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import streamlit as st
import os
from streamlit_chat import message
# st.config(PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION="python")
# from datasets import load_dataset
# dataset = load_dataset("wikipedia", "20220301.en", split="train[240000:250000]")
# wikidata = []
# for record in dataset:
# wikidata.append(record["text"])
# wikidata = list(set(wikidata))
# # print("\n".join(wikidata[:5]))
# # print(len(wikidata))
from sentence_transformers import SentenceTransformer
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if device != 'cuda':
st.text(f"you are using {device}. This is much slower than using "
"a CUDA-enabled GPU. If on colab you can chnage this by "
"clicking Runtime > change runtime type > GPU.")
model = SentenceTransformer("all-MiniLM-L6-v2", device=device)
st.divider()
# Creating a Index(Pinecone Vector Database)
import os
import pinecone
from pinecone.client import Index
from pinecone.grpc import GRPCIndex
PINECONE_API_KEY=os.getenv("PINECONE_API_KEY")
PINECONE_ENV=os.getenv("PINECONE_ENV")
PINECONE_ENVIRONMENT=os.getenv("PINECONE_ENVIRONMENT")
def connect_pinecone():
pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENV)
st.code(pinecone)
st.divider()
st.text(pinecone.list_indexes())
st.divider()
st.text(f"Succesfully connected to the pinecone")
return pinecone
def get_pinecone_semantic_index(pinecone):
index_name = "sematic-search"
# only create if it deosnot exists
if index_name not in pinecone.list_indexes():
pinecone.create_index(
name=index_name,
description="Semantic search",
dimension=model.get_sentence_embedding_dimension(),
metric="cosine",
)
# now connect to index
index = pinecone.GRPCIndex(index_name)
st.text(f"Succesfully connected to the pinecone")
return index
def chat_actions():
pinecone = connect_pinecone()
index = get_pinecone_semantic_index(pinecone)
st.session_state["chat_history"].append(
{"role": "user", "content": st.session_state["chat_input"]},
)
response = model.encode(st.session_state["chat_input"])
st.session_state["chat_history"].append(
{
"role": "assistant",
"content": response.text,
}, # This can be replaced with your chat response logic
)
if "chat_history" not in st.session_state:
st.session_state["chat_history"] = []
st.chat_input("Enter your message", on_submit=chat_actions, key="chat_input")
for i in st.session_state["chat_history"]:
with st.chat_message(name=i["role"]):
st.write(i["content"])
### Creating a Index(Pinecone Vector Database)
# %%writefile .env
PINECONE_API_KEY=os.getenv("PINECONE_API_KEY")
PINECONE_ENV=os.getenv("PINECONE_ENV")
PINECONE_ENVIRONMENT=os.getenv("PINECONE_ENVIRONMENT")
import os
import pinecone
from pinecone import Index, GRPCIndex
pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENV)
st.text(pinecone)
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