# Below code combines the whole flow to generate output

user_input = input("Please enter your question here: (if you want to exit then write 'exit' or 'bye'.) ")

# We will continue QNA till user says `exit` or `bye`
while user_input.strip().lower() != "exit" and user_input.strip().lower() != "bye":
   # Prepare messages to send to OpenAI
   messages = [
       {
           "role": "system",
           "content": "You are a helpful customer support assistant. Use the supplied tools to assist the user.",
       },
       {
           "role": "user",
           "content": user_input,
       }
   ]

   # Call OpenAI with prompt instruction, User Input and function definition (tools)
   response = client.chat.completions.create(
       model=GPT_MODEL, messages=messages, tools=tools
   )

   # Check if model has detected the tool call
   if response.choices[0].finish_reason == "tool_calls":
       # Fetch tool name and arguments
       tool_name = response.choices[0].message.tool_calls[0].function.name
       fn_argument = response.choices[0].message.tool_calls[0].function.arguments
       print("Detected tool", tool_name)
       print("Extracted function arguments:", fn_argument)
      
       # call the function associated with the particular tool
       if tool_name == "stockPriceData":
           result = get_current_stock_price(fn_argument)
           print(f"Stock price of {json.loads(fn_argument)['ticker_symbol']}:", result)
       elif tool_name == "currencyExchangeRate":
           result = currency_exchange_rate(fn_argument)
           print(f"Currency exchange rate from {json.loads(fn_argument)['from_country_currency']} to {json.loads(fn_argument)['to_country_currency']} is {result}.")
  
   # Check for the normal replies
   elif response.choices[0].finish_reason == "stop":
       print("response:", response.choices[0].message.content)

   # Check if OpeAI is identifying our content as restricted one
   elif response.choices[0].finish_reason == "content_filter":
       print("Your request or response may include restricted content.")

   # Check if we are exceeding maximum context window
   elif response.choices[0].finish_reason == "length":
       print(f"Your input token exceed the maximum input window of the model `{GPT_MODEL}`.")

   # Continue next iteration
   user_input = input("Please enter your question here: ")