Update app.py

app.py

@@ -73,7 +73,7 @@ def load_db():
 
 
 # Initialize langchain LLM chain
-def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+def initialize_llmchain(temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
     progress(0.1, desc="Initializing HF tokenizer...")
     
     # HuggingFaceHub uses HF inference endpoints
@@ -140,11 +140,11 @@ def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Pr
     return vector_db, collection_name, "Complete!"
 
 
-def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+def initialize_LLM(llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
     # print("llm_option",llm_option)
     llm_name = llm_model
     print("llm_name: ",llm_name)
-    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
+    qa_chain = initialize_llmchain(llm_temperature, max_tokens, top_k, vector_db, progress)
     return qa_chain, "Complete!"
 
 
@@ -266,7 +266,7 @@ def demo():
             inputs=[document, slider_chunk_size, slider_chunk_overlap], \
             outputs=[vector_db, collection_name, db_progress])
         qachain_btn.click(initialize_LLM, \
-            inputs=[llm_model, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
+            inputs=[slider_temperature, slider_maxtokens, slider_topk, vector_db], \
             outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
             inputs=None, \
             outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \