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Runtime error
Kushwanth Chowday Kandala
StreamlitAPIException: _repr_html_() is not a valid Streamlit command.
a37e99b
unverified
import streamlit as st | |
import os | |
from streamlit_chat import message | |
import numpy as np | |
import pandas as pd | |
# 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.grpc import PineconeGRPC | |
PINECONE_API_KEY=os.getenv("PINECONE_API_KEY") | |
PINECONE_ENV=os.getenv("PINECONE_ENV") | |
PINECONE_ENVIRONMENT=os.getenv("PINECONE_ENVIRONMENT") | |
# pc = PineconeGRPC( api_key=os.environ.get("PINECONE_API_KEY") ) # Now do stuff if 'my_index' not in pc.list_indexes().names(): pc.create_index( name='my_index', dimension=1536, metric='euclidean', spec=ServerlessSpec( cloud='aws', region='us-west-2' ) ) | |
def connect_pinecone(): | |
pinecone = PineconeGRPC(api_key=PINECONE_API_KEY, environment=PINECONE_ENV) | |
st.code(pinecone) | |
st.divider() | |
st.text(pinecone.list_indexes().names()) | |
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().names(): | |
pinecone.create_index( | |
name=index_name, | |
description="Semantic search", | |
dimension=model.get_sentence_embedding_dimension(), | |
metric="cosine", | |
spec=ServerlessSpec( cloud='gcp', region='us-central1' ) | |
) | |
# now connect to index | |
index = PineconeGRPC(index_name) | |
st.text(f"Succesfully connected to the pinecone index") | |
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": pd.DataFrame(response), | |
}, # 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) | |