import os import openai import sys import gradio as gr from IPython import get_ipython import json import requests from tenacity import retry, wait_random_exponential, stop_after_attempt from IPython import get_ipython # from termcolor import colored # doesn't actually work in Colab ¯\_(ツ)_/¯ GPT_MODEL = "gpt-3.5-turbo-1106" openai.api_key = os.environ['OPENAI_API_KEY'] messages=[] def exec_python(cell): ipython = get_ipython() print(ipython) result = ipython.run_cell(cell) log = str(result.result) if result.error_before_exec is not None: log += f"\n{result.error_before_exec}" if result.error_in_exec is not None: log += f"\n{result.error_in_exec}" prompt = """You are a genius math tutor, Python code expert, and a helpful assistant. answer = {ans} Please answer user questions very well with explanations and match it with the multiple choices question. """.format(ans = log) return log # Now let's define the function specification: functions = [ { "name": "exec_python", "description": "run cell in ipython and return the execution result.", "parameters": { "type": "object", "properties": { "cell": { "type": "string", "description": "Valid Python cell to execute.", } }, "required": ["cell"], }, }, ] # In order to run these functions automatically, we should maintain a dictionary: functions_dict = { "exec_python": exec_python, } def openai_api_calculate_cost(usage,model=GPT_MODEL): pricing = { # 'gpt-3.5-turbo-4k': { # 'prompt': 0.0015, # 'completion': 0.002, # }, # 'gpt-3.5-turbo-16k': { # 'prompt': 0.003, # 'completion': 0.004, # }, 'gpt-3.5-turbo-1106': { 'prompt': 0.001, 'completion': 0.002, }, # 'gpt-4-1106-preview': { # 'prompt': 0.01, # 'completion': 0.03, # }, # 'gpt-4-32k': { # 'prompt': 0.06, # 'completion': 0.12, # }, # 'text-embedding-ada-002-v2': { # 'prompt': 0.0001, # 'completion': 0.0001, # } } try: model_pricing = pricing[model] except KeyError: raise ValueError("Invalid model specified") prompt_cost = usage['prompt_tokens'] * model_pricing['prompt'] / 1000 completion_cost = usage['completion_tokens'] * model_pricing['completion'] / 1000 total_cost = prompt_cost + completion_cost print(f"\nTokens used: {usage['prompt_tokens']:,} prompt + {usage['completion_tokens']:,} completion = {usage['total_tokens']:,} tokens") print(f"Total cost for {model}: ${total_cost:.4f}\n") return total_cost @retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(3)) def chat_completion_request(messages, functions=None, function_call=None, model=GPT_MODEL): """ This function sends a POST request to the OpenAI API to generate a chat completion. Parameters: - messages (list): A list of message objects. Each object should have a 'role' (either 'system', 'user', or 'assistant') and 'content' (the content of the message). - functions (list, optional): A list of function objects that describe the functions that the model can call. - function_call (str or dict, optional): If it's a string, it can be either 'auto' (the model decides whether to call a function) or 'none' (the model will not call a function). If it's a dict, it should describe the function to call. - model (str): The ID of the model to use. Returns: - response (requests.Response): The response from the OpenAI API. If the request was successful, the response's JSON will contain the chat completion. """ # Set up the headers for the API request headers = { "Content-Type": "application/json", "Authorization": "Bearer " + openai.api_key, } # Set up the data for the API request json_data = {"model": model, "messages": messages} # If functions were provided, add them to the data if functions is not None: json_data.update({"functions": functions}) # If a function call was specified, add it to the data if function_call is not None: json_data.update({"function_call": function_call}) # Send the API request try: response = requests.post( "https://api.openai.com/v1/chat/completions", headers=headers, json=json_data, ) return response except Exception as e: print("Unable to generate ChatCompletion response") print(f"Exception: {e}") return e def first_call(init_prompt, user_input): # Set up a conversation messages = [] messages.append({"role": "system", "content": init_prompt}) # Write a user message that perhaps our function can handle...? messages.append({"role": "user", "content": user_input}) # Generate a response chat_response = chat_completion_request( messages, functions=functions ) # Save the JSON to a variable assistant_message = chat_response.json()["choices"][0]["message"] # Append response to conversation messages.append(assistant_message) usage = chat_response.json()['usage'] cost1 = openai_api_calculate_cost(usage) # Let's see what we got back before continuing return assistant_message, cost1 def second_prompt_build(prompt, log): prompt_second = prompt.format(ans = log) return prompt_second def function_call_process(assistant_message): if assistant_message.get("function_call") != None: # Retrieve the name of the relevant function function_name = assistant_message["function_call"]["name"] # Retrieve the arguments to send the function # function_args = json.loads(assistant_message["function_call"]["arguments"], strict=False) arg_dict = {'cell': assistant_message["function_call"]["arguments"]} # print(function_args) # Look up the function and call it with the provided arguments result = functions_dict[function_name](**arg_dict) return result # print(result) def second_call(prompt, result, function_name = "exec_python"): # Add a new message to the conversation with the function result messages.append({ "role": "function", "name": function_name, "content": str(result), # Convert the result to a string }) # Call the model again to generate a user-facing message based on the function result chat_response = chat_completion_request( messages, functions=functions ) assistant_message = chat_response.json()["choices"][0]["message"] messages.append(assistant_message) usage = chat_response.json()['usage'] cost2 = openai_api_calculate_cost(usage) # Print the final conversation # pretty_print_conversation(messages) return assistant_message, cost2 def main_function(init_prompt, prompt, user_input): first_call_result, cost1 = first_call(init_prompt, user_input) function_call_process_result = function_call_process(first_call_result) second_prompt_build_result = second_prompt_build(prompt, function_call_process_result) second_call_result, cost2 = second_call(second_prompt_build_result, function_call_process_result) return first_call_result, function_call_process_result, second_call_result, cost1, cost2 def gradio_function(): init_prompt = gr.Textbox(label="init_prompt (for 1st call)") prompt = gr.Textbox(label="prompt (for 2nd call)") user_input = gr.Textbox(label="User Input") output_1st_call = gr.Textbox(label="output_1st_call") output_fc_call = gr.Textbox(label="output_fc_call") output_2nd_call = gr.Textbox(label="output_2nd_call") cost = gr.Textbox(label="Cost 1") cost2 = gr.Textbox(label="Cost 2") iface = gr.Interface( fn=main_function, inputs=[init_prompt, prompt, user_input], outputs=[output_1st_call, output_fc_call, output_2nd_call, cost, cost2], title="Test", description="Accuracy", ) iface.launch(share=True) if __name__ == "__main__": gradio_function()