Be my Python tutor who is great at building python apps. Don't worry about the Python IDE and Python Interpreter as I am using PyCharm as my Python IDE and Python version 3.9 as the interpreter.
The app I want to build: I want to build an app that will take user input and then spit out a response and continue until the user input is 'exit'. Initially, the response will be a mock response, but later will be talking to openAI APIs. Go step by step and make the app use Python properly with reusable functions. Explain the code as you go step by step. Ask for my confirmation after every step.
Great! I'm happy to help you build this Python app step by step. Let's start with the first step: creating a function that takes user input and continues to do so until the user types 'exit'.
Step 1: Create a function to get user input and exit when the input is 'exit'.
def get_user_input():
while True:
user_input = input("Please enter your message: ")
if user_input.lower() == 'exit':
break
else:
process_input(user_input)
def process_input(user_input):
response = generate_mock_response(user_input)
print(response)
def generate_mock_response(user_input):
return f"Mock response for: {user_input}"In this step, we have created a get_user_input() function that takes input from the user in a loop. If the user types 'exit', the loop breaks, and the function stops taking input. If the user types anything else, it calls the process_input() function, which generates a mock response using the generate_mock_response() function and prints it.
Please confirm if this step is clear and if you're ready to proceed.
Yes, done.
Great! Now that you've understood the first step, let's move on to the second step. In this step, we'll make the app more modular by creating a main function that calls get_user_input() and add the name == 'main' guard to ensure the app only runs when it's the main module.
Step 2: Create a main function and add the name == 'main' guard.
def main():
print("Welcome to the Python app!")
get_user_input()
print("Exiting the app. Have a great day!")
if __name__ == '__main__':
main()The main() function serves as an entry point for our app. It welcomes the user, calls the get_user_input() function, and then prints a goodbye message when the user exits. The name == 'main' guard ensures that the code inside the if block only runs when this script is the main module, and not when it's imported as a module in another script.
Please confirm if this step is clear and if you're ready to proceed.
Yes
Great! Now that you've understood the second step, let's move on to the third step. In this step, we'll replace the mock response with an actual API call to OpenAI's GPT API. To do this, we'll need to install the openai package and set up the API key. Make sure you have an API key from OpenAI before proceeding.
Step 3: Replace the mock response with a call to OpenAI's GPT API.
First, install the openai package using pip:
pip install openaiNext, let's update the generate_mock_response() function to generate_response() that makes an API call. Don't forget to replace your_api_key with your actual API key.
import openai
openai.api_key = "your_api_key"
def generate_response(user_input):
response = openai.Completion.create(
engine="davinci-codex",
prompt=f"{user_input}\n",
max_tokens=50,
n=1,
stop=None,
temperature=0.5,
)
return response.choices[0].text.strip()In this step, we've replaced the generate_mock_response() function with a new generate_response() function that uses OpenAI's GPT API. The function takes the user's input and sends it as a prompt to the GPT API. The API returns a generated response based on the input, which is then returned by the function.
Please confirm if this step is clear and if you're ready to proceed.
I am reading the api key from config.ini
Great! Instead of hardcoding the API key, it's a good practice to read it from a configuration file. In this case, we'll use the configparser package to read the config.ini file.
Step 4: Read the API key from the config.ini file.
First, create a config.ini file in your project directory with the following content:
[openai]
api_key = your_api_keyReplace your_api_key with your actual API key.
Next, update the code to read the API key from the config.ini file:
import configparser
config = configparser.ConfigParser()
config.read('config.ini')
openai.api_key = config.get('openai', 'api_key')In this step, we've used the configparser package to read the config.ini file. We load the configuration file with config.read('config.ini') and then get the API key using config.get('openai', 'api_key'). Finally, we set the openai.api_key to the value we've read from the configuration file.
Please confirm if this step is clear and if you're ready to proceed.
