Create app.py

app.py

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+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
+
+from IPython import get_ipython
+
+def exec_python(cell):
+    ipython = get_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
+
+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):
+  # 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, result)
+    second_call_result, cost2 = second_call(second_prompt_build_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()