ['10', '20', '30', '50']:
            seed_value = st.text_input("**Seed Value**", value = 20, label_visibility = "collapsed")
            random.seed(seed_value)
            contract_list = sorted(random.choices(os.listdir(SOURCE_DIRECTORY), k=int(contract_count)))
        elif contract_count == '1':
            contract_name = st.selectbox('Contract Name', (contract_list), label_visibility = "collapsed")
            contract_list = [contract_name]


    llm_row = st.columns([0.15, 0.45, 0.4])
    with llm_row[0]:
        st.write("**Langauge Model**")
    with llm_row[1]:
        llm_selected = st.selectbox('Langauge Model',('Claude 2', 'Claude Instant', 'Llama 2 Chat 13B', 'Llama 2 Chat 70B'
            , 'Titan Text Express'), label_visibility = "collapsed")

    st.write("**Prompt**")
    sequence_input = field_prompt_mapping.get(field)
    prompt_row = st.columns([0.8, 0.2])
    with prompt_row[1]:
        if st.button("Clear Prompt"):
            sequence_input = ''
        if st.button("Back to default"):
            prompt = sequence_input
        st.button("Save Prompt")
    with prompt_row[0]:
        prompt = st.text_area("**Prompt**", sequence_input, height = 150, label_visibility = "collapsed")

    page_list_all = []
    for contract in contract_list:
        page_list = []
        with open(os.path.join(SOURCE_DIRECTORY, contract[:-4]+'.txt'), 'r') as infile:
            text = infile.read()
        page_count = text.count('Start of Page No. = ')
        for page in range(page_count+1):
            file_path = "SOURCE_DOCUMENTS\\" + f'{contract[:-4]}_page{page}.txt'
            dict_with_pages = { 'source': { '$eq': file_path }}
            page_list.append(dict_with_pages)
        page_list_all.append(page_list)
    contract_txt_mapping = dict(zip(contract_list, page_list_all))

    column_name = fields.loc[fields['Field Name'] == field, 'SF_DB_COL_NAME'].iloc[0]
    column_list = ['(internal) Document Name', '(Internal) Carveout ID', column_name]
    if column_name+'_PG' in list(field_values.columns):
        column_list.append(column_name+'_PG')
    field_values = field_values[column_list]
    field_values.rename(columns={'(internal) Document Name': 'Contract Name', column_name: 'Actual Value Stored'
        , '(Internal) Carveout ID': 'Contract ID', column_name+'_PG': 'Original Page Number'}, inplace=True)
    field_values = field_values.drop_duplicates(subset='Contract Name', keep="first").sort_values('Contract Name')

    # Setup bedrock
    bedrock_runtime = boto3.client(
        service_name="bedrock-runtime",
        region_name="us-east-1",
    )

    # Define the retreiver
    # load the vectorstore
    if "EMBEDDINGS" not in st.session_state:
        EMBEDDINGS = BedrockEmbeddings(
            client=bedrock_runtime,
            model_id="amazon.titan-embed-text-v1",
        )
        st.session_state.EMBEDDINGS = EMBEDDINGS

    if "DB" not in st.session_state:
        DB = Chroma(
            persist_directory=PERSIST_DIRECTORY,
            embedding_function=st.session_state.EMBEDDINGS,
            client_settings=CHROMA_SETTINGS,
        )
        st.session_state.DB = DB

    # if "RETRIEVER" not in st.session_state:
    #     # { "source": { '$eq': "SOURCE_DOCUMENTS\\A.1_UH_Health_System_eff_2_1_08 (1)_page0.txt"} }
    #     RETRIEVER = DB.as_retriever(search_kwargs={"filter": { "source": { '$eq': "SOURCE_DOCUMENTS\\A.1_UH_Health_System_eff_2_1_08 (1)_page0.txt"} }, "k": 2})
    #     st.session_state.RETRIEVER = RETRIEVER

