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

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")

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',('10', '20', '30', '50', 'All'), index=0, label_visibility = "collapsed")

seed_row = st.columns([0.15, 0.45, 0.4])
with seed_row[0]:
    st.write("**Seed Value**")
with seed_row[1]:
    seed_value = st.text_input("**Seed Value**", value = 20, label_visibility = "collapsed")

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")

random.seed(seed_value)
try:
    contract_list = sorted(random.choices(os.listdir(SOURCE_DIRECTORY), k=int(contract_count)))
except:
    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'])]

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')

# sample = dict(zip(sample.Filename, sample.Answer))
# actual_value_list = [sample.get(contract.rsplit('.',1)[0]+'.txt', ' ') for contract in contract_list]

# AWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID')
# AWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY')
# AWS_SESSION_TOKEN=os.getenv('AWS_SESSION_TOKEN')

# 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','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 #'])
attempt = 0

if llm_selected in ['Llama 2 Chat 13B', 'Llama 2 Chat 70B']:
    k_value = 14
else:
    k_value = 25


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]
    df['Snippet'] = [str(doc[0].page_content) for doc in doc_list]
    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'])
    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))