doczyai-pipelines/streamlit/interface_3.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
import numpy as np
from datetime import datetime
import random
import os
import dateutil
import util
import anthropic
import re

REDIRECT_URI = 'https://doczy.aarete.com:8503'
user_list = ['maamseek@aarete.com', 'smahdavian@aarete.com', 'ahinge@aarete.com', 'akadam@aarete.com'
, 'piragavarapu@aarete.com', 'umistry@aarete.com', 'ahutchison@aarete.com', 'bgrunst@aarete.com', 'ddimeglio@aarete.com'
, 'vnair@aarete.com', 'kminhas@aarete.com','dculotta@aarete.com','cbull@aarete.com','sclark@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")

try:
    util.setup_page(REDIRECT_URI)
    _,c1= st.columns([4,1])
    c1.write(f"User: **{st.session_state.user_info['displayName']}**")
    user_mail = st.session_state.user_info['mail']
except:
    user_mail = 'maamseek@aarete.com'


if user_mail in user_list: 

    field_row = st.columns([0.15, 0.45, 0.4])
    with field_row[0]:
        st.write("**Field Group**")
    with field_row[1]:
        field_group = st.selectbox('Field Group',('Unique Key', 'Contract Related', 'Pricing Before Carveouts - I'
            , 'Pricing Before Carveouts - II', 'Carveout Indicator, Code Type and Code #s - I'
            , 'Carveout Indicator, Code Type and Code #s - II', 'Carveout Indicator, Code Type and Code #s - III'
            , 'Optimize Carving Indic.', 'Carveout Method - I', 'Carveout Method - II', 'Provider'
            , 'Timeline'), label_visibility = "collapsed")

    fields = pd.read_csv('contract_fields.csv', encoding='unicode_escape', skipinitialspace=True)
    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()]

    if field_group == 'Unique Key':
        fields = fields[fields['PRIORITY'] == 'A']
    elif field_group == 'Contract Related':
        fields = fields[fields['PRIORITY'] == 'C']
    elif field_group == 'Pricing Before Carveouts - I':
        fields = fields[fields['PRIORITY'] == 'B']
        fields = np.array_split(fields, 2)[0]
    elif field_group == 'Pricing Before Carveouts - II':
        fields = fields[fields['PRIORITY'] == 'B']
        fields = np.array_split(fields, 2)[1]
    elif field_group == 'Carveout Indicator, Code Type and Code #s - I':
        fields = fields[fields['PRIORITY'] == 'F']
        fields = np.array_split(fields, 3)[0]
    elif field_group == 'Carveout Indicator, Code Type and Code #s - II':
        fields = fields[fields['PRIORITY'] == 'F']
        fields = np.array_split(fields, 3)[1]
    elif field_group == 'Carveout Indicator, Code Type and Code #s - III':
        fields = fields[fields['PRIORITY'] == 'F']
        fields = np.array_split(fields, 3)[2]
    elif field_group == 'Carveout Methodology - I':
        fields = fields[fields['PRIORITY'] == 'G']
        fields = np.array_split(fields, 4)[0]
    elif field_group == 'Carveout Methodology - II':
        fields = fields[fields['PRIORITY'] == 'G']
        fields = np.array_split(fields, 4)[1]
    elif field_group == 'Carveout Method - III':
        fields = fields[fields['PRIORITY'] == 'G']
        fields = np.array_split(fields, 4)[2]
    elif field_group == 'Carveout Method - IV':
        fields = fields[fields['PRIORITY'] == 'G']
        fields = np.array_split(fields, 4)[3]
    elif field_group == 'Provider':
        fields = fields[fields['PRIORITY'] == 'D']
    elif field_group == 'Timeline':
        fields = fields[fields['PRIORITY'] == 'E']

    fields['Interrogation Question?'] = fields['Interrogation Question?'].fillna(' ')
    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 70B'
            , 'Titan Text Express'), index=1, 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")


    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",
    )

