app.py

import openai
import os
# openai.api_key=os.getenv("OPENAI_API_KEY")

from dotenv import load_dotenv
load_dotenv()

from flask import Flask, jsonify, render_template, request
import requests, json

# import nltk
# nltk.download("punkt")


import shutil
from werkzeug.utils import secure_filename
from werkzeug.datastructures import FileStorage

import nltk

from datetime import datetime

import openai
from langchain.llms import OpenAI

from langchain.embeddings.openai import OpenAIEmbeddings
#from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings

from langchain.document_loaders import SeleniumURLLoader, PyPDFLoader

from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter

from langchain.chains import VectorDBQA

from langchain.document_loaders import UnstructuredFileLoader, TextLoader
from langchain import PromptTemplate

from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferWindowMemory

import warnings

warnings.filterwarnings("ignore")


#app = Flask(__name__)

app = Flask(__name__, template_folder="./")

# Create a directory in a known location to save files to.
uploads_dir = os.path.join(app.root_path,'static', 'searchUploads')

os.makedirs(uploads_dir, exist_ok=True)


def pretty_print_docs(docs):
    print(f"\n{'-' * 100}\n".join([f"Document {i + 1}:\n\n" + "Document Length>>>" + str(
        len(d.page_content)) + "\n\nDocument Source>>> " + d.metadata['source'] + "\n\nContent>>> " + d.page_content for
                                   i, d in enumerate(docs)]))


def getEmbeddingModel(embeddingId):
    # if (embeddingId == 1):
    #     embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    # else:
    #     embeddings = OpenAIEmbeddings()
    return OpenAIEmbeddings()


def getLLMModel(LLMID):
    llm = OpenAI(temperature=0.0)
    return llm


def clearKBUploadDirectory(uploads_dir):
    for filename in os.listdir(uploads_dir):
        file_path = os.path.join(uploads_dir, filename)
        print("Clearing Doc Directory. Trying to delete" + file_path)
        try:
            if os.path.isfile(file_path) or os.path.islink(file_path):
                os.unlink(file_path)
            elif os.path.isdir(file_path):
                shutil.rmtree(file_path)
        except Exception as e:
            print('Failed to delete %s. Reason: %s' % (file_path, e))


def loadKB(fileprovided, urlProvided, uploads_dir, request):
    documents = []
    if fileprovided:
        # Delete Files
        clearKBUploadDirectory(uploads_dir)
        # Read and Embed New Files provided
        for file in request.files.getlist('files[]'):
            print("File Received>>>" + file.filename)
            file.save(os.path.join(uploads_dir, secure_filename(file.filename)))
            loader = PyPDFLoader(os.path.join(uploads_dir, secure_filename(file.filename)))
            documents.extend(loader.load())
    else:
        loader = TextLoader('Jio.txt')
        documents.extend(loader.load())

    if urlProvided:
        weburl = request.form.getlist('weburl')
        print(weburl)
        urlList = weburl[0].split(';')
        print(urlList)
        print("Selenium Started", datetime.now().strftime("%H:%M:%S"))
        # urlLoader=RecursiveUrlLoader(urlList[0])
        urlLoader = SeleniumURLLoader(urlList)
        print("Selenium Completed", datetime.now().strftime("%H:%M:%S"))
        documents.extend(urlLoader.load())
        print("inside selenium loader:")
        print(documents)

    return documents


def getRAGChain(customerName,customerDistrict, custDetailsPresent,vectordb):
    chain = RetrievalQA.from_chain_type(
        llm=getLLMModel(0),
        chain_type='stuff',
        retriever=vectordb.as_retriever(),
        verbose=False,
        chain_type_kwargs={
            "verbose": False,
            "prompt": createPrompt(customerName, customerDistrict, custDetailsPresent),
            "memory": ConversationBufferWindowMemory(
                k=3,
                memory_key="history",
                input_key="question"),
        }
    )
    return chain

def createVectorDB(documents):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)
    texts = text_splitter.split_documents(documents)
    print("All chunk List START ***********************\n\n")
    pretty_print_docs(texts)
    print("All chunk List END ***********************\n\n")
    embeddings = getEmbeddingModel(0)
    vectordb = Chroma.from_documents(texts, embeddings)
    return vectordb

def createPrompt(cName, cCity, custDetailsPresent):
    cProfile = "Customer's Name is " + cName + "\nCustomer's lives in or customer's Resident State or Customer's place is " + cCity + "\n"
    print(cProfile)

    template1 = """You role is of a Professional Customer Support Executive and your name is Jio AIAssist.
        You are talking to the below customer whose information is provided in block delimited by <cp></cp>.
        Use the following customer related information (delimited by <cp></cp>) and context (delimited by <ctx></ctx>) to answer the question at the end by thinking step by step alongwith reaonsing steps:
        If you don't know the answer, just say that you don't know, don't try to make up an answer.
        Use the customer information to replace entities in the question before answering\n
        \n"""

    template2 = """
        <ctx>
        {context}
        </ctx>
        <hs>
        {history}
        </hs>
        Question: {question}
        Answer: """

    prompt_template = template1 + "<cp>\n" + cProfile + "\n</cp>\n" + template2
    PROMPT = PromptTemplate(template=prompt_template, input_variables=["history", "context", "question"])
    return PROMPT

vectordb = createVectorDB(loadKB(False, False, uploads_dir, None))

