Spaces:
Runtime error
Runtime error
File size: 3,420 Bytes
4997aeb da863bf 3ae066d 4997aeb 0fb4cf5 4997aeb 8eb3e51 4997aeb 2da6f20 4997aeb 2da6f20 4997aeb 2da6f20 4997aeb 2da6f20 4997aeb 8ce3d9b 4997aeb 8ce3d9b 4997aeb 9eb3e78 11bc07e 9eb3e78 dcca063 9eb3e78 79497a3 c9dd21c 9eb3e78 2376b2f 9eb3e78 c9dd21c 9eb3e78 c9dd21c 9eb3e78 c9dd21c 9eb3e78 dcca063 3ae066d 9eb3e78 8ce3d9b 9eb3e78 5d2299c 9eb3e78 8ce3d9b 9eb3e78 8ce3d9b a37e99b 8ce3d9b 9eb3e78 dcca063 9eb3e78 dcca063 9eb3e78 dcca063 9eb3e78 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
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)
|