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import os
import streamlit as st
import pickle
import time
from langchain.chains import RetrievalQA
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredURLLoader
#from langchain.vectorstores import FAISS
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEndpoint
from sentence_transformers import SentenceTransformer
from langchain.embeddings import HuggingFaceEmbeddings
#from langchain import HuggingFaceHub
from langchain_community.llms import HuggingFaceHub
from dotenv import load_dotenv
with st.sidebar:
st.image("sriram.jpg", caption="Say cheese :)", use_container_width=True)
load_dotenv()
st.title("Sriram’s Q Reflections 🔎")
#st.sidebar.title("Article URLs")
# urls=[]
# for i in range(3):
# url=st.sidebar.text_input(f"URL {i+1}")
# urls.append(url)
# process_url_clicked=st.sidebar.button("Process URLs")
file_path="faiss_index.pkl"
chunk_path="chunks.pkl"
placeholder=st.empty()
temp=st.empty()
query=placeholder.text_input("Search for a Memory :")
submit=st.button("Recall it")
if query:
temp.text("Searching for memories..!")
if os.path.exists(file_path):
with open(file_path,'rb') as f:
index=pickle.load(f)
with open(chunk_path,'rb') as f:
chunks=pickle.load(f)
model = SentenceTransformer("thenlper/gte-large")#'sentence-transformers/paraphrase-MiniLM-L12-v2')
temp.text("Searching for memories..!")
query_embedding = model.encode(query).astype('float32').reshape(1, -1) # Encode the query
k = 6 # Number of nearest neighbors to retrieve
distances, indices = index.search(query_embedding, k)
retrieved_chunks = [chunks[i] for i in indices[0]]
# # Use a prompt to generate a response with your language model
# input_prompt = f"""Given the question and
# context, Understand the question and give answer based on the context passed.
# Question: {query}\nContext: {context}\n Answer: """
# response = llm.invoke(input_prompt) # Replace with your LLM call
# text=response
if query or submit:
st.header("Q Memories :")
temp.text("Memories retrieved..!")
cleaned_text_list = []
for item in retrieved_chunks:
item = item.strip()
item = item.replace(" .", ".").replace("\n", " ")
item = item.lstrip(". ").strip()
item+="."
cleaned_text_list.append(item)
temp.empty()
for item in cleaned_text_list:
st.write(item)
# start_index = text.find("\nHelpful Answer:")
# # Extract everything after "\nHelpful Answer:" if it exists
# if start_index != -1:
# parsed_text =text[start_index + len("\nHelpful Answer:"):]
# parsed_text = parsed_text.strip() # Optionally strip any extra whitespace
# if query or submit:
# st.header("Answer :")
# st.write(parsed_text)
st.markdown("""
<hr style="margin-top: 2em;">
<p style="text-align: center; color: gray; font-size: small;">
Developed by Aditya Hariharan
</p>
""", unsafe_allow_html=True) |