format, fix memory consumption, load data dictionary from GitHub repo

src/b2aiprep/app/pages/5_Search.py

@@ -6,60 +6,105 @@
 from scipy.spatial import distance as ssd
 import numpy as np
 import streamlit as st
+import typing as t
 
-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)
+    """Mean pool the model output over the tokens factoring in attention."""
+    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'):
-
+def load_model(model_path="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):
+tokenizer, model = load_model()
 
-    # Tokenize sentences
-    encoded_input = tokenizer(text_list, padding=True, truncation=True, return_tensors='pt')
 
+def embed_sentences(text_list):
     # 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)
+    # Batch this for memory efficiency
+    batch_size = 10
+    sentence_embeddings = []
+    for i in range(0, len(text_list), batch_size):
+        batch = text_list[i:i+batch_size]
+        # Tokenize sentences
+        encoded_input = tokenizer(batch, padding=True, truncation=True, return_tensors="pt")
+        with torch.no_grad():
+            model_output = model(**encoded_input)
+        batch_embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
+        batch_embeddings = F.normalize(batch_embeddings, p=2, dim=1)
+        sentence_embeddings.append(batch_embeddings)
+
+    sentence_embeddings = torch.cat(sentence_embeddings, dim=0)
     return sentence_embeddings
 
 
 @st.cache_data
 def embed_corpus(c):
     return embed_sentences(c)
 
+@st.cache_data
+def download_and_load_data_dictionary() -> pd.DataFrame:
+    """Load the data dictionary from the public release GitHub repo.
+    
+    https://github.com/eipm/bridge2ai-redcap/
+
+    Returns:
+        pd.DataFrame: The data dictionary as a DataFrame.
+    """
+    data_dictionary_url = (
+        "https://raw.githubusercontent.com/eipm/bridge2ai-redcap/main/data/bridge2ai_voice_project_data_dictionary.csv"
+    )
+    return pd.read_csv(data_dictionary_url)
+
+rcdict = download_and_load_data_dictionary()
+
 
+def extract_descriptions(df: pd.DataFrame) -> t.Tuple[t.List[str], t.List[str]]:
+    """Extract the descriptions from the data dictionary."""
 
-corpus = rcdict['Field Label'].values.tolist()
-field_ids = rcdict['Variable / Field Name'].values.tolist()
+    # There are a number of fields in the data dictionary which do not have a label
+    # For example, page_1, page2, page3... of the eConsent
+    # We will only consider fields with a label, as the ones missing label
+    # are not useful for our search.
+    idx = df["Field Label"].notnull()
+    corpus = df.loc[idx, "Field Label"].values.tolist()
+    field_ids = df.loc[idx, "Variable / Field Name"].values.tolist()
+    return corpus, field_ids
 
+corpus, field_ids = extract_descriptions(rcdict)
+
+# TODO: This is a very memory hungry operation, spikes with ~40 GB of memory.
 corpus_as_vector = embed_corpus(corpus)
 
-search_string = st.text_input('Search string', 'age')
+st.markdown(
+    """
+    # Search the data dictionary
+
+    The following text box allows you to semantically search the data dictionary.
+    You can use it to find the name for fields collected in the study.
+    """
+)
+search_string = st.text_input("Search string", "age")
 
-search_embedding = embed_sentences([search_string,])
+search_embedding = embed_sentences(
+    [
+        search_string,
+    ]
+)
 
 # Compute cosine similarity scores for the search string to all other sentences
 sims = []
@@ -76,19 +121,16 @@ def embed_corpus(c):
 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)
+    final_df = pd.DataFrame(
+        {"field_ids": field_ids_to_show, "field_desc": sentences_to_show}
+    )
+    st.table(final_df)