Yes
Great! Now that you've understood how to read the API key from the config.ini file, let's summarize the complete code for your app:
import openai
import configparser
config = configparser.ConfigParser()
config.read('config.ini')
openai.api_key = config.get('openai', 'api_key')
def get_user_input():
while True:
user_input = input("Please enter your message: ")
if user_input.lower() == 'exit':
break
else:
process_input(user_input)
def process_input(user_input):
response = generate_response(user_input)
print(response)
def generate_response(user_input):
response = openai.Completion.create(
engine="davinci-codex",
prompt=f"{user_input}\n",
max_tokens=50,
n=1,
stop=None,
temperature=0.5,
)
return response.choices[0].text.strip()
def main():
print("Welcome to the Python app!")
get_user_input()
print("Exiting the app. Have a great day!")
if __name__ == '__main__':
main()This is the complete code for your app that takes user input, calls the OpenAI GPT API, and prints the generated response. It continues to do so until the user inputs 'exit'. The app reads the API key from the config.ini file instead of hardcoding it, making it more secure and modular.
Please confirm if everything is clear and if you have any questions.
Ok, is the API call async? Make it async and while the API is waiting, the response should say ".....waiting for magic....."
To make the API call async and show the ".....waiting for magic....." message while waiting, we'll need to use Python's asyncio and aiohttp libraries. First, install the aiohttp library using pip:
pip install aiohttpNow, let's update the code to make the API call async:
import asyncio
import aiohttp
import json
async def generate_response(user_input):
async with aiohttp.ClientSession() as session:
payload = {
"engine": "davinci-codex",
"prompt": f"{user_input}\n",
"max_tokens": 50,
"n": 1,
"stop": None,
"temperature": 0.5
}
headers = {
"Authorization": f"Bearer {openai.api_key}",
"Content-Type": "application/json"
}
async with session.post('https://api.openai.com/v1/engines/davinci-codex/completions', data=json.dumps(payload), headers=headers) as resp:
result = await resp.json()
return result['choices'][0]['text'].strip()
async def process_input(user_input):
print(".....waiting for magic.....")
response = await generate_response(user_input)
print(response)
def run_async(coro):
return asyncio.get_event_loop().run_until_complete(coro)
def get_user_input():
while True:
user_input = input("Please enter your message: ")
if user_input.lower() == 'exit':
break
else:
run_async(process_input(user_input))Here's what we changed in the code:
- Import asyncio, aiohttp, and json.
- Change the generate_response() function to an asynchronous function using async def. Instead of using the openai package, we now use aiohttp to make an async API call. The rest of the function remains the same.
- Change the process_input() function to an asynchronous function using async def. Add await before the generate_response() function call.
- Create a new run_async() function that takes an asynchronous function and runs it using asyncio.get_event_loop().run_until_complete(). This function is needed to call async functions from a synchronous context.
- Update the get_user_input() function to call process_input() using the run_async() function.
With these changes, the API call will be made asynchronously, and the ".....waiting for magic....." message will be displayed while waiting for the response.
Please confirm if this step is clear and if you have any questions.
what is coro here?
def run_async(coro):
return asyncio.get_event_loop().run_until_complete(coro)In the run_async() function, coro is short for "coroutine". In the context of Python's asyncio library, a coroutine is a special type of function that can be paused and resumed, allowing other tasks to run concurrently. Coroutines are defined using the async def keyword.
In the run_async() function, the coro parameter represents a coroutine object. The purpose of this function is to run the given coroutine to completion using the event loop. The asyncio.get_event_loop() function returns the event loop for the current context, and run_until_complete(coro) runs the given coroutine until it completes.
The run_async() function is used to call async functions (like process_input()) from a synchronous context (like the get_user_input() function). It allows us to bridge the gap between synchronous and asynchronous code in this specific case.
