Update app.py

app.py

@@ -90,7 +90,7 @@ current_dir = os.getcwd()
 
 prompt_templates = {"All Needs Experts": "Respond as if you are combination of all needs assessment experts."}
 actor_description = {"All Needs Experts": "<div style='float: left;margin: 0px 5px 0px 5px;'><img src='https://na.weshareresearch.com/wp-content/uploads/2023/04/experts2.jpg' alt='needs expert image' style='width:70px;align:top;'></div>A combination of all needs assessment experts."}
-prompt_template_name = []
+
 
 
 def get_empty_state():
@@ -184,7 +184,7 @@ langchain_document_loader()
 
 text_splitter = RecursiveCharacterTextSplitter(
     separators = ["\n\n", "\n", " ", ""],    
-    chunk_size = 1500,
+    chunk_size = 1000,
     chunk_overlap= 50
 )
 
@@ -284,7 +284,7 @@ if create_vectorstores:
         documents = chunks,
         vectorstore_name="Vit_All_HF_Embeddings"
     )
-    print("vector_store_HF:",vector_store_HF._collection.count(),"chunks.", prompt_template_name)
+    print("vector_store_HF:",vector_store_HF._collection.count(),"chunks.")
     
     print("")
 
@@ -834,8 +834,8 @@ def submit_message(prompt, prompt_template, temperature, max_tokens, context_len
     
 
     completion = chain.invoke({"question":prompt})
-    print("completion")
-    print(completion)
+    #print("completion")
+    #print(completion)
     #chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff")
     #completion = chain.run(input_documents=docs, question=query)