Update app.py

app.py

@@ -1,16 +1,22 @@
 import gradio as gr
 from langchain.vectorstores import FAISS
 from langchain.embeddings import HuggingFaceEmbeddings
-# import torch
 
 
 def get_matches(query, db_name="miread_contrastive"):
+    """
+    Wrapper to call the similarity search on the required index
+    """
     matches = vecdbs[index_names.index(
         db_name)].similarity_search_with_score(query, k=60)
     return matches
 
 
 def inference(query, model="miread_contrastive"):
+    """
+    This function processes information retrieved by the get_matches() function
+    Returns - Gradio update commands for the authors, abstracts and journals tablular output
+    """
     matches = get_matches(query, model)
     auth_counts = {}
     j_bucket = {}
@@ -28,12 +34,12 @@ def inference(query, model="miread_contrastive"):
         date = doc.metadata.get('date', 'None')
         link = doc.metadata.get('link', 'None')
         submitter = doc.metadata.get('submitter', 'None')
-        # journal = doc.metadata.get('journal', 'None').strip()
         journal = doc.metadata['journal']
         if (journal is None or journal.strip() == ''):
             journal = 'None'
         else:
             journal = journal.strip()
+
         # For journals
         if journal not in j_bucket:
             j_bucket[journal] = score
@@ -87,7 +93,9 @@ model_names = [
 model_kwargs = {'device': 'cpu'}
 encode_kwargs = {'normalize_embeddings': False}
 faiss_embedders = [HuggingFaceEmbeddings(
-    name, model_kwargs, encode_kwargs) for name in model_names]
+    model_name=name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs) for name in model_names]
 
 vecdbs = [FAISS.load_local(index_name, faiss_embedder)
           for index_name, faiss_embedder in zip(index_names, faiss_embedders)]