Explain the asyncio.get_event_loop().run_until_complete(coroutine_object) line
asyncio.get_event_loop().run_until_complete(coroutine_object) is a way to run a coroutine to completion using the asyncio event loop. Let's break down this line into its components to better understand how it works:
asyncio.get_event_loop(): This function returns the asyncio event loop associated with the current context. An event loop is the central component of the asyncio library, responsible for managing the execution of asynchronous tasks, like coroutines. It schedules, runs, and manages the tasks concurrently.
run_until_complete(coroutine_object): This method is part of the event loop object and takes a coroutine object as its argument. It runs the given coroutine until it is completed. This method is blocking, which means it will not return until the coroutine has finished executing or an error occurs.
The line asyncio.get_event_loop().run_until_complete(coroutine_object) is used to run a coroutine to completion from a synchronous context. In our specific case, it allows us to run the asynchronous process_input() function from the synchronous get_user_input() function. Note that this approach should be used with caution, as it can block the event loop and make the code less efficient.
In Python 3.7 and later, you can use asyncio.run(coroutine_object) as a more convenient way to run a coroutine from a synchronous context. However, since we're using the input() function, which is blocking, in this specific case, it's better to stick with the run_until_complete() approach to avoid potential issues.
Ok. All steps done so far. Am I ready to test?
Yes, you've completed all the necessary steps, and your app should be ready for testing. To recap, you've built an app that takes user input, calls the OpenAI GPT API asynchronously, and prints the generated response until the user inputs 'exit'. You've also set up a configuration file to store the API key.
Here's the complete code for reference:
Copy code
import asyncio
import aiohttp
import json
import configparser
import openai
config = configparser.ConfigParser()
config.read('config.ini')
openai.api_key = config.get('openai', 'api_key')
async def generate_response(user_input):
async with aiohttp.ClientSession() as session:
payload = {
"engine": "davinci-codex",
"prompt": f"{user_input}\n",
"max_tokens": 50,
"n": 1,
"stop": None,
"temperature": 0.5
}
headers = {
"Authorization": f"Bearer {openai.api_key}",
"Content-Type": "application/json"
}
async with session.post('https://api.openai.com/v1/engines/davinci-codex/completions', data=json.dumps(payload), headers=headers) as resp:
result = await resp.json()
return result['choices'][0]['text'].strip()
async def process_input(user_input):
print(".....waiting for magic.....")
response = await generate_response(user_input)
print(response)
def run_async(coro):
return asyncio.get_event_loop().run_until_complete(coro)
def get_user_input():
while True:
user_input = input("Please enter your message: ")
if user_input.lower() == 'exit':
break
else:
run_async(process_input(user_input))
def main():
print("Welcome to the Python app!")
get_user_input()
print("Exiting the app. Have a great day!")
if __name__ == '__main__':
main()You can now run the app and test it by entering different messages as input. The app should display the ".....waiting for magic....." message while waiting for a response from the GPT API and print the generated response once it's received.
If you encounter any issues or have any questions, please don't hesitate to ask. Good luck with testing!
i am getting runtime error KeyError: 'choices' in this line: return result['choices'][0]['text'].strip()
I apologize for the confusion. It seems like the response from the API is not in the expected format. To better understand the issue, let's modify the generate_response() function to print the response received from the API:
async def generate_response(user_input):
# ... (the rest of the function remains unchanged)
async with session.post('https://api.openai.com/v1/engines/davinci-codex/completions', data=json.dumps(payload), headers=headers) as resp:
result = await resp.json()
print("API response:", result) # Add this line to print the response
return result['choices'][0]['text'].strip()Run the app again and check the output when you receive the KeyError. The printed API response should give us some insight into what's causing the issue.
Please let me know what the output is, and I'll help you resolve the problem.
well, so, i got an error as we are using wrong model name.
API response: {'error': {'message': 'The model: davinci-codex does not exist', 'type': 'invalid_request_error', 'param': None, 'code': 'model_not_found'}}
I think i can correct that but can we handle the error and exit gracefully?
I apologize for the mistake in the model name. You can replace "davinci-codex" with the correct model name, such as "text-davinci-002" or another available model.
