app.py

import os
import langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import TextLoader
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.schema import Document
import streamlit as st
from langchain_groq import ChatGroq
from langchain.text_splitter import CharacterTextSplitter
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
text_splitter = RecursiveCharacterTextSplitter(chunk_size=75, chunk_overlap=0, separators=["\n",'.  '])
loader = TextLoader('data.txt')
docs = loader.load()
split_docs = text_splitter.split_documents(docs)
# Iterate over the Document objects and extract the content and metadata using the appropriate attributes
split_docss = [Document(page_content=t.page_content, metadata=t.metadata) for t in split_docs]
embeddings = HuggingFaceBgeEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectorstore = FAISS.from_documents(split_docss,embeddings)
retriever=vectorstore.as_retriever()
retriever.search_kwargs['k'] = 5
os.environ['GROQ_API_KEY']='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'
def preprocess(text):
  return "\n".join(x.page_content for x in text)
def format_docs(docs):
  return "\n".join(doc.page_content for doc in docs)
def printer(text):
  print(text)
  return text

llm = ChatGroq(temperature=0, model_name="mixtral-8x7b-32768", streaming=True, verbose=False)
# llama3-8b-8192
prompt='''You are a coupon recommender chatbot.
Understand the question with the category of the item and the location:
{summary}
and answer with only relevant coupons:
{context}

Instructions:
List only the coupons that match the category of the item along with the location in the question.Use three sentences maximum and keep the answer concise.
 '''
template=PromptTemplate(template=prompt,input_variables=['summary','context'])
rag_chain=({ "summary": RunnablePassthrough(), "context": retriever | format_docs ,
             } | template|printer| llm| StrOutputParser())

st.set_page_config(page_title="Lloyds Mobile Rewards Section", layout="wide")

st.title("Mock Rewards Section")
st.header("Welcome to Your Rewards")
st.write("Find exclusive offers and discounts just for you!")

st.sidebar.title("Check your eligibility to turn on the discover mode!")
# category = st.sidebar.selectbox("Select Category", ["All", "Electronics", "Food", "Groceries"])
if 'count' not in st.session_state:
    st.session_state.count = 0

enable_controls = st.session_state.count == 5

def increment_count():
    st.session_state.count += 1

st.sidebar.title(f'Number of transactions : {st.session_state.count}')
# st.write(f"Button has been pressed {st.session_state.count} times")
button=st.sidebar.button("Mock Transaction")
if button:
    increment_count()
response=None
# Display the updated count
if enable_controls:
    st.sidebar.write("You are now eligible for discover mode ")
    user_input = st.sidebar.text_input("Enter your request or choice of category", key='user_input')
    response=retriever.get_relevant_documents(query=st.session_state.user_input)

st.write(f'{response}')
if not enable_controls:
    st.sidebar.write(f"Keep going {20-st.session_state.count} times.")