handle no images/text

app.py

@@ -99,6 +99,7 @@ def get_vectordb(text, images):
         client.delete_collection("text_db")
     if "image_db" in [i.name for i in client.list_collections()]:
         client.delete_collection("image_db")
+    
     text_collection = client.get_or_create_collection(
         name="text_db",
         embedding_function=sentence_transformer_ef,
@@ -114,22 +115,24 @@ def get_vectordb(text, images):
     image_descriptions = get_image_descriptions(images)
     image_dict = [{"image": image_to_bytes(img) for img in images}]
 
-    image_collection.add(
-        ids=[str(i) for i in range(len(images))],
-        documents=image_descriptions,
-        metadatas=image_dict,
-    )
+    if len(images)>0:
+        image_collection.add(
+            ids=[str(i) for i in range(len(images))],
+            documents=image_descriptions,
+            metadatas=image_dict,
+        )
 
     splitter = RecursiveCharacterTextSplitter(
         chunk_size=500,
         chunk_overlap=10,
     )
 
-    docs = splitter.create_documents([text])
-    doc_texts = [i.page_content for i in docs]
-    text_collection.add(
-        ids=[str(i) for i in list(range(len(doc_texts)))], documents=doc_texts
-    )
+    if len(text)>0:
+        docs = splitter.create_documents([text])
+        doc_texts = [i.page_content for i in docs]
+        text_collection.add(
+            ids=[str(i) for i in list(range(len(doc_texts)))], documents=doc_texts
+        )
     return client
 
 
@@ -214,7 +217,7 @@ def conversation(vectordb_client, msg, num_context, img_context, history):
     results = text_collection.query(
         query_texts=[msg], include=["documents"], n_results=num_context
     )["documents"][0]
-
+    
     similar_images = image_collection.query(
         query_texts=[msg],
         include=["metadatas", "distances", "documents"],
@@ -246,7 +249,7 @@ def conversation(vectordb_client, msg, num_context, img_context, history):
     context = "\n\n".join(results)
     # references = [gr.Textbox(i, visible=True, interactive=False) for i in results]
     response = llm(prompt.format(context=context, question=msg, images=img_desc))
-    return history + [(msg, response)], results, images_and_locs
+    yield history + [(msg, response)], results, images_and_locs
 
 
 def check_validity_and_llm(session_states):