From f4d6999a691907423cee47e67b15a5e02e6e3f24 Mon Sep 17 00:00:00 2001
From: Steven Bedrick <bedricks@ohsu.edu>
Date: Mon, 20 May 2024 09:53:44 -0700
Subject: [PATCH] Adding small demo of using a trivial embedding model to
 'search' through the data dictionary.

---
 docs/semantic-search-streamlit.py | 94 +++++++++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 docs/semantic-search-streamlit.py

diff --git a/docs/semantic-search-streamlit.py b/docs/semantic-search-streamlit.py
new file mode 100644
index 0000000..c48370f
--- /dev/null
+++ b/docs/semantic-search-streamlit.py
@@ -0,0 +1,94 @@
+import matplotlib.pyplot as plt
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+import pandas as pd
+from scipy.spatial import distance as ssd
+import numpy as np
+import streamlit as st
+
+data_path = "bridge2ai-Voice/bridge2ai-voice-corpus-1//b2ai-voice-corpus-1-dictionary.csv"
+rcdict = pd.read_csv(data_path)
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+@st.cache_data
+def load_model(model_path='models/sentence-transformers/all-MiniLM-L6-v2'):
+
+    # Load model from HuggingFace Hub
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    model = AutoModel.from_pretrained(model_path)
+
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+def embed_sentences(text_list):
+
+    # Tokenize sentences
+    encoded_input = tokenizer(text_list, padding=True, truncation=True, return_tensors='pt')
+
+    # Compute token embeddings
+    with torch.no_grad():
+        model_output = model(**encoded_input)
+
+    # Perform pooling
+    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+    # Normalize embeddings
+    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+    return sentence_embeddings
+
+
+@st.cache_data
+def embed_corpus(c):
+    return embed_sentences(c)
+
+
+
+corpus = rcdict['Field Label'].values.tolist()
+field_ids = rcdict['Variable / Field Name'].values.tolist()
+
+corpus_as_vector = embed_corpus(corpus)
+
+search_string = st.text_input('Search string', 'age')
+
+search_embedding = embed_sentences([search_string,])
+
+# Compute cosine similarity scores for the search string to all other sentences
+sims = []
+for embedding in corpus_as_vector:
+    sims.append(1 - ssd.cosine(search_embedding[0], embedding))
+
+# Sort sentences by similarity score in descending order (the most similar ones are first)
+sorted_index = np.argsort(sims)[::-1]
+
+sentences_sorted = np.array(corpus)[sorted_index]
+field_ids_sorted = np.array(field_ids)[sorted_index]
+sims = np.array(sims)[sorted_index]
+
+col1, col2 = st.columns(2)
+
+with col1:
+
+    cutoff = st.number_input("Cutoff", 0.0, 1.0, 0.3)
+
+    plt.plot(sims)
+    plt.title("Cosine similarity")
+    st.pyplot(plt)
+
+
+with col2:
+
+    sentences_to_show = sentences_sorted[sims > cutoff].tolist()
+    field_ids_to_show = field_ids_sorted[sims > cutoff].tolist()
+
+    final_df = pd.DataFrame({'field_ids': field_ids_to_show, 'field_desc': sentences_to_show})
+
+    st.table(final_df)
\ No newline at end of file