    # 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','Snippet','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 #'])
    

    question = prompt
    question_with_schema = question
    attempt = 0

    if st.button("Test Configuration"):

        answer_list = []
        snippet_list = []
        page_no_list = []
        attempt = attempt + 1

        for contract in contract_list:
            with open(os.path.join(SOURCE_DIRECTORY, contract[:-4]+'.txt'), 'r') as infile:
                context = infile.read()

            # Add "You must answer in correct JSON format."
            # Add Answer in JSON format: {{
            if llm_selected == "Titan Text Express":
                context = context[:16000]
                prompt_data = f"""Answer the question based only on the information provided between ## and give step by step guide.
                #
                {context}
                #

                Question: {question}
                Answer:"""
                parameters = {
                    "maxTokenCount":512,
                    "stopSequences":[],
                    "temperature":0,
                    "topP":0.9
                    }

                body = json.dumps({"inputText": prompt_data, "textGenerationConfig": parameters})
                model_id = "amazon.titan-text-express-v1"  # change this to use a different version from the model provider

            elif llm_selected == 'Llama 2 Chat 70B':
                context = context[:7000]
                prompt_data = f"""Answer the question based only on the information provided between ## and give step by step guide.
                ##
                {context}
                ##

                Question: {question}
                Answer:"""
                payload={
                    "prompt":"[INST]"+ prompt_data +"[/INST]",
                    "max_gen_len":512,
                    "temperature":0.0,
                    "top_p":0.9
                }
                body=json.dumps(payload)
                model_id="meta.llama2-70b-chat-v1"

            elif llm_selected in ['Claude Instant', 'Claude 2']:

                prompt_data = f"""

                Human: Use the following pieces of context to provide a concise answer to the questions at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.

                {context}

                Question: {question_with_schema}

                Assistant:"""
                body = json.dumps(
                    {"prompt": anthropic.HUMAN_PROMPT + prompt_data + anthropic.AI_PROMPT, 
                     "max_tokens_to_sample": 1024,
                     "temperature":0.0,
                     "top_p":1,
                     "top_k":250,
                     "stop_sequences":[anthropic.HUMAN_PROMPT]
                      })
                if llm_selected == "Claude 2":
                    model_id = "anthropic.claude-v2:1"
                else:
                    model_id = "anthropic.claude-instant-v1"

            response = bedrock_runtime.invoke_model(
                body=body, 
                modelId=model_id, 
                accept="application/json", 
                contentType="application/json"
            )
            response_body = json.loads(response.get("body").read())

            if llm_selected == "Titan Text Express":
                response_text = response_body.get("results")[0].get("outputText")
            elif llm_selected == 'Llama 2 Chat 70B':
                response_text = response_body['generation']
            elif llm_selected in ['Claude Instant', 'Claude 2']:
                response_text = response_body['completion']

            # st.write(response_text)
            try:
                response_text = "{" + response_text.split("{",1)[1]
                response_text = response_text.split("}",1)[0] + "}"
                response_dict = json.loads(response_text)
            except:
                response_dict = {field:response_text}

            answer = response_dict.get(field, " ")
            answer_list.append(answer)
            location = context.find(answer) if isinstance(answer, str) and answer != "" else -1
            snippet = ' '.join(context[:location].split()[-25:]) + ' ' + ' '.join(context[location:].split()[:30]) if location != -1 else ' '
            page_no = " " if location == -1 else context[:location].rsplit("Start of Page No. = ", 1)[1] if len(context[:location].rsplit(
            "Start of Page No. = ", 1)) > 1 else context[:location].rsplit("Start of Page No. = ", 1)[0]
            page_no = re.search(r'\d+', page_no).group() if page_no != " " and re.search(r'\d+', page_no) is not None else ""
            snippet_list.append(snippet)
            page_no_list.append(page_no)

        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 "do not have" not in str(answer) else " " for answer in answer_list]
            answer_list = [answer if "does not specify" not in str(answer) else " " for answer in answer_list]
            answer_list = [answer if "does not explicitly" 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'] = ' '
        df['Snippet'] = snippet_list
        df['New Page Number'] = page_no_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'
            ,'Snippet','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'
            ,'Snippet', '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")