Main Papers Talks Code Writing Writing How I Use "AI" by Nicholas Carlini 2024-08-01 Outline Introduction Nuance Background on me How I use AI - To make applications - As a tutor - To get started - To simplify code - For boring tasks - To automate tasks - As an API reference - As a search engine - To solve one-offs - To teach me - Solving solved problems - To fix errors Conclusion I don't think that "AI" models [a] I hate this word. It's not AI. But I want people who use this word, and also people who hate this word, to find this post. And so I guess I'm stuck with it for marketing, SEO, and clickbait. (by which I mean: large language models) are over-hyped. Yes, it's true that any new technology will attract the grifters. And it is definitely true that many companies like to say they're "Using AI" in the same way they previously said they were powered by "The Blockchain". (As we've seen again , and again , and again , and again .) It's also the case we may be in a bubble. The internet was a bubble that burst in 2000, but the Internet applications we now have are what was previously the stuff of literal science fiction. But the reason I think that the recent advances we've made aren't just hype is that, over the past year, I have spent at least a few hours every week interacting with various large language models, and have been consistently impressed by their ability to solve increasingly difficult tasks I give them. And as a result of this, I would say I'm at least 50% faster at writing code for both my research projects and my side projects as a result of these models. Most of the people online I find who talk about LLM utility are either wildly optimistic, and claim all jobs will be automated within three years, or wildly pessimistic, and say they have contributed nothing and never will. So in this post, I just want to try and ground the conversation. I'm not going to make any arguments about what the future holds. I just want to provide a list of 50 conversations that I (a programmer and research scientist studying machine learning) have had with different large language models to meaningfully improve my ability to perform research and help me work on random coding side projects. Among these: Building entire webapps with technology I've never used before. Teaching me how to use various frameworks having never previously used them. Converting dozens of programs to C or Rust to improve performance 10-100x. Trimming down large codebases to significantly simplify the project. Writing the initial experiment code for nearly every research paper I've written in the last year. Automating nearly every monotonous task or one-off script. Almost entirely replaced web searches for helping me set up and configure new packages or projects. About 50% replaced web searches for helping me debug error messages If I were to categorize these examples into two broad categories, they would be “helping me learn” and “automating boring tasks”. Helping me learn is obviously important because it means that I can now do things I previously would have found challenging; but automating boring tasks is (to me) actually equally important because it lets me focus on what I do best, and solve the hard problems. Most importantly, these examples are real ways I've used LLMs to help me. They're not designed to showcase some impressive capabiltiy; they come from my need to get actual work done. This means the examples aren't glamorous , but a large fraction of the work I do every day isn't, and the LLMs that are available to me today let me automate away almost all of that work. My hope in this post is literally to exhaust you with example after example of how I've concretely used LLMs to improve my productivity over the past year. Just know that, after you've had enough of the examples I've provided, I've only showed you less than 2% of the cases I've used LLMs to help me. So when you get exhausted---and you will---please feel free to just skip along with the new navigation menu that's at the left which I (read: a LLM) wrote new just for this post because it had gotten so long. Nuance If the internet does one thing poorly, it's nuance. I am not going to be claiming that today's LLMs are going to take over the world. I am not going to talk about what future models may or may not be able to do. I'm only going to discuss whether or not models, today, are helpful to me. You might think--why would someone write an entire article justifying that language models are useful??! Isn't that obvious?!? But there seem to be a (large?) contingent of people out there---in the academic literature, in the software engineering space, and also in the media sphere---who proclaim widely that LLMs contribute nothing, are just another hype cycle, and in a few years will die having had no impact on the world. I will be arguing these people are wrong because current LLMs are already useful. But I feel the need to caveat what I'm saying, because there is another (equally loud) contingent of people out there who claim the opposite: that today's models can replace all programmers, and people shouldn't learn programming because they'll all be out of jobs next year. I'm not going to be explicitly refuting these peoples' claims (that's not the point of this post), but I want to make it clear I'm not trying to argue on their behalf. I'm also not going to be trying to argue "the ends justify the means" and say that we should be training these models despite the harmful effects they have, of which there are many. I fully understand there will be negative (potentially very negative) consequences of these models. And by this I mean everything from disinformation to abuse to surveillance to job displacement. (Or, if you're to believe some, human extinction??) I will write an entire post about my thoughts on the harmful effects of LLMs at some point soon. The link will go here. But this is separate from the question of whether or not language models can be useful---which as I've said is what I want to talk about here. I further understand the limitations of why you might not want to use language models due to their propensity to hallucinate, to regurgitate facts, and to fail spectacularly due to their lack of robustness--- probably better than you understand these limitations . This post won't be about that. Because I think that models can be useful despite these failings. I further, further understand that the ethics of training these models is questionable at best. Maybe you don't like that they were trained on people's data without their permission ( I again probably understand this better than you ). Or maybe you're thinking about the people who are paid pennies on the dollar to explicitly train these models directly. I agree these are problems. But this post won't be about that either. As I've said many times now: all I'll be talking about is whether or not the models, as they exist now, are useful. Some background on me I'm not, as a general rule, someone who believes in things . For example: despite living through the crypto-hype in the security community a decade ago, I completely avoided ever writing a paper about blockchains. I've never owned a bitcoin. They have essentially no purpose---except for gambling and fraud. I am, day in and day out, a skeptic of all claims. Whenever someone tells me “[new technology] is going to change the world,” my general response is indifference. And so it should come as no surprise when I tell you I had basically the same reaction the first time someone told me that this AI thing was going to be incredibly useful and significantly alter the way I handle my day-to-day work: “I'll believe it when I see it.” Compounding on this, I'm also a security researcher. My day-to-day job for nearly the last decade now has been to show all of the ways in which AI models fail spectacularly when confronted with any kind of environment they were not trained to handle. I've shown that it's trivial to slightly perturb inputs to machine learning models to make them produce wildly incorrect outputs; or that most machine learning models memorize specific examples from their training datasets and repeat them when you use them. I fully understand the ways in which these systems are limited. And yet , here I am, saying that I think current large language models have provided the single largest improvement to my productivity since the internet was created. Honestly, today, if you gave me the choice of solving a randomly selected programming task from my work either with access to the internet or access to a state of the art language model, I'd probably pick the language model more than half the time. How I use language models So here's how I use LLMs to help me. [b] But please note: the help me is important here because how I work is almost certainly not how you work. That's okay! But I only have examples that suit my use cases, so that's what I'll give you. You may not like my use cases. You may think they're silly. It also may be the case that none of these relate to things that would help you. I accept this may be true. But I can only speak for myself. And each of these cases is something I've directly pulled from my chat history with some LLM over the past year. To build complete applications for me Last year I made a quiz for people to test how well they could predict the ability of GPT-4 to solve a handful of tasks. It ended up being pretty popular---it's gotten over ten million page views. And guess what? I had GPT-4 write almost the entire initial version of this application for me. I did this through a series of questions starting with me asking for the basic structure of the application and then slowly building out various features. In total this conversation is 30,000 words long and it really stress the capabilities of the (at the time state-of-the-art) original GPT-4 model. User I want to build a trivia-like game with Flask. Each page should have a question and an expected answer. The person then has a slider to predict the probability they believe GPT4 will answer the question with the expected answer. There should be a submit button on each page. When they click submit, it should show whether or not GPT-4 could actually answer the question (and give the answer the model gave as a reference). Then there should be a "Next Question" button. I want the following pages - GET /home: introduce the contest. Explain the rules. Have a single link to start with the first question. Create a session variable for this person. - GET /question?id=[uuid]: show question [uuid] as described above - POST /score: given POST parameters id=[uuid] session=[uuid] and guess=[float from 0 to 1] * First add this guess to the list of all guesses for that question * Then compute the person's log loss to return * Finally say how well this is compared to everyone else on that question. You will need global variables for - scores: a dictionary mapping question UUID to a list of predictions