To handle errors and exit gracefully, we can modify the generate_response() function to catch exceptions and return an error message instead. Here's how you can update the function:
async def generate_response(user_input):
async with aiohttp.ClientSession() as session:
payload = {
"engine": "text-davinci-002", # Replace with the correct model name
"prompt": f"{user_input}\n",
"max_tokens": 50,
"n": 1,
"stop": None,
"temperature": 0.5
}
headers = {
"Authorization": f"Bearer {openai.api_key}",
"Content-Type": "application/json"
}
try:
async with session.post('https://api.openai.com/v1/engines/text-davinci-002/completions', data=json.dumps(payload), headers=headers) as resp:
result = await resp.json()
if 'choices' in result:
return result['choices'][0]['text'].strip()
else:
print("Error:", result.get('error', 'Unknown error'))
return "Sorry, an error occurred while generating a response."
except Exception as e:
print("Exception:", e)
return "Sorry, an exception occurred while generating a response."In this version of the generate_response() function, we have added a try-except block to catch exceptions that might occur during the API call. If the API response contains an error, we print the error message and return a generic error message to the user. If an exception occurs, we print the exception and return a generic exception message to the user.
With these changes, the app should handle errors more gracefully and provide feedback when something goes wrong.
Please update the model name, make the suggested changes, and test the app again. Let me know if you encounter any further issues.
doesn't the api return a specific http response code for errors?
Yes, you are correct. The API does return specific HTTP response codes for errors. We can use these response codes to handle errors more effectively in our generate_response() function.
Here's an updated version of the generate_response() function that checks the HTTP response status code and handles errors accordingly:
async def generate_response(user_input):
async with aiohttp.ClientSession() as session:
payload = {
"engine": "text-davinci-002", # Replace with the correct model name
"prompt": f"{user_input}\n",
"max_tokens": 50,
"n": 1,
"stop": None,
"temperature": 0.5
}
headers = {
"Authorization": f"Bearer {openai.api_key}",
"Content-Type": "application/json"
}
async with session.post('https://api.openai.com/v1/engines/text-davinci-002/completions', data=json.dumps(payload), headers=headers) as resp:
result = await resp.json()
if resp.status == 200:
return result['choices'][0]['text'].strip()
else:
print(f"Error (HTTP {resp.status}):", result.get('error', 'Unknown error'))
return f"Sorry, an error occurred while generating a response (HTTP {resp.status})."In this updated function, we check the HTTP response status code using resp.status. If the status code is 200, which indicates a successful request, we proceed with extracting the response text. For other status codes, we print the error message along with the HTTP status code and return a generic error message to the user that includes the status code.
With these changes, the app should handle API errors more effectively and provide more informative feedback when something goes wrong.
Please update the generate_response() function and test the app again. Let me know if you encounter any further issues.
can you also exit the app after displaying error message/
To exit the app after displaying an error message, we can modify the process_input() and get_user_input() functions. We will update the process_input() function to return a boolean value that indicates if the app should exit, and the get_user_input() function will check this value to determine whether to continue or exit the loop.
Here's how you can update the process_input() and get_user_input() functions:
async def process_input(user_input):
print(".....waiting for magic.....")
response = await generate_response(user_input)
print(response)
if "Sorry, an error occurred while generating a response" in response:
return True # Return True to indicate that the app should exit
return False # Return False to continue the app
def get_user_input():
while True:
user_input = input("Please enter your message: ")
if user_input.lower() == 'exit':
break
else:
should_exit = run_async(process_input(user_input)) # Check the return value of process_input()
if should_exit: # If True, break the loop and exit the app
breakNow, when the process_input() function detects an error in the response, it will return True, and the get_user_input() function will break the loop and exit the app.
Please update the process_input() and get_user_input() functions with these changes and test the app again. Let me know if you encounter any issues or have any questions.
i dont like to work with strings, can we use some efficient ways
Certainly! Instead of using strings to check for errors, we can update the generate_response() function to raise a custom exception when it encounters an error. This approach will allow us to handle errors more efficiently and avoid using strings for error checking.
