from flask import Flask, request, jsonify
from PyPDF2 import PdfReader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
# from config import DEBUG, SECRET_KEY, DATABASE_URI,OPENAI_API_KEY

# import nltk
import sys
import os

app = Flask(__name__)

# @app.route('/',methods=['GET'])
# def home():
#     return jsonify({
#         'name':'Saravanan',
#         'email':'saravanan@idealtraits.com'
#     })


input=sys.argv[0]
print (input)

# os.environ["OPENAI_API_KEY"]='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'
# basedir = os.path.abspath(os.path.dirname(__file__))
# pdflocation = os.path.join(basedir,'idealpolicy.pdf')
# reader =PdfReader(pdflocation)

# raw_text = ''
# for i, page in enumerate(reader.pages):
#   text = page.extract_text()
#   if text:
#     raw_text += text

# text_splitter = CharacterTextSplitter(
#     separator = "\n",
#     chunk_size = 1000,
#     chunk_overlap = 200,
#     length_function = len,
# )
# texts = text_splitter.split_text(raw_text)

# embeddings = OpenAIEmbeddings()
# docsearch = FAISS.from_texts(texts, embeddings)

# chain = load_qa_chain(OpenAI(), chain_type="stuff")

# @app.route('/jobdetails',methods=['POST'])
# def jobdetail():
#     jobdet = request.json['jobdetails']+", Please analyze above content, this is suitable for our police if not means return 'Validation Failed' and give two words why. If validation is ture means return 'success'"
#     query=jobdet
#     docs = docsearch.similarity_search(query)
#     response = chain.run(input_documents=docs,question=query)

#     return jsonify({'jobdet':jobdet,'response':response})

# if __name__ =='__main__':
#     app.run(debug=True)