    # if "LLM" not in st.session_state:
    if llm_selected == 'Titan Text Express':
        LLM = Bedrock(
            model_id="amazon.titan-text-express-v1",
            client=bedrock_runtime,
            model_kwargs={
                "maxTokenCount": 512,
                "stopSequences": [],
                "temperature": 0,
                "topP": 1,
            }
        )
    elif llm_selected == 'Llama 2 Chat 70B':
        LLM = Bedrock(
            model_id="meta.llama2-70b-chat-v1",
            client=bedrock_runtime,
            model_kwargs={
                "max_gen_len": 512,
                "temperature": 0,
                # "topP": 0.9,
            }
        )
    elif llm_selected == 'Llama 2 Chat 13B':
        LLM = Bedrock(
            model_id="meta.llama2-13b-chat-v1",
            client=bedrock_runtime,
            model_kwargs={
                "max_gen_len": 512,
                "temperature": 0,
                # "topP": 0.9,
            }
        )
    elif llm_selected == 'Claude Instant':
        LLM = Bedrock(
            model_id="anthropic.claude-instant-v1",
            client=bedrock_runtime,
            model_kwargs={
                # "max_tokens_to_sample": 512,
                "temperature": 0,
                # "topP": 0.9,
            }
        )
    elif llm_selected == 'Claude 2':
        LLM = Bedrock(
            model_id="anthropic.claude-v2:1",
            client=bedrock_runtime,
            model_kwargs={
                # "max_tokens_to_sample": 512,
                "temperature": 0,
                # "topP": 0.9,
            }
        )
    st.session_state["LLM"] = LLM

    # if "QA" not in st.session_state:
    #     prompt, memory = model_memory()

    #     QA = RetrievalQA.from_chain_type(
    #         llm=LLM,
    #         chain_type="stuff",
    #         retriever=RETRIEVER,
    #         return_source_documents=True,
    #         chain_type_kwargs={"prompt": prompt, "memory": memory},
    #     )
    #     st.session_state["QA"] = QA

    # df = pd.DataFrame(columns=['Contract Name','Contract ID','Actual Value Stored','New Extracted value','Confidence Level','Snippet',
    #     'Original Page Number', 'New Page Number', 'Revised Prompt', 'Result'])
    df = pd.DataFrame(columns=['Contract Name','Raw value','New Extracted value','Confidence Level','Snippet1','Snippet2','Snippet3'
        ,'Snippet4','Snippet5','Snippet6','Snippet7','Snippet8','Snippet9','Snippet10','New Page Number'
        , 'Revised Prompt', 'Result'])
    try:
        history = pd.read_csv('history.csv')
    except:
        history = pd.DataFrame(columns=['Field Name','# Contracts Tested', 'Username', 'Date/Time', 'Accuracy', 'Attempt #'])
    attempt = 0

    if llm_selected in ['Llama 2 Chat 13B', 'Llama 2 Chat 70B']:
        k_value = 10
    else:
        k_value = 20


    if st.button("Test Configuration"):

        answer_list = []
        doc_list = []
        response_list = []
        score_list = []
        attempt = attempt + 1

        for page_list in page_list_all:
            RETRIEVER = st.session_state.DB.as_retriever(search_kwargs={"filter": {"$or": page_list}, "k": k_value})
            QA = RetrievalQA.from_chain_type(
                llm=st.session_state["LLM"],
                chain_type="stuff",
                retriever=RETRIEVER,
                return_source_documents=True,
                # chain_type_kwargs={"prompt": prompt, "memory": None},
            )
            score = st.session_state.DB.similarity_search_with_relevance_scores(prompt, k=4, filter={"$or": page_list})
            score_list.append(max(d[1] for d in score))
            response = QA(prompt)
            answer, docs = response["result"], response["source_documents"]
            answer_list.append(answer)
            doc_list.append(docs)
            response_list.append(response)

        df['Raw value'] = answer_list
        # post-processing
        if 'Date' in field:
            date_list = []
            for answer in answer_list:
                try:
                    extracted_date = dateutil.parser.parse(str(answer).replace('"',''), fuzzy=True).date()
                except:
                    extracted_date = " "
                date_list.append(extracted_date)
            answer_list = date_list
        elif llm_selected in ['Llama 2 Chat 13B', 'Llama 2 Chat 70B']:
            answer_list = [answer.rstrip(".") for answer in answer_list]
            answer_list = [answer if "I don't know" not in str(answer) else " " for answer in answer_list]
            answer_list = [answer if "N/A" not in str(answer) else " " for answer in answer_list]
            answer_list = [answer if "does not contain" not in str(answer) else " " for answer in answer_list]
            answer_list = [answer if "None" not in str(answer) else " " for answer in answer_list]
            answer_list = [answer if "Not specified in the contract" not in str(answer) else " " for answer in answer_list]
            answer_list = [answer if "Not applicable" not in str(answer) else " " for answer in answer_list]
        elif llm_selected in ['Claude 2', 'Claude Instant']:
            answer_list = [answer if "Unfortunately, I do not have enough context" not in str(answer) else " " for answer in answer_list]
            answer_list = [answer.rstrip(".") for answer in answer_list]
        else:
            answer_list = [answer.rstrip(".") for answer in answer_list]
            # answer_list = [str(x).rsplit(':',1)[0] if len(str(x).rsplit(':',1)) < 2  else str(x).rsplit(':',1)[1] for x in answer_list]