First, let's define a custom exception class:
class APIError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
self.message = message
super().__init__(self.message)Now, update the generate_response() function to raise an APIError when it encounters an error:
async def generate_response(user_input):
async with aiohttp.ClientSession() as session:
# ... (rest of the function remains unchanged)
async with session.post('https://api.openai.com/v1/engines/text-davinci-002/completions', data=json.dumps(payload), headers=headers) as resp:
result = await resp.json()
if resp.status == 200:
return result['choices'][0]['text'].strip()
else:
raise APIError(resp.status, result.get('error', 'Unknown error'))Next, update the process_input() function to catch the APIError exception and return the status_code instead of a boolean value:
async def process_input(user_input):
print(".....waiting for magic.....")
try:
response = await generate_response(user_input)
print(response)
return None # No error occurred, return None
except APIError as e:
print(f"Error (HTTP {e.status_code}): {e.message}")
return e.status_code # Return the error status codeFinally, update the get_user_input() function to check for the returned status code and exit the app accordingly:
def get_user_input():
while True:
user_input = input("Please enter your message: ")
if user_input.lower() == 'exit':
break
else:
status_code = run_async(process_input(user_input))
if status_code is not None: # If a status code is returned, exit the app
breakWith these changes, the app uses exceptions and status codes to handle errors more efficiently instead of relying on string matching.
Please update your code with these changes and test the app again. Let me know if you have any questions or encounter any issues.
explain the APIError method
The APIError class is a custom exception class we've defined to handle errors encountered while making API calls. It inherits from the base Exception class, which allows it to be used like any other exception in Python.
Here's the APIError class definition:
class APIError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
self.message = message
super().__init__(self.message)Let's break down the APIError class:
- class APIError(Exception):: This line defines the APIError class, which inherits from the built-in Exception class in Python.
- def init(self, status_code, message):: The init method is the constructor for the APIError class. It is called when you create a new instance of the class. It takes two arguments: status_code and message. The status_code represents the HTTP status code returned by the API, and message represents the error message provided by the API.
- self.status_code = status_code: This line assigns the status_code argument to the status_code attribute of the APIError instance. This allows you to access the status code later using the status_code attribute of the APIError instance.
- self.message = message: This line assigns the message argument to the message attribute of the APIError instance. This allows you to access the error message later using the message attribute of the APIError instance.
- super().init(self.message): This line calls the constructor of the parent Exception class with the message attribute. This sets the message as the default error message for the exception, which can be accessed using the str() function when handling the exception.
By defining the custom APIError class, we can raise it in the generate_response() function when we encounter an error. Then, in the process_input() function, we can catch the APIError exception, access its status_code and message attributes, and handle the error accordingly. This approach provides a more efficient and structured way to handle API errors compared to using string matching.
explain this line: result.get('error', 'Unknown error')
In the line result.get('error', 'Unknown error'), we are using the get() method of a Python dictionary to access the value associated with the key 'error'.
The get() method takes two arguments:
- The key you want to access in the dictionary
- A default value that the method returns if the key is not found in the dictionary
In this case, result is the dictionary containing the JSON response from the API call. We want to access the value associated with the key 'error' in the dictionary. If the 'error' key is not present in the result dictionary, the get() method will return the default value 'Unknown error'.
This approach is useful because it allows us to safely access the 'error' key without raising a KeyError exception if the key is not present in the dictionary. Instead, we get a default value that we can use to provide a meaningful error message.
right now, the ".....waiting for magic....." remains there even after receiving response. I want to remove that once i receive the response and only output response (or error)
To achieve this, we can use the end='\r' parameter in the print() function to overwrite the ".....waiting for magic....." message once we receive the response.
