Update app.py

app.py

@@ -47,14 +47,14 @@ def create_db(splits):
         st.error(f"Error creating vector database: {e}")
         return None
 
-def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
+def initialize_llmchain(llm_model, vector_db):
     try:
         llm = HuggingFaceEndpoint(
             repo_id=llm_model,
             huggingfacehub_api_token=api_token,
-            temperature=temperature,
-            max_new_tokens=max_tokens,
-            top_k=top_k,
+            temperature=0.5,
+            max_new_tokens=4096,
+            top_k=3,
         )
         memory = ConversationBufferMemory(
             memory_key="chat_history",
@@ -89,10 +89,10 @@ def initialize_database(uploaded_files):
         st.error(f"Error initializing database: {e}")
         return None, "Failed to initialize database."
 
-def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db):
+def initialize_LLM(llm_option, vector_db):
     try:
         llm_name = list_llm[llm_option]
-        qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db)
+        qa_chain = initialize_llmchain(llm_name, vector_db)
         if qa_chain is None:
             return None, "Failed to initialize QA chain."
         return qa_chain, "QA chain initialized. Chatbot is ready!"
@@ -148,13 +148,10 @@ def main():
 
     st.markdown("### Select Large Language Model (LLM) and input parameters")
     llm_option = st.radio("Available LLMs", list_llm_simple)
-    temperature = st.slider("Temperature", 0.01, 1.0, 0.5, 0.1)
-    max_tokens = st.slider("Max New Tokens", 128, 9192, 4096, 128)
-    top_k = st.slider("top-k", 1, 10, 3, 1)
 
     if st.button("Initialize Question Answering Chatbot"):
         with st.spinner("Initializing QA chatbot..."):
-            qa_chain, llm_message = initialize_LLM(list_llm_simple.index(llm_option), temperature, max_tokens, top_k, st.session_state['vector_db'])
+            qa_chain, llm_message = initialize_LLM(list_llm_simple.index(llm_option), st.session_state['vector_db'])
             st.session_state['qa_chain'] = qa_chain
             st.success(llm_message)