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import streamlit as st
from sentence_transformers import SentenceTransformer, util
from llama_index import VectorStoreIndex, SimpleDirectoryReader
import os
# Load BERT model and create vectorizer
model_name = "bert-base-nli-mean-tokens"
model = SentenceTransformer(model_name)
vectorizer = model.encode
# Create docs folder if it doesn't exist
docs_path = "docs"
os.makedirs(docs_path, exist_ok=True)
# Streamlit app layout
st.title("Semantic and Similarity Search with BERT")
# File upload functionality
uploaded_file = st.file_uploader("Upload a text file", type=["txt"])
if uploaded_file is not None:
with open(os.path.join(docs_path, uploaded_file.name), "wb") as f:
f.write(uploaded_file.getbuffer())
# Reload documents and index after file upload
documents = SimpleDirectoryReader(
input_dir=docs_path,
filename_as_id=True,
required_exts=[".txt"]
).load_data()
# Creating embedding
document_embeddings = [vectorizer(doc.text) for doc in documents]
# Creating Index
index = VectorStoreIndex(document_embeddings=document_embeddings)
# Sucess message
st.success("File uploaded & processed successfully!")
query = st.text_input("Enter your search query:")
if query:
# Semantic search (approximated)
most_similar_doc = index.similarity_search(query)[0]
st.subheader("Semantic Search Results")
st.write(most_similar_doc.text)
# Similarity search
similar_documents = index.similarity_search(query, k=5) # Show top 5 results
st.subheader("Similarity Search Results")
for doc in similar_documents:
st.write(f"- {doc.id}: {doc.text[:50]}...")
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