Here's how you can update the process_input() function:
async def process_input(user_input):
print(".....waiting for magic.....", end="\r")
try:
response = await generate_response(user_input)
print(" " * len(".....waiting for magic....."), end="\r") # Clear the waiting message
print(response)
return None
except APIError as e:
print(" " * len(".....waiting for magic....."), end="\r") # Clear the waiting message
print(f"Error (HTTP {e.status_code}): {e.message}")
return e.status_codeThe end="\r" parameter in the print() function replaces the default newline character (\n) with a carriage return character (\r). This causes the cursor to return to the beginning of the line without moving to a new line. By doing this, we can overwrite the ".....waiting for magic....." message with the response or the error message.
Additionally, we add a line to print spaces with the same length as the waiting message before printing the response or error message. This ensures that the waiting message is fully overwritten.
Please update the process_input() function with these changes and test the app again. Let me know if you have any questions or encounter any issues.
explain this line: print(" " * len(".....waiting for magic....."), end="\r")
This line of code is used to clear the ".....waiting for magic....." message by overwriting it with spaces.
Here's a breakdown of the line:
- " " * len(".....waiting for magic....."): This part creates a string consisting of spaces with a length equal to the length of the ".....waiting for magic....." message. The len() function calculates the length of the message, and the " " * length expression creates a new string with that many spaces.
- print(" " * len(".....waiting for magic....."), end="\r"): This part prints the string of spaces created in step 1. The end="\r" parameter in the print() function replaces the default newline character (\n) with a carriage return character (\r). This causes the cursor to return to the beginning of the line without moving to a new line. By doing this, we can overwrite the ".....waiting for magic....." message with spaces, effectively clearing it from the console.
This line of code is used in the process_input() function right before printing the response or the error message. By clearing the waiting message, we ensure that only the response or error message is displayed on the console.
Please let me know if you have any further questions or need any clarification.
how does it know it needs to clear after receving a response?
The code knows it needs to clear the waiting message after receiving a response because of the way the process_input() function is structured.
Here's a simplified version of the process_input() function for clarity:
async def process_input(user_input):
print(".....waiting for magic.....", end="\r")
try:
response = await generate_response(user_input) # Make the API call
print(" " * len(".....waiting for magic....."), end="\r") # Clear the waiting message
print(response) # Print the response
except APIError as e:
print(" " * len(".....waiting for magic....."), end="\r") # Clear the waiting message
print(f"Error (HTTP {e.status_code}): {e.message}") # Print the error messageWhen the process_input() function is called, it first prints the waiting message using print(".....waiting for magic.....", end="\r"). Then, it makes the API call using response = await generate_response(user_input).
The await keyword is used here because the generate_response() function is asynchronous, meaning it can run concurrently with other tasks. When the API call is made, the code execution in the process_input() function is paused, allowing other tasks (such as waiting for user input) to continue.
Once the API call is completed and the response is received, the code execution in the process_input() function resumes. At this point, it's time to clear the waiting message, so the code executes the line print(" " * len(".....waiting for magic....."), end="\r"). This overwrites the waiting message with spaces, effectively clearing it from the console. Then, the code either prints the response or the error message, depending on whether the API call was successful or not.
In summary, the code knows when to clear the waiting message because it is placed immediately after the await generate_response(user_input) line, which ensures that it is executed once the response has been received.
Please let me know if you have any further questions or need any clarification.
is this the effective way of achieving this outcome?
Yes, the approach we've used here to clear the waiting message and display the response or error message is effective and relatively simple. It makes use of the carriage return character (\r) to overwrite the waiting message with spaces and then print the response or error message on the same line.
This method works well for console-based applications, where the output is displayed line-by-line, and the user experience is enhanced by providing some feedback while waiting for the response.
However, if you plan to build a more sophisticated user interface, such as a web application or a desktop application with a graphical user interface (GUI), you might consider using a different approach. In such cases, you can use dedicated UI components (e.g., progress bars, loading indicators, or async updates) to provide feedback to the user and update the displayed information once the response is received.
For the given console-based application, the current method is an effective and straightforward way to achieve the desired outcome.
If you have any further questions or need more information, please feel free to ask.
ok. but the message 'waiting for magic' doesn't get printed at all now
Apologies for the confusion. The issue you are experiencing might be due to the buffering behavior of the standard output. Some systems buffer the output by default, meaning that they don't display it immediately. To resolve this issue, you can use the flush=True parameter in the print() function to force the output to be displayed immediately.
Here's how to update the process_input() function:
async def process_input(user_input):
print(".....waiting for magic.....", end="\r", flush=True)
try:
response = await generate_response(user_input)
print(" " * len(".....waiting for magic....."), end="\r", flush=True) # Clear the waiting message
print(response)
except APIError as e:
print(" " * len(".....waiting for magic....."), end="\r", flush=True) # Clear the waiting message
print(f"Error (HTTP {e.status_code}): {e.message}")
return e.status_codeAdding the flush=True parameter to the print() function ensures that the waiting message is displayed immediately, without waiting for the output buffer to be filled or for a newline character to be encountered.
Please update the process_input() function with these changes and test the app again. Let me know if you have any questions or encounter any issues.
nope, its not showing still
I apologize for the confusion. It seems like the issue might be due to the use of the carriage return character (\r) in the print() function, which is causing the waiting message to be overwritten too quickly.
To resolve this, let's use a different approach that makes use of the sys.stdout.write() function and the sys.stdout.flush() method. This method will give us more control over the console output.
Here's how to update the process_input() function:
import sys
import asyncio
async def process_input(user_input):
sys.stdout.write(".....waiting for magic.....")
sys.stdout.flush()
try:
response = await generate_response(user_input)
sys.stdout.write("\r" + " " * len(".....waiting for magic.....") + "\r")
sys.stdout.flush()
print(response)
except APIError as e:
sys.stdout.write("\r" + " " * len(".....waiting for magic.....") + "\r")
sys.stdout.flush()
print(f"Error (HTTP {e.status_code}): {e.message}")
return e.status_codeIn this updated version of the process_input() function, we use sys.stdout.write() to display the waiting message and sys.stdout.flush() to force the output to be displayed immediately.
When the response is received or an error occurs, we overwrite the waiting message with spaces and then print the response or error message using the print() function.
Please update the process_input() function with these changes and test the app again. Let me know if you have any questions or encounter any issues.
what is the difference between this and print earlier?
The main difference between using print() and using sys.stdout.write() with sys.stdout.flush() lies in the control and flexibility they offer over the console output.
print(): The print() function in Python is a high-level function that writes a string to the console followed by a newline character (\n) by default. It has some built-in formatting options, like the sep, end, and file parameters. However, it is limited in terms of the control it provides over the output buffering and the exact behavior of the console output.
sys.stdout.write() and sys.stdout.flush(): These lower-level functions provide more control over the console output. sys.stdout.write() writes a string to the console without appending a newline character, allowing you to write multiple strings on the same line. sys.stdout.flush() forces the output buffer to be flushed, ensuring that the output is displayed immediately without waiting for the buffer to be filled or for a newline character to be encountered.
In the updated process_input() function, we use sys.stdout.write() and sys.stdout.flush() to have more control over the output buffering and the console output behavior. This allows us to display the waiting message, overwrite it with spaces when the response is received, and then print the response or error message, providing a cleaner user experience.
Using sys.stdout.write() and sys.stdout.flush() in this case helps to address the issue where the waiting message was not being displayed properly with the print() function.
Please let me know if you have any further questions or need more clarification.
This works great! I think my app is complete now!
I'm glad to hear that the app is working as expected now! It's been a pleasure helping you build this app step by step. If you have any questions or need assistance with any other Python projects or concepts in the future, please don't hesitate to ask.
Good luck with your app, and happy coding!
can you add good comments to the full code as i am going to publish to GitHub
import openai
import configparser
import asyncio
import aiohttp
import json
import sys
config = configparser.ConfigParser()
config.read('config.ini')
openai.api_key = config.get('API_KEYS', 'OPENAI-API_KEY')
class APIError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
self.message = message
super().__init__(self.message)
def get_user_input():
while True:
user_input = input(f"\nYour message: ")
if user_input.lower() == 'exit':
break
else:
status_code = run_async(process_input(user_input))
if status_code is not None:
break
def run_async(coroutine_object):
return asyncio.get_event_loop().run_until_complete(coroutine_object)
async def process_input(user_input):
sys.stdout.write(".....waiting for magic.....")
sys.stdout.flush()
try:
response = await generate_response(user_input)
sys.stdout.write("\r" + " " * len(".....waiting for magic.....") + "\r")
sys.stdout.flush()
print(response, "\n")
return None
except APIError as e:
sys.stdout.write("\r" + " " * len(".....waiting for magic.....") + "\r")
sys.stdout.flush()
print(f"Error occurred: {e.message}")
return e.status_code
async def generate_response(user_input):
async with aiohttp.ClientSession() as session:
payload = {
"model": "text-davinci-003",
"prompt": f"{user_input}\n",
"max_tokens": 50,
"temperature": 0.5
}
headers = {
"Authorization": f"Bearer {openai.api_key}",
"Content-Type": "application/json"
}
async with session.post('https://api.openai.com/v1/completions',
data=json.dumps(payload),
headers=headers) as resp:
result = await resp.json()
if resp.status == 200:
return result['choices'][0]['text'].strip()
else:
raise APIError(resp.status, result.get('error', 'Unknown error'))
def main():
print(f"\nWelcome to the ChatGPT Python app!")
get_user_input()
print(f"\nExiting the app. Have a great day!\n")
if __name__ == '__main__':
main()Certainly! Here's the full code with comments added to provide context and explanations for each section:
Copy code
import openai
import configparser
import asyncio
import aiohttp
import json
import sys
# Load API key from config file
config = configparser.ConfigParser()
config.read('config.ini')
openai.api_key = config.get('API_KEYS', 'OPENAI-API_KEY')
# Define custom APIError exception
class APIError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
self.message = message
super().__init__(self.message)
# Get user input and process it
def get_user_input():
while True:
user_input = input(f"\nYour message: ")
if user_input.lower() == 'exit':
break
else:
status_code = run_async(process_input(user_input))
if status_code is not None:
break
# Run async function synchronously
def run_async(coroutine_object):
return asyncio.get_event_loop().run_until_complete(coroutine_object)
# Process user input and display response or error message
async def process_input(user_input):
sys.stdout.write(".....waiting for magic.....")
sys.stdout.flush()
try:
response = await generate_response(user_input)
sys.stdout.write("\r" + " " * len(".....waiting for magic.....") + "\r")
sys.stdout.flush()
print(response, "\n")
return None
except APIError as e:
sys.stdout.write("\r" + " " * len(".....waiting for magic.....") + "\r")
sys.stdout.flush()
print(f"Error occurred: {e.message}")
return e.status_code
# Call OpenAI API to generate a response
async def generate_response(user_input):
async with aiohttp.ClientSession() as session:
payload = {
"model": "text-davinci-003",
"prompt": f"{user_input}\n",
"max_tokens": 50,
"temperature": 0.5
}
headers = {
"Authorization": f"Bearer {openai.api_key}",
"Content-Type": "application/json"
}
async with session.post('https://api.openai.com/v1/completions',
data=json.dumps(payload),
headers=headers) as resp:
result = await resp.json()
if resp.status == 200:
return result['choices'][0]['text'].strip()
else:
raise APIError(resp.status, result.get('error', 'Unknown error'))
# Main function to start the app
def main():
print(f"\nWelcome to the ChatGPT Python app!")
get_user_input()
print(f"\nExiting the app. Have a great day!\n")
# Entry point of the script
if __name__ == '__main__':
main()The added comments explain the purpose of each function, class, and major code block, making it easier for other developers to understand and maintain the code. This should be helpful when publishing your code on GitHub.
If you have any further questions or need more information, please feel free to ask.