@app.route('/', methods=['GET'])
def test():
    return "Docker hello"

@app.route('/KBUploader')
def KBUpload():
    return render_template("KBTrain.html")

@app.route('/aiassist')
def aiassist():
    return render_template("index.html")

@app.route('/aiSearch')
def html():
    return render_template("AISearch.html")

@app.route('/searchKB')
def KBUpload():
    return render_template("SearchKB.html")

@app.route('/agent/chat/suggestion', methods=['POST'])
def process_json():
    print(f"\n{'*' * 100}\n")
    print("Request Received >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
    content_type = request.headers.get('Content-Type')
    if (content_type == 'application/json'):
        requestQuery = request.get_json()
        print()
        
        relevantDoc=vectordb.similarity_search_with_score(requestQuery['query'],distance_metric="cos", k = 3)
        searchResultArray=[]
        for doc in relevantDoc:
            searchResult = {}
            print(f"\n{'-' * 100}\n")
            searchResult['documentSource']=doc[len(doc)-2].metadata['source']
            searchResult['pageContent']=doc[len(doc)-2].page_content
            searchResult['similarityScore']=str(doc[len(doc)-1])
            print(doc)
            print("Document Source>>>>>>  "+searchResult['documentSource']+"\n\n")
            print("Page Content>>>>>> "+searchResult['pageContent']+"\n\n")
            print("Similarity Score>>>> "+searchResult['similarityScore'])
            print(f"\n{'-' * 100}\n")
            searchResultArray.append(searchResult)        
        print(f"\n{'*' * 100}\n")
        
        return jsonify(botMessage=searchResultArray) 
    else:
        return 'Content-Type not supported!'

@app.route('/file_upload', methods=['POST'])
def file_Upload(): 
        fileprovided=not request.files.getlist('files[]')[0].filename==''
        urlProvided=not request.form.getlist('weburl')[0]==''
        print("*******")
        print("File Provided:"+str(fileprovided))
        print("URL Provided:"+str(urlProvided))
        print("*******")
            
        documents = []
        if fileprovided:
            #Delete Files
            for filename in os.listdir(uploads_dir):
                file_path = os.path.join(uploads_dir, filename)
                print("Clearing Doc Directory. Trying to delete"+file_path)
                try:
                    if os.path.isfile(file_path) or os.path.islink(file_path):
                        os.unlink(file_path)
                    elif os.path.isdir(file_path):
                        shutil.rmtree(file_path)
                except Exception as e:
                    print('Failed to delete %s. Reason: %s' % (file_path, e))
            #Read and Embed New Files provided        
            for file in request.files.getlist('files[]'):
                print("File Received>>>"+file.filename)
                file.save(os.path.join(uploads_dir, secure_filename(file.filename)))
                #loader = UnstructuredFileLoader(os.path.join(uploads_dir, secure_filename(file.filename)), mode='elements')
                loader = PyPDFLoader(os.path.join(uploads_dir, secure_filename(file.filename)))
                documents.extend(loader.load())
        if urlProvided:
            weburl=request.form.getlist('weburl')
            print(weburl)
            urlList=weburl[0].split(';')
            print(urlList)
            print("Selenium Started", datetime.now().strftime("%H:%M:%S"))
            #urlLoader=RecursiveUrlLoader(urlList[0])
            urlLoader=SeleniumURLLoader(urlList)
            print("Selenium Completed", datetime.now().strftime("%H:%M:%S"))
            documents.extend(urlLoader.load())
            
        print(uploads_dir)
        global chain;

        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=150)
        #text_splitter = CharacterTextSplitter(chunk_size=1500, chunk_overlap=150,separator="</Q>")
        texts = text_splitter.split_documents(documents)
        
        print("All chunk List START ***********************\n\n")
        pretty_print_docs(texts)
        
        print("All chunk List END ***********************\n\n")
        
        #embeddings = OpenAIEmbeddings()
        from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
        embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
        global vectordb
        #vectordb = Chroma.from_documents(texts,embeddings)
        vectordb=Chroma.from_documents(documents=texts, embedding=embeddings, collection_metadata={"hnsw:space": "cosine"})
        return render_template("AISearch.html")

if __name__ == '__main__':
    app.run(host='0.0.0.0',  port=int(os.environ.get('PORT', 7860)))