        df['Contract Name'] = contract_list
        # to be deleted later
        df['Contract Name'] = [contract.replace(' MU','').replace('_MU','').replace('.txt','') for contract in contract_list] 

        df['New Extracted value'] = answer_list
        df['Confidence Level'] = [round(score, 2) for score in score_list]
        Snippet = []
        count = 0
        for i in range(int(contract_count)):
            for j in range(10):  
                try:
                    content = str(doc_list[i][j].page_content)
                except:
                    content = " "
                Snippet.append(content)
        # df['Snippet1'] = [str(doc[0].page_content) for doc in doc_list]
        df['Snippet1'] = Snippet[:int(contract_count)]
        df['Snippet2'] = Snippet[int(contract_count):2*int(contract_count)]
        df['Snippet3'] = Snippet[2*int(contract_count):3*int(contract_count)]
        df['Snippet4'] = Snippet[3*int(contract_count):4*int(contract_count)]
        df['Snippet5'] = Snippet[4*int(contract_count):5*int(contract_count)]
        df['Snippet6'] = Snippet[5*int(contract_count):6*int(contract_count)]
        df['Snippet7'] = Snippet[6*int(contract_count):7*int(contract_count)]
        df['Snippet8'] = Snippet[7*int(contract_count):8*int(contract_count)]
        df['Snippet9'] = Snippet[8*int(contract_count):9*int(contract_count)]
        df['Snippet10'] = Snippet[9*int(contract_count):]
        df['New Page Number'] = [int(str(doc[0].metadata["source"]).rsplit('_page')[1].replace('.txt',''))+1 for doc in doc_list]
        df['Revised Prompt'] = [prompt] * len(contract_list)

        df = pd.merge(df, field_values, how ='left', on ='Contract Name')
        
        answer_list = list(df['New Extracted value'])
        df['Actual Value Stored'] = pd.to_datetime(df['Actual Value Stored'],errors='coerce').dt.date
        df.fillna(" ", inplace=True)
        actual_value_list = list(df['Actual Value Stored'])
        result_list = [i==j for i, j in zip(actual_value_list, answer_list)]
        df['Result'] = [str(x) for x in result_list]
        df = df[~df['Contract ID'].isnull()]
        if 'Original Page Number' in df.columns:
            df = df[['Contract Name','Contract ID','Actual Value Stored','Raw value','New Extracted value','Confidence Level'
            ,'Snippet1','Snippet2','Snippet3','Snippet4','Snippet5','Snippet6','Snippet7','Snippet8','Snippet9','Snippet10'
            ,'Original Page Number', 'New Page Number', 'Revised Prompt', 'Result']]
        else:
            df = df[['Contract Name','Contract ID','Actual Value Stored','Raw value','New Extracted value','Confidence Level'
            ,'Snippet1','Snippet2','Snippet3','Snippet4','Snippet5','Snippet6','Snippet7','Snippet8','Snippet9','Snippet10'
            , 'New Page Number', 'Revised Prompt', 'Result']]

        try:
            accuracy = round(sum(bool(x) for x in result_list) * 100 / len(list(df['Result'])), 2)
        except:
            accuracy = 'NA'

        history.loc[len(history.index)] = [field, str(contract_count), None, datetime.now().strftime("%Y-%m-%d %H:%M:%S"), accuracy, attempt]
        # df.to_csv("RESULTS\\"+field.replace("?","").replace("/","_")+'-'+llm_selected+'.csv', index=False)
        history.to_csv('history.csv', index=False)

    # df_copy = df.set_index(df.columns[0]).copy()
    # df_2_copy = history.set_index(history.columns[0]).copy()
    st.dataframe(df)
    st.dataframe(history)


    # @st.cache_data 
    # def convert_df(df):
    #    return df.to_csv(index=False).encode('utf-8')

    # csv = convert_df(edited_df)

    # buttons = st.columns(3)
    # with buttons[0]:
    #     st.button("Save All Imputations") 
    # with buttons[1]:
    #     st.download_button("Download Table", csv, "file.csv", "text/csv", key='download-csv')
    # with buttons[2]:
    #     st.button("Kickoff Database Integration") 


    st.write(column_name)
    st.write(len(contract_list))

else:
    st.write("Access Denied")