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from flask import Flask, render_template, jsonify, request
from src.helper import download_hugging_face_embeddings
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.prompts import PromptTemplate
from langchain_groq import ChatGroq
# from langchain.llms import CTransformers
from langchain.chains import RetrievalQA
from langchain_core.prompts import ChatPromptTemplate
from dotenv import load_dotenv
from src.prompt import *
import os
from store_index import create_vector_db
app = Flask(__name__)
# load_dotenv()
# PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
# PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV')
# DATA_PATH = '/kaggle/input/book-pdf'
# DB_FAISS_PATH = r'G:\Chatbot\data\vector'
# Create vector database
#create_vector_db()
print("###333")
# Call the function directly in the cell
'''#Initializing the Pinecone
pinecone.init(api_key=PINECONE_API_KEY,
environment=PINECONE_API_ENV)
index_name="medical-bot"'''
#embeddings = download_hugging_face_embeddings()
# Load the FAISS vector database
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
# also i change here
# db = FAISS.load_local(DB_FAISS_PATH, embeddings)
DB_FAISS_PATH = "data/vector"
print("vector_base is loading from the folder")
db = FAISS.load_local(DB_FAISS_PATH, embeddings,
allow_dangerous_deserialization=True)
# Loading the index
# db = FAISS.load_local(r"G:\Chatbot\DB_FAISS_PATH",embeddings, allow_dangerous_deserialization=True)
# docsearch=Pinecone.from_existing_index(index_name, embeddings)
# Create a ChatPromptTemplate
prompt = ChatPromptTemplate.from_template(prompt_template)
chain_type_kwargs = {"prompt": prompt}
'''PROMPT = PromptTemplate(template=prompt_template,
input_variables=["context", "question"])
chain_type_kwargs = {"prompt": PROMPT}'''
'''llm = CTransformers(model=r"G:\Chatbot\model\llama-2-7b-chat.ggmlv3.q4_0.bin",
model_type="llama",
config={'max_new_tokens': 512,
'temperature': 0.8})'''
# Initialize the LLM
# llm = ChatGroq(groq_api_key=groq_api_key, model_name="Llama3-8b-8192")
# groq_api_key = ('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')
# Load the GROQ and OpenAI API keys
groq_api_key = os.getenv('GROQ_API_KEY')
# os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
# Initialize the LLM
llm = ChatGroq(groq_api_key=groq_api_key,
model_name="Llama3-8b-8192")
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=db.as_retriever(search_kwargs={'k': 2}),
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs)
@app.route("/")
def index():
return render_template('chat.html')
@app.route("/get", methods=["GET", "POST"])
def chat():
msg = request.form["msg"]
input = msg
print(input)
result = qa({"query": input})
print("Response : ", result["result"])
return str(result["result"])
if __name__ == '__main__':
app.run(host="0.0.0.0", port=7860, debug=True)
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