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app.py
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app.py
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from flask import Flask, render_template, request, redirect, send_file
from langchain.llms import HuggingFaceHub
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
import os
import sys
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.document_loaders import TextLoader
from langchain.document_loaders import OnlinePDFLoader
from pypdf import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.text_splitter import CharacterTextSplitter
import json
import re
import random
import spacy
import platform
app = Flask(__name__)
global isServer
if platform.system() == "Darwin":
isServer = False
elif platform.system() == "Windows":
isServer = False
else:
isServer = True
global baseFilePath
global jsonPath
if isServer:
baseFilePath = "/data/"
jsonPath = baseFilePath + "keyvalues/redacted.json"
else:
baseFilePath = "./"
jsonPath = baseFilePath + "keyvalues/redacted.json"
access_token = os.environ.get("ACCESS_TOKEN")
lastnames = ["Smith", "Johnson", "Williams", "Jones", "Brown", "Davis", "Miller", "Wilson", "Moore", "Taylor", "Anderson", "Thomas", "Jackson", "White", "Harris", "Martin", "Thompson", "Garcia", "Martinez", "Robinson", "Clark", "Rodriguez", "Lewis", "Lee", "Walker", "Hall", "Allen", "Young", "Hernandez", "King", "Wright", "Lopez", "Hill", "Scott", "Green", "Adams", "Baker", "Gonzalez", "Nelson", "Carter", "Mitchell", "Perez", "Roberts", "Turner", "Phillips", "Campbell", "Parker", "Evans", "Edwards", "Collins", "Stewart", "Sanchez", "Morris", "Rogers", "Reed", "Cook", "Morgan", "Bell", "Murphy", "Bailey", "Rivera", "Cooper", "Richardson", "Cox", "Howard", "Ward", "Torres", "Peterson", "Gray", "Ramirez", "James", "Watson", "Brooks", "Kelly", "Sanders", "Price", "Bennett", "Wood", "Barnes", "Ross", "Henderson", "Coleman", "Jenkins", "Perry", "Powell", "Long", "Patterson", "Hughes", "Flores", "Washington", "Butler", "Simmons", "Foster", "Gonzales", "Bryant", "Alexander", "Russell", "Griffin", "Diaz", "Hayes"]
def generateName():
return names[random.randint(0, len(names)-1)].title() + " " + lastnames[random.randint(0, len(lastnames)-1)]
def valueInJSON(value, key):
try:
if data[key][value] != "":
return data[key][value]
except KeyError:
return ""
os.makedirs(baseFilePath + "documents/", exist_ok=True)
os.makedirs(baseFilePath + "text/", exist_ok=True)
os.makedirs(baseFilePath + "redacted/", exist_ok=True)
os.makedirs(baseFilePath + "chroma_db/", exist_ok=True)
os.makedirs(baseFilePath + "keyvalues/", exist_ok=True)
if not os.path.exists(jsonPath):
with open(jsonPath, 'w+') as file:
json.dump({"names": {}, "addresses": {}, "companyNames": {}, "phoneNumbers": {}, "emails": {}}, file, indent=2)
with open(jsonPath, 'r') as file:
data = json.load(file)
with open('names.txt', 'r') as file:
names = file.read().splitlines()
names = [x.lower() for x in names]
#with open('addresses.txt', 'r') as file:
# addresses = file.read().splitlines()
def redactDocument(filepath):
#TAKES A DOCUMENT AND REDACTS SENSITIVE INFO SUCH AS NAMES, ADDRESSES, PHONE NUMBERS, EMAILS, ETC.
file = open(filepath, "r")
filename = filepath.split("/")[-1].split(".")[0]
file = file.readlines()
text = ""
for line in file:
text += line
lineOfText = NER(line)
#NAMES
for word in lineOfText.ents:
if word.label_ == "PERSON" and " " in word.text and word.text.lower().split(' ')[0] in names:
inJson = valueInJSON(word.text, "names")
if inJson != "":
fakeName = inJson
else:
fakeName = generateName()
data['names'][word.text] = fakeName
text = text.replace(word.text, fakeName)
text = text.replace(word.text+"'s", fakeName+"'s")
text = text.replace(word.text+"'", fakeName+"'")
text = text.replace(word.text.split(' ')[1], fakeName.split(' ')[1])
else:
pass
#EMAIL
#if re.search(r'\S+@\S+', line):
# for i in re.findall(r'\S+@\S+', line):
# if i in data['emails']:
# fakeEmail = data['emails'][i]
# else:
# emailProviders = ["gmail.com", "yahoo.com", "outlook.com", "hotmail.com", "aol.com", "icloud.com", "protonmail.com"]
# fakeEmail = os.urandom(10).hex() + emailProviders[random.randint(0, len(emailProviders)-1)]
# data['emails'][i] = fakeEmail
# text = text.replace(i, fakeEmail)
txtFile = baseFilePath + "redacted/" + filename + ".txt"
with open(txtFile, "w+") as f:
f.write(text)
return text
global isFirst
isFirst = True
global history
history = [("", "")]
global embeddings
if isServer:
embeddings = HuggingFaceEmbeddings()
else:
#embeddings = HuggingFaceEmbeddings()
model = "BAAI/bge-base-en-v1.5"
encode_kwargs = {
"normalize_embeddings": True
}
embeddings = HuggingFaceBgeEmbeddings(
model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
)
def hideOutput():
sys.stdout = open(os.devnull, 'w')
sys.stderr = open(os.devnull, 'w')
def showOutput():
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
def prepareLLM():
#PREPARES CHROMA DB AND ACCESSES THE MIXTRAL LLM
