Update app.py

app.py

@@ -16,6 +16,7 @@ from sklearn.preprocessing import normalize
 from rank_bm25 import BM25Okapi
 from gensim.models import Word2Vec
 from typing import List, Optional, Tuple
+import tempfile
 
 # Cell 3: Set up logging and Mistral client
 logger = logging.getLogger(__name__)
@@ -505,12 +506,22 @@ def chatbot_interface(user_query, response_style, selected_retrieval_methods, se
     vector_db_path = "vector_db.pkl"
     annoy_index_path = "vector_index.ann"
     if pdf_file is not None:
-        pdf_path = pdf_file.name  # Get the path of the uploaded file
-        create_vector_db_and_annoy_index(pdf_path, 'vector_db.pkl', 'vector_index.ann')
+        with tempfile.NamedTemporaryFile(mode="wb", suffix=".pdf", delete=False) as tmp:
+            tmp.write(pdf_file)  # Write the uploaded file content to the temporary file
+            pdf_path = tmp.name  
+
+        # Create vector DB and Annoy index 
+        create_vector_db_and_annoy_index(pdf_path, vector_db_path, annoy_index_path)
         store_embeddings_in_vector_db(pdf_path, 'vector_db.pkl', 'vector_index.ann', chunk_size, overlap)
-        
     else:
         return "Please upload a PDF file." 
+    # if pdf_file is not None:
+    #     pdf_path = pdf_file.name  # Get the path of the uploaded file
+    #     create_vector_db_and_annoy_index(pdf_path, 'vector_db.pkl', 'vector_index.ann')
+    #     store_embeddings_in_vector_db(pdf_path, 'vector_db.pkl', 'vector_index.ann', chunk_size, overlap)
+        
+    # else:
+    #     return "Please upload a PDF file." 
 
     # Load the documents and create embeddings with the provided chunk_size and overlap
     #store_embeddings_in_vector_db('med.pdf', 'vector_db.pkl', 'vector_index.ann', chunk_size, overlap)