as floats. {question1: [.5, .1, .9]} - entries: a dictionary mapping user UUIDs to a dictionary of question UUIDs to floats. So for example {person1: {question1: .5, question2: .8}} - questions: a list of tuples of Q/A pairs: [("What is 1+1?", "2")...] - answers: a list of integers for whether or not the model gets it right [0, 1, 0 ...] Assistant You can start by installing Flask and other necessary libraries using pip if you haven't done so already: 1 pip install flask numpy scikit-learn Now, let's implement your game using Flask: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 from flask import Flask , request , session , redirect , url_for , render_template from sklearn.metrics import log_loss from uuid import uuid4 import numpy as np app = Flask ( __name__ ) app . config [ 'SECRET_KEY' ] = 'a very secret key' # replace with your secret key # Global variables scores = {} entries = {} questions = [( "What is 1+1?" , "2" ), ( "What is the capital of France?" , "Paris" )] answers = [ 1 , 0 ] # Suppose for these questions GPT-4 got the first one right and the second one wrong @app . route ( '/home' , methods = [ 'GET' ]) def home (): session [ 'id' ] = str ( uuid4 ()) return render_template ( 'home.html' ) # render a template that explains the game @app . route ( '/question' , methods = [ 'GET' ]) def question (): qid = request . args . get ( 'id' , default = 0 , type = int ) if 'id' in session : question , expected_answer = questions [ qid ] return render_template ( 'question.html' , question = question , expected_answer = expected_answer , id = qid ) else : return redirect ( url_for ( 'home' )) @app . route ( '/score' , methods = [ 'POST' ]) def score (): qid = request . form . get ( 'id' , type = int ) guess = request . form . get ( 'guess' , type = float ) user_id = session . get ( 'id' ) if user_id and qid is not None and guess is not None : # Add to the list of all guesses for that question scores . setdefault ( qid , []) . append ( guess ) # Add to the person's guesses entries . setdefault ( user_id , {})[ qid ] = guess # Compute the person's log loss user_log_loss = log_loss ([ answers [ qid ]], [ guess ], labels = [ 0 , 1 ]) # Compare with others other_scores = scores [ qid ] other_log_losses = log_loss ([ answers [ qid ]] * len ( other_scores ), other_scores , labels = [ 0 , 1 ]) comparison = 'better' if user_log_loss < other_log_losses else 'worse' return render_template ( 'score.html' , user_log_loss = user_log_loss , comparison = comparison ) else : return redirect ( url_for ( 'home' )) if __name__ == "__main__" : app . run ( debug = True ) This code is a starting point. It implements three endpoints: /home , /question , and /score , and it manages the global variables to record user predictions and compute log loss scores. However, to make this run, you'll also need to create the templates used for rendering, which are home.html , question.html , and score.html in this case. These templates should contain your HTML code with placeholders for the dynamic parts. Please ensure you read the Flask documentation on sessions and templates to get a better understanding if needed. Also, note that the usage of global variables to store information is okay for a small application with limited users and duration, or a quick prototype. For a production environment or an application with more users, you would probably want to use a database or other data storage system to store this information. User Can you give me the HTML files needed? Assistant Sure! Below are basic versions of the HTML files you might use. You'd need to modify them according to your exact needs, especially with the styling, but this can give you a starting point. For home.html : 1 2 3 4 5 6 7 8 9 10 11 12 13 <!DOCTYPE html> < html > < head > < title > Trivia Game </ title > </ head > < body > < h1 > Welcome to the Trivia Game! </ h1 > < p > This is a game where you guess the probability that GPT-4 will correctly answer various trivia questions. Your score is based on how close your guesses are to reality. </ p > < a href = "{{ url_for('question', id=0) }}" > Start Game </ a > </ body > </ html > For question.html : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 <!DOCTYPE html> < html > < head > < title > Trivia Game </ title > </ head > < body > < h1 > Question </ h1 > < p > {{ question }} </ p > < form method = "POST" action = "{{ url_for('score') }}" > < label for = "guess" > Your guess (probability that GPT-4 will give the answer "{{ expected_answer }}"): </ label > < input type = "range" id = "guess" name = "guess" min = "0" max = "1" step = "0.01" required > < input type = "hidden" id = "id" name = "id" value = "{{ id }}" > < input type = "submit" value = "Submit" > </ form > </ body > </ html > For score.html : 1 2 3 4 5 6 7 8 9 10 11 12 <!DOCTYPE html> < html > < head > < title > Trivia Game </ title > </ head > < body > < h1 > Your Score </ h1 > < p > Your log loss for this question is: {{ user_log_loss }} </ p > < p > You did {{ comparison }} than the average player on this question. </ p > < a href = "{{ url_for('question', id=id+1) }}" > Next Question </ a > </ body > </ html > In each of these, the parts in {{ }} are placeholders for the values that will be filled in by the Flask app. The url_for function generates URLs for the given endpoints, and id=id+1 in score.html is used to move to the next question. Please note that these HTML files should be placed in a directory named templates in the same directory