Update app.py

app.py

@@ -99,7 +99,7 @@ def wrap_model(model, tokenizer):
 
 
 
-def fetch_context(db, model, query, logger, template, use_compressor=True):
+def fetch_context(db, model, query, template, use_compressor=True):
     """
     Perform similarity search and retrieve related context to query.
     I have stored large documents in db so I can apply compressor on the set of retrived documents to 
@@ -140,7 +140,7 @@ def format_context(docs):
 
 
 
-def llm_chain_with_context(model, model_name, query, context, template, logger):
+def llm_chain_with_context(model, model_name, query, context, template):
     """ 
     Run simple chain with formatted prompt including query and retrieved context and the underlying model to generate a response.
     """
@@ -158,11 +158,11 @@ def llm_chain_with_context(model, model_name, query, context, template, logger):
     return output
 
 
-def generate_response(query,  model, template, logger):
+def generate_response(query,  model, template):
     start_time = time.time()
     progress_text = "Loading model. Please wait."
     my_bar = st.progress(0, text=progress_text)
-    context = fetch_context(db, model, model_name, query, template, logger)
+    context = fetch_context(db, model, model_name, query, template)
     # fill those as appropriate
     my_bar.progress(0.1, "Loading Database.  Please wait.")
 
@@ -171,7 +171,7 @@ def generate_response(query,  model, template, logger):
     my_bar.progress(0.5, "Running RAG.  Please wait.")
 
     my_bar.progress(0.7, "Generating Answer.  Please wait.")
-    response = llm_chain_with_context(model, model_name, query, context, template, logger)
+    response = llm_chain_with_context(model, model_name, query, context, template)
 
     logger.info(f"Total Execution Time: {time.time() - start_time}") 
 
@@ -286,7 +286,7 @@ if __name__=="__main__":
     if user_question is not None and user_question!="":
         with st.chat_message("Human", avatar="🧔🏻"):
             st.write(user_question)
-        response = generate_response(user_question,  model, all_templates, logger)   
+        response = generate_response(user_question,  model, all_templates)   
         with st.chat_message("AI", avatar="🏛️"):
             st.write(response)