Spaces:
Sleeping
Sleeping
File size: 2,378 Bytes
e2b9039 3ecbeff e2b9039 b21411e 9610c39 e2b9039 d792c52 b21411e 9610c39 b21411e e2b9039 3ecbeff 9610c39 3ecbeff 9610c39 b21411e 3ecbeff e2b9039 b21411e e2b9039 b21411e fff69e7 e2b9039 b21411e 9610c39 3ecbeff b0a56a3 9610c39 b21411e b0a56a3 b21411e b0a56a3 b21411e 9610c39 a19ad68 b21411e 9610c39 b21411e a19ad68 9610c39 b21411e |
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 |
import os
import streamlit as st
from pinecone import Pinecone
from sentence_transformers import SentenceTransformer
# Title of the Streamlit App
st.title("Medical Hybrid Search")
# Initialize Pinecone globally
index = None
# Function to initialize Pinecone
def initialize_pinecone():
api_key = os.getenv('PINECONE_API_KEY') # Get Pinecone API key from environment variable
if api_key:
# Initialize Pinecone client using the new class instance method
pc = Pinecone(api_key=api_key)
return pc
else:
st.error("Pinecone API key not found! Please set the PINECONE_API_KEY environment variable.")
return None
# Function to connect to the 'pubmed-splade' index
def connect_to_index(pc):
index_name = 'pubmed-splade' # Hardcoded index name
# Connect to the 'pubmed-splade' index
if index_name in pc.list_indexes().names():
index = pc.Index(index_name)
return index
else:
st.error(f"Index '{index_name}' not found!")
return None
# Function to encode query using sentence transformers model
def encode_query(model, query_text):
return model.encode(query_text).tolist()
# Initialize Pinecone
pc = initialize_pinecone()
# If Pinecone initialized successfully, proceed with index management
if pc:
# Connect directly to 'pubmed-splade' index
index = connect_to_index(pc)
# Model for query encoding
model = SentenceTransformer('msmarco-bert-base-dot-v5')
# Query input
query_text = st.text_input("Enter a Query to Search", "Can clinicians use the PHQ-9 to assess depression?")
# Button to encode query and search the Pinecone index
if st.button("Search Query"):
if query_text and index:
dense_vector = encode_query(model, query_text)
# Search the index
results = index.query(
vector=dense_vector,
top_k=3,
include_metadata=True
)
st.write("### Search Results:")
for match in results.matches:
st.markdown(f"#### Score: **{match.score:.4f}**")
st.write(f"**Context:** {match.metadata.get('context', 'No context available.')}")
st.write("---")
else:
st.error("Please enter a query and ensure the index is initialized.")
|