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from flask import Flask, request, jsonify
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
# To comment in production usage
# import Constants
import os
key = "hf_" + "TFQcwtpWpItqrR" +"kKazmgBASKDIsTnGDeqM"
os.environ["HUGGINGFACEHUB_API_TOKEN"] = key
app = Flask(__name__)
# try:
loader = TextLoader("./data/app.txt")
document = loader.load()
# Split the document into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(document)
# Create embeddings
embedding = HuggingFaceEmbeddings(model_name = "sentence-transformers/all-mpnet-base-v2")
db = FAISS.from_documents(docs, embedding)
# Load the Question-Answering chain
llm = HuggingFaceEndpoint(repo_id="google/flan-t5-xxl", temperature=0.8, model_kwargs={"max_length": 512})
chain = load_qa_chain(llm, chain_type="stuff")
# except Exception as e:
# print("Recived Setup error: ", e)
def process_query(query):
# os.system("cls")
# try:
querySimilarDocs = db.similarity_search(query)
res = chain.run(input_documents = querySimilarDocs, question = query)
return res
# except Exception as e:
# print("Received process error: ", e)
# # return "An Error occurred!!"
# return e
@app.route('/query', methods=['POST'])
def process_request():
# Debugging...
for name, value in os.environ.items():
print("{0}: {1}".format(name, value))
# try:
data = request.get_json()
user_input = data['query']
response = process_query(user_input)
return jsonify({"response": response})
# except Exception as e:
# print("Received Process request error: ", e)
# return jsonify({"response": str(e)})
## Development phase use case only
# if __name__ == '__main__':
# app.run(debug=True)
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