db = Chroma(persist_directory=baseFilePath + "chroma_db", embedding_function=embeddings)
retriever = db.as_retriever(search_kwargs={'k': 1})
if isServer:
llm = HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_kwargs={"temperature": 0.1, "max_new_tokens": 700})
else:
llm = HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_kwargs={"temperature": 0.1, "max_new_tokens": 700},huggingfacehub_api_token=access_token)
global qa
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
def question(history, text):
#global isFirst
#if isFirst:
prepareLLM()
# isFirst = False
with open(jsonPath, 'r') as file:
jsonValues = json.load(file)
#REDACTING SENSITIVE INFO IN REQUEST
for key in jsonValues:
for value in jsonValues[key]:
if value in text:
text = text.replace(value, jsonValues[key][value])
if value.lower() in text:
text = text.replace(value.lower(), jsonValues[key][value])
query = "You are a helpful assistant. Generate responses exclusively from the information contained in the documents. In the event that a user inquiry seeks information not explicitly stated in the documents, refrain from providing an answer. Exercise precision by relying solely on the information explicitly presented in the documents; avoid making inferences, assumptions, or speculations beyond what is explicitly mentioned. User Prompt: " + text
result = qa({"query": query})
history.append((text, result['result']))
resultValue = result['result']
print(resultValue)
#UNREDACTING THE RESULT
for key in jsonValues:
for value in jsonValues[key]:
resultValue = resultValue.replace(jsonValues[key][value], value)
return resultValue
def extractText(file):
#TAKING A PDF FILE AND CONVERTING IT TO A .TXT IN THE "TEXT" FOLDER
reader = PdfReader(file)
filename = os.path.splitext(os.path.basename(file))[0]
text = ""
for page in reader.pages:
text += page.extract_text() + "\n"
txtFile = baseFilePath + "text/" + filename + ".txt"
#with open(txtFile, "w+") as f:
#make utf 8
with open(txtFile, "w+") as f:
#f.write(re.sub(r'\s+', ' ', text))
#write text file in utf-8 format
f.write(text)
#f.write(text)
redactDocument(txtFile)
print(data)
with open(jsonPath, 'w') as file:
json.dump(data, file, indent=2)
def newFile(files, filepaths):
count = 0
for file in files:
print("Processing: " + filepaths[count].split("/")[-1])
#EXTRACTING TEXT AND PROCESSING PDF
extractText(filepaths[count])
redactedFile = filepaths[count].split("/")[-1].split(".")[0]
redactedFile = baseFilePath + "redacted/" + redactedFile + ".txt"
loader = TextLoader(redactedFile, encoding='UTF-8')
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000, chunk_overlap=0, separators=[" ", ",", "\n"]
)
texts = text_splitter.split_documents(documents)
print(texts)
chromaDirectory = baseFilePath + "chroma_db"
Chroma.from_documents(texts, embeddings, persist_directory=chromaDirectory)
print("Done processing: " + filepaths[count].split("/")[-1])
count = count + 1
@app.route('/', methods=['GET', 'POST'])
def chat():
if request.method == 'POST':
#HANDLES FILE UPLOADS
global NER
NER = spacy.load("en_core_web_lg")
files = request.files.getlist('pdf-files[]')
filenames = []
for file in files:
filenames.append(file.filename)
filepaths = []
documents_directory = baseFilePath + "documents/"
os.makedirs(documents_directory, exist_ok=True)
count = 0
for file in files:
filepath = os.path.join(documents_directory, filenames[count])
with open(filepath, 'wb') as f:
f.write(file.read())
filepaths.append(filepath)
count = count + 1
newFile(files, filepaths)
return "Success"
#MAIN PAGE LOAD
documents_directory = baseFilePath + "documents/"
documents = os.listdir(documents_directory)
return render_template('chat.html', history=[("", "")], documents=documents)
@app.route('/chat', methods=['GET'])
def askQuestion():
#PROCESSING USER QUESTIONS
text = request.args.get('message')
display = question(history, text)
return display
@app.route('/document', methods=['GET'])
def document():
#RETURNS DOCUMENTS
name = request.args.get('name')
path = os.path.join("documents", name)
return send_file(path)
@app.route('/clear', methods=['GET', 'POST'])
def clear():
#CLEARS ALL FILES
documents_directory = baseFilePath + "documents/"
documents = os.listdir(documents_directory)
for document in documents:
os.system("rm -rf " + os.path.join(documents_directory, document))
documents_directory = baseFilePath + "text/"
documents = os.listdir(documents_directory)
for document in documents:
os.system("rm -rf " + os.path.join(documents_directory, document))
documents_directory = baseFilePath + "redacted/"
documents = os.listdir(documents_directory)
for document in documents:
os.system("rm -rf " + os.path.join(documents_directory, document))
chroma_directory = baseFilePath + "chroma_db/"
os.system("rm -rf " + chroma_directory)
with open(jsonPath, 'w') as file:
json.dump({"names": {}, "addresses": {}, "companyNames": {}, "phoneNumbers": {}, "emails": {}}, file, indent=2)
return redirect('/')
if __name__ == '__main__':
app.run(debug=True)