Update app.py

app.py

@@ -562,7 +562,7 @@ def instantiate_LLM(LLM_provider,api_key,temperature=0.5,top_p=0.95,model_name=N
         )
     return llm
 
-
+"""
 def get_environment_variable(key):
     if key in os.environ:
         value = os.environ.get(key)
@@ -571,7 +571,7 @@ def get_environment_variable(key):
         print(f"\n[ERROR]: {key} is not found in your environment variables.") 
         value = getpass(f"Insert your {key}")
     return value
-
+"""
 
 
 
@@ -625,45 +625,7 @@ memory.load_memory_variables({})
 
 
 
-standalone_question_template = """Given the following conversation and a follow up question, 
-rephrase the follow up question to be a standalone question, in its original language.\n\n
-Chat History:\n{chat_history}\n
-Follow Up Input: {question}\n
-Standalone question:"""
-
-standalone_question_prompt = PromptTemplate(
-    input_variables=['chat_history', 'question'], 
-    template=standalone_question_template
-)
-
 
-def answer_template(language="english"):
-    """Pass the standalone question along with the chat history and context 
-    to the `LLM` wihch will answer"""
-    
-    template = f"""Answer the question at the end, using only the following context (delimited by <context></context>).
-    Your answer must be in the language at the end. 
-    
-    <context>
-    {{chat_history}}
-    
-    {{context}} 
-    </context>
-    
-    Question: {{question}}
-    Language: {language}.
-    
-    """
-    return template
-
-answer_prompt = ChatPromptTemplate.from_template(answer_template())
-
-# invoke the ChatPromptTemplate
-answer_prompt.invoke(
-    {"question":"plaese give more details about DTC, including its use cases and implementation.",
-     "context":[Document(page_content="DTC use cases include...")], # the context is a list of retrieved documents.
-     "chat_history":memory.chat_memory}
-)