as your Flask application, as this is the default location where Flask looks for templates. Also, make sure you handle the scenario when the questions are finished. User I want score to return JSON output, and the question page to fetch the JSON output (no libraries please), parse it, and render the output below the question, and then draw a new next question button. I don't need a score.html any more. Assistant Alright, we'll need to modify the Flask application and the question.html accordingly. Here is the updated Flask application where /score returns a JSON object instead of rendering a HTML page: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 from flask import Flask , request , session , redirect , url_for , render_template , jsonify from sklearn.metrics import log_loss from uuid import uuid4 import numpy as np app = Flask ( __name__ ) app . config [ 'SECRET_KEY' ] = 'a very secret key' # replace with your secret key # Global variables scores = {} entries = {} questions = [( "What is 1+1?" , "2" ), ( "What is the capital of France?" , "Paris" )] answers = [ 1 , 0 ] # Suppose for these questions GPT-4 got the first one right and the second one wrong @app . route ( '/home' , methods = [ 'GET' ]) def home (): session [ 'id' ] = str ( uuid4 ()) return render_template ( 'home.html' ) # render a template that explains the game @app . route ( '/question' , methods = [ 'GET' ]) def question (): qid = request . args . get ( 'id' , default = 0 , type = int ) if 'id' in session : question , expected_answer = questions [ qid ] return render_template ( 'question.html' , question = question , expected_answer = expected_answer , id = qid ) else : return redirect ( url_for ( 'home' )) @app . route ( '/score' , methods = [ 'POST' ]) def score (): qid = request . form . get ( 'id' , type = int ) guess = request . form . get ( 'guess' , type = float ) user_id = session . get ( 'id' ) if user_id and qid is not None and guess is not None : # Add to the list of all guesses for that question scores . setdefault ( qid , []) . append ( guess ) # Add to the person's guesses entries . setdefault ( user_id , {})[ qid ] = guess # Compute the person's log loss user_log_loss = log_loss ([ answers [ qid ]], [ guess ], labels = [ 0 , 1 ]) # Compare with others other_scores = scores [ qid ] other_log_losses = log_loss ([ answers [ qid ]] * len ( other_scores ), other_scores , labels = [ 0 , 1 ]) comparison = 'better' if user_log_loss < other_log_losses else 'worse' return jsonify ({ 'user_log_loss' : user_log_loss , 'comparison' : comparison }) else : return redirect ( url_for ( 'home' )) if __name__ == "__main__" : app . run ( debug = True ) And the updated question.html file: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 <!DOCTYPE html> < html > < head > < title > Trivia Game </ title > < script > function fetchScore () { var xhr = new XMLHttpRequest(); xhr.open("POST", "/score", true); xhr.setRequestHeader("Content-Type", "application/x-www-form-urlencoded"); xhr.onreadystatechange = function() { if (this.readyState === XMLHttpRequest.DONE &amp;&amp; this.status === 200) { var result = JSON.parse(xhr.responseText); var scoreDiv = document.getElementById("score"); scoreDiv.textContent = "Your log loss for this question is: " + result.user_log_loss + ". You did " + result.comparison + " than the average player on this question."; var nextButton = document.createElement('a'); nextButton.href = "/question?id=" + (parseInt(document.getElementById('id').value) + 1); nextButton.textContent = "Next Question"; document.body.appendChild(nextButton); } } var guess = document.getElementById("guess").value; var id = document.getElementById("id").value; xhr.send("guess User Please continue Assistant Apologies for the cutoff in the previous message. Here is the completion of the question.html file: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 <!DOCTYPE html> < html > < head > < title > Trivia Game </ title > < script > function fetchScore () { var xhr = new XMLHttpRequest (); xhr . open ( "POST" , "/score" , true ); xhr . setRequestHeader ( "Content-Type" , "application/x-www-form-urlencoded" ); xhr . onreadystatechange = function () { if ( this . readyState === XMLHttpRequest . DONE & amp ; & amp ; this . status === 200 ) { var result = JSON . parse ( xhr . responseText ); var scoreDiv = document . getElementById ( "score" ); scoreDiv . textContent = "Your log loss for this question is: " + result . user_log_loss + ". You did " + result . comparison + " than the average player on this question." ; var nextButton = document . createElement ( 'a' ); nextButton . href = "/question?id=" + ( parseInt ( document . getElementById ( 'id' ). value ) + 1 ); nextButton . textContent = "Next Question" ; document . body . appendChild ( nextButton ); } } var guess = document . getElementById ( "guess" ). value ; var id = document . getElementById ( "id" ). value ; xhr . send ( "guess=" + guess + "&amp;id=" + id ); } </ script > </ head > < body > < h1 > Question </ h1 > < p > {{ question }} </ p > < form onsubmit = "event.preventDefault(); fetchScore();" > < label for = "guess" > Your guess (probability that GPT-4 will give the answer "{{ expected_answer }}"): </ label > < input type = "range" id = "guess" name = "guess" min = "0" max = "1" step = "0.01" required > < input type = "hidden" id = "id" name = "id" value = "{{ id }}" > < input type = "submit" value = "Submit" > </ form > < div id = "score" ></ div > </ body > </ html > In the question.html file, a JavaScript function fetchScore() is used to send a POST request to /score when the form is submitted. The function parses the returned JSON, displays the user's score, and creates a "Next Question" button. The form's onsubmit attribute is set to call fetchScore() and prevent the form's default submission behavior, which would refresh the page. This way, the user stays on the same page after submitting their guess, and the page is only refreshed when they click "Next Question". Please ensure to handle the scenario when the questions are finished in both the Flask application and the question.html file. User Great. Now I want you to rewrite score to: - Simplify the code by putting an early abort return if there's no session instead of indenting it all. - Returning not just a better/worse comparison but a histogram of how well the person did compared to everyone else drawn as a jpeg with matplotlib. Assistant Sure, here's how you can restructure your /score route with an early return if there's no valid session: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 from flask import send_file @app . route ( '/score' , methods = [ 'POST' ]) def score (): # Ensure there is a valid session user_id = session . get ( 'id' ) if not user_id : return redirect ( url_for ( 'home' )) qid = request . form . get ( 'id' , type = int ) guess = request . form . get ( 'guess' , type = float ) if qid is None or guess is None : return redirect ( url_for ( 'home' )) # Add to the list of all guesses for that question scores . setdefault ( qid , []) . append ( guess ) # Add to the person's guesses entries . setdefault ( user_id , {})[ qid ] = guess # Compute the person's log loss user_log_loss = log_loss ([ answers [ qid ]], [ guess ], labels = [ 0 , 1 ]) # Compare with others other_scores = scores [ qid ] other_log_losses = [ log_loss ([ answers [ qid ]], [ score ], labels = [ 0 , 1 ]) for score in other_scores ] comparison = 'better' if user_log_loss < np . mean ( other_log_losses ) else 'worse' # Draw a histogram using matplotlib and save it as jpeg import matplotlib.pyplot as plt plt . hist ( other_log_losses , bins = 30 , alpha = 0.5 ) plt . axvline ( user_log_loss , color = 'r' , linestyle = 'dashed' , linewidth = 2 ) plt . title ( 'Your score compared to others' ) plt . xlabel ( 'Log Loss' ) plt . ylabel ( 'Count' ) plt . grid ( True ) plt . savefig ( 'histogram.jpg' ) plt . close () return jsonify ({ 'user_log_loss' : user_log_loss , 'comparison' : comparison , 'image_url' : url_for ( 'static' , filename = 'histogram.jpg' )}) The /score route now draws a histogram of the log losses of all players using matplotlib, and then saves it as a histogram.jpg in the same directory as your Flask application. The route then returns the URL of this image along with the other data in the JSON object. You will need to move the histogram.jpg file to a directory named static (in the same directory as your Flask application) after it's created, or change the directory where the image is saved to the static directory directly. Note: You may need to install matplotlib using pip if you haven't already: 1 pip install matplotlib Also note, you'll need to handle multiple requests to the /score endpoint concurrently, as the current implementation would overwrite the histogram.jpg file. Consider creating a unique filename for each histogram, perhaps by using the user's session ID and/or a timestamp. User Can you make it not have a race condition for the file saving with some io buffer? Assistant Sure. Rather than writing the image file to disk and then reading it back again, you can use an io.BytesIO object as an in-memory buffer when saving the figure. Here's how to update the /score route: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 from io import BytesIO from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure @app . route ( '/score' , methods = [ 'POST' ]) def score (): # Ensure there is a valid session user_id = session . get ( 'id' ) if not user_id : return redirect ( url_for ( 'home' )) qid = request . form . get ( 'id' , type = int ) guess = request . form . get ( 'guess' , type = float ) if qid is None or guess is None : return redirect ( url_for ( 'home' )) # Add to the list of all guesses for that question scores . setdefault ( qid , []) . append ( guess ) # Add to the person's guesses entries . setdefault ( user_id , {})[ qid ] = guess # Compute the person's log loss user_log_loss = log_loss ([ answers [ qid ]], [ guess ], labels = [ 0 , 1 ]) # Compare with others other_scores = scores [ qid ] other_log_losses = [ log_loss ([ answers [ qid ]], [ score ], labels = [ 0 , 1 ]) for score in other_scores ] comparison = 'better' if user_log_loss < np . mean ( other_log_losses ) else 'worse' # Draw a histogram using matplotlib and save it as jpeg in memory fig = Figure () canvas = FigureCanvas ( fig ) ax = fig . add_subplot ( 111 ) ax . hist ( other_log_losses , bins = 30 , alpha = 0.5 ) ax . axvline ( user_log_loss , color = 'r' , linestyle = 'dashed' , linewidth = 2 ) ax . set_title ( 'Your score compared to others' ) ax . set_xlabel ( 'Log Loss' ) ax . set_ylabel ( 'Count' ) ax . grid ( True ) buf = BytesIO () canvas . print_figure ( buf , format = 'jpeg' ) buf . seek ( 0 ) return send_file ( buf , mimetype = 'image/jpeg' , as_attachment = False ) Now, the /score route returns the JPEG image directly. You would need to modify your fetchScore function in question.html to handle this image response. The image can be displayed on the page using a blob URL. Unfortunately, this would not work directly with the current structure of fetchScore because it can either handle JSON or blob data but not both at the same time. One solution would be to split the response into two separate API calls - one for the JSON data and another for the image. However, you also have to note that the in-memory method might be less efficient in terms of memory usage compared to saving the image on disk, especially if the image is large or if there are many concurrent requests. The image data is stored in the memory until the garbage collector deletes it, which might not happen immediately after the request is finished. User Adjust the HTML too please Assistant Here's how you can update the question.html file to handle the separate JSON and image responses: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 <!DOCTYPE html> < html > < head > < title > Trivia Game </ title > < script > function fetchScore () { var xhr = new XMLHttpRequest (); xhr . open ( "POST" , "/score" , true ); xhr . setRequestHeader ( "Content-Type" , "application/x-www-form-urlencoded" ); xhr . onreadystatechange = function () { if ( this . readyState === XMLHttpRequest . DONE & amp ; & amp ; this . status === 200 ) { var result = JSON . parse ( xhr . responseText ); var scoreDiv = document . getElementById ( "score" ); scoreDiv . textContent = "Your log loss for this question is: " + result . user_log_loss + ". You did " + result . comparison + " than the average player on this question." ; fetchHistogram (); } } var guess = document . getElementById ( "guess" ). value ; var id = document . getElementById ( "id" ). value ; xhr . send ( "guess=" + guess + "&amp;id=" + id ); } function fetchHistogram () { var xhr = new XMLHttpRequest (); xhr . open ( "POST" , "/score/histogram" , true ); xhr . setRequestHeader ( "Content-Type" , "application/x-www-form-urlencoded" ); xhr . responseType = 'blob' ; xhr . onload = function ( e ) { if ( this . status == 200 ) { var img = document . createElement ( "img" ); img . src = URL . createObjectURL ( this . response ); document . body . appendChild ( img ); } }; var guess = document . getElementById ( "guess" ). value ; var id = document . getElementById ( "id" ). value ; xhr . send ( "guess=" + guess + "&amp;id=" + id ); } </ script > </ head > < body > < h1 > Question </ h1 > < p > {{ question }} </ p > < form onsubmit = "event.preventDefault(); fetchScore();" > < label for = "guess" > Your guess (probability that GPT-4 will give the answer "{{ expected_answer }}"): </ label > < input type = "range" id = "guess" name = "guess" min = "0" max = "1" step = "0.01" required > < input type = "hidden" id = "id" name = "id" value = "{{ id }}" > < input type = "submit" value = "Submit" > </ form > < div id = "score" ></ div > </ body > </ html > In the updated question.html , the fetchScore function now calls a separate fetchHistogram function to retrieve the histogram image once it's done with processing the JSON response. The fetchHistogram function retrieves the histogram image as a blob and then creates an image tag with the blob as its source. You'll also need to adjust your Flask application to accommodate this. Split the current /score route into two routes: /score for returning the JSON data, and /score/histogram for returning the image. Here is an example of how the updated Flask application could look like: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 @app . route ( '/score' , methods = [ 'POST' ]) def score (): # Ensure there is a valid session user_id = session . get ( 'id' ) if not user_id : return redirect ( url_for ( 'home' )) qid = request . form . get ( 'id' , type = int ) guess = request . form . get ( 'guess' , type = float ) if qid is None or guess is None : return redirect ( url_for ( 'home' )) # Add to the list of all guesses for that question scores . setdefault ( qid , []) . append ( guess ) # Add to the person's guesses entries . setdefault ( user_id , {})[ qid ] = guess # Compute the person's log loss Assistant user_log_loss = log_loss([answers[qid]], [guess], labels=[0, 1]) # Compare with others other_scores = scores[qid] other_log_losses = [log_loss([answers[qid]], [score], labels=[0, 1]) for score in other_scores] comparison = 'better' if user_log_loss < np.mean(other_log_losses) else 'worse' return jsonify({'user_log_loss': user_log_loss, 'comparison': comparison}) @app.route('/score/histogram', methods=['POST']) def score_histogram(): # Ensure there is a valid session user_id = session.get('id') if not user_id: return redirect(url_for('home')) qid = request.form.get('id', type=int) guess = request.form.get('guess', type=float) if qid is None or guess is None: return redirect(url_for('home')) other_scores = scores[qid] other_log_losses = [log_loss([answers[qid]], [score], labels=[0, 1]) for score in other_scores] user_log_loss = log_loss([answers[qid]], [guess], labels=[0, 1]) # Draw a histogram using matplotlib and save it as jpeg in memory fig = Figure() canvas = FigureCanvas(fig) ax = fig.add_subplot(111) ax.hist(other_log_losses, bins=30, alpha=0.5) ax.axvline(user_log_loss, color='r', linestyle='dashed', linewidth=2) ax.set_title('Your score compared to others') ax.set_xlabel('Log Loss') ax.set_ylabel('Count') ax.grid(True) buf = BytesIO() canvas.print_figure(buf, format='jpeg') buf.seek(0) return send_file(buf, mimetype='image/jpeg', as_attachment=False) 1 User Remember on the question page I want a "next question" button to show up after the graph comes back. Don't let the user also submit the same question multiple times. Check this in the background not the foreground. Assistant To prevent users from submitting the same question multiple times, you can modify your Flask routes to check whether a user has already submitted a guess for a particular question before processing the submission. You can add this check in both /score and /score/histogram routes: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 @app . route ( '/score' , methods = [ 'POST' ]) def score (): # Ensure there is a valid session user_id = session . get ( 'id' ) if not user_id : return redirect ( url_for ( 'home' )) qid = request . form . get ( 'id' , type = int ) guess = request . form . get ( 'guess' , type = float ) if qid is None or guess is None : return redirect ( url_for ( 'home' )) # Check if user has already answered this question if qid in entries . setdefault ( user_id , {}): return jsonify ({ 'error' : 'You have already submitted an answer for this question.' }) # ... The same check can be added to /score/histogram route. If the user has already answered the question, the server responds with an error message. In your JavaScript, check for this error message and display it to the user: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 function fetchScore () { var xhr = new XMLHttpRequest (); // ... if ( this . readyState === XMLHttpRequest . DONE & amp ; & amp ; this . status === 200 ) { var result = JSON . parse ( xhr . responseText ); if ( result . error ) { alert ( result . error ); return ; } // ... } // ... } function fetchHistogram () { var xhr = new XMLHttpRequest (); // ... xhr . onload = function ( e ) { if ( this . status == 200 ) { var img = document . createElement ( "img" ); img . src = URL . createObjectURL ( this . response ); document . body . appendChild ( img ); createNextButton (); } else { var reader = new FileReader (); reader . onload = function () { var result = JSON . parse ( reader . result ); if ( result . error ) { alert ( result . error ); } }; reader . readAsText ( this . response ); } }; // ... } function createNextButton () { var btn = document . createElement ( "button" ); btn . textContent = "Next Question" ; btn . onclick = function () { window . location . href = '/question?id=' + ( parseInt ( document . getElementById ( "id" ). value ) + 1 ); }; document . body . appendChild ( btn ); } In the updated JavaScript, fetchScore and fetchHistogram check if the server response contains an error message, and if so, they display this message to the user. The fetchHistogram function calls a new createNextButton function after the histogram image is displayed. This function creates a "Next Question" button, which redirects the user to the next question when clicked. The ID of the next question is assumed to be the current question's ID incremented by 1. User Now when you submit, also grey out the previous question area. Also change the format of the question to have: Question: Answer: [ the question] [the answer] Side by side like this. I also want the slider to be the full width of the page, and there to be a text number box that shows your current probability. Assistant Sure, you can modify the HTML and CSS in your question.html file to meet these requirements. Here's how: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 <!DOCTYPE html> < html > < head > < title > Trivia Game </ title > < style > . question-answer { display : flex ; justify-content : space-between ; margin-bottom : 20 px ; } . disabled-content { pointer-events : none ; opacity : 0.4 ; } # guess { width : 100 % ; } </ style > <!-- The rest of your script here... --> </ head > < body > < div id = "content" > < div class = "question-answer" > < div > < h1 > Question: </ h1 > < p > {{ question }} </ p > </ div > < div > < h1 > Answer: </ h1 > < p > {{ expected_answer }} </ p > </ div > </ div > < form id = "guess-form" onsubmit = "event.preventDefault(); fetchScore();" > < label for = "guess" > Your guess (probability that GPT-4 will give the expected answer): </ label > < input type = "range" id = "guess" name = "guess" min = "0" max = "1" step = "0.01" required oninput = "updateGuessValue(this.value);" > < output id = "guess-output" for = "guess" ></ output > < input type = "hidden" id = "id" name = "id" value = "{{ id }}" > < input type = "submit" value = "Submit" > </ form > </ div > < div id = "score" ></ div > </ body > </ html > I've added some CSS rules to make the slider full width and display the question and answer side by side. The updateGuessValue function is triggered when the slider is moved, and it updates the number box with the current value of the slider. The disabled-content class is added to the content when the score is being fetched, to disable user interaction and visually indicate that it's been submitted. Here are the corresponding JavaScript changes: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 function fetchScore () { document . getElementById ( "content" ). classList . add ( "disabled-content" ); var xhr = new XMLHttpRequest (); // Rest of your code here... } function fetchHistogram () { var xhr = new XMLHttpRequest (); // Rest of your code here... } function updateGuessValue ( val ) { document . getElementById ( "guess-output" ). value = val ; } Please note that this code assumes that the id value for the questions increases by one for each subsequent question. In practice, you would likely use a unique identifier (e.g. UUID) for each question, in which case you would need a different way to determine the id for the "Next Question" button. User I want the numbers to show up in an input box after the "your guess" text that will also edit the slider and vice versa. Center the submit button and make it bigger. Center the question and answer text. Each should take 1/2 of the space. Assistant You can adjust the HTML and CSS to achieve these requirements. Let's update the question.html : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 <!DOCTYPE html> < html > < head > < title > Trivia Game </ title > < style > . question-answer { display : flex ; justify-content : space-between ; margin-bottom : 20 px ; } . question-answer div { width : 50 % ; text-align : center ; } . disabled-content { pointer-events : none ; opacity : 0.4 ; } # guess { width : 100 % ; } # guess-form { display : flex ; flex-direction : column ; align-items : center ; } # guess-form input [ type = "submit" ] { width : 50 % ; padding : 10 px ; font-size : 1.2 em ; } </ style > <!-- The rest of your script here... --> </ head > < body > < div id = "content" > < div class = "question-answer" > < div > < h1 > Question: </ h1 > < p > {{ question }} </ p > </ div > < div > < h1 > Answer: </ h1 > < p > {{ expected_answer }} </ p > </ div > </ div > < form id = "guess-form" onsubmit = "event.preventDefault(); fetchScore();" > < label for = "guess" > Your guess (probability that GPT-4 will give the expected answer): </ label > < div > < input type = "range" id = "guess" name = "guess" min = "0" max = "1" step = "0.01" required oninput = "updateGuessValue(this.value);" > < input type = "number" id = "guess-output" min = "0" max = "1" step = "0.01" onchange = "updateSliderValue(this.value);" > </ div > < input type = "hidden" id = "id" name = "id" value = "{{ id }}" > < input type = "submit" value = "Submit" > </ form > </ div > < div id = "score" ></ div > </ body > </ html > In this revision, I've placed the range and number inputs in a div to keep them together. The onchange event on the number input updates the slider when the number is manually changed. The Submit button is also made larger and centered by adding CSS styles. The corresponding JavaScript updates are: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 function fetchScore () { document . getElementById ( "content" ). classList . add ( "disabled-content" ); var xhr = new XMLHttpRequest (); // Rest of your code here... } function fetchHistogram () { var xhr = new XMLHttpRequest (); // Rest of your code here... } function updateGuessValue ( val ) { document . getElementById ( "guess-output" ). value = val ; } function updateSliderValue ( val ) { document . getElementById ( "guess" ). value = val ; } Now, changing the number in the input box will update the position of the slider, and moving the slider will update the number in the input box. User Also make the next page button big, and make it centered Assistant To make the "Next Question" button larger and centered, we can add some additional CSS styles. Here's how to modify your question.html : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 <!DOCTYPE html> < html > < head > < title > Trivia Game </ title > < style > /* Your existing styles... */ # next-button { display : block ; width : 50 % ; margin : 20 px auto ; padding : 10 px ; font-size : 1.2 em ; text-align : center ; } </ style > <!-- The rest of your script here... --> </ head > < body > < div id = "content" > <!-- Your existing HTML content... --> </ div > < div id = "score" ></ div > </ body > </ html > Now, in your JavaScript, assign the CSS ID next-button to your "Next Question" button: 1 2 3 4 5 6 7 8 9 function createNextButton () { var btn = document . createElement ( "button" ); btn . id = "next-button" ; btn . textContent = "Next Question" ; btn . onclick = function () { window . location . href = '/question?id=' + ( parseInt ( document . getElementById ( "id" ). value ) + 1 ); }; document . body . appendChild ( btn ); } This will style the "Next Question" button to be larger, centered, and placed a bit further from the image. The width is set to 50% of the parent container, and margin: auto is used to center the button. The font size, padding, and text alignment are also adjusted for better appearance. User If you have already answered, you should not alert(), but still show the graph, just not register the new score. and say you've already answered this question and that this won't update your score. Assistant Sure, you can modify your Flask route and the JavaScript code to display the graph and a message instead of an alert when a user tries to submit an answer to a question they've already answered. Firstly, modify the Flask route /score to return the score and the histogram even if the question has already been answered: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 @app . route ( '/score' , methods = [ 'POST' ]) def score (): # Ensure there is a valid session user_id = session . get ( 'id' ) if not user_id : return redirect ( url_for ( 'home' )) qid = request . form . get ( 'id' , type = int ) guess = request . form . get ( 'guess' , type = float ) if qid is None or guess is None : return redirect ( url_for ( 'home' )) # If the user has already answered the question, add a flag to the response already_answered = qid in entries . setdefault ( user_id , {}) if not already_answered : entries [ user_id ][ qid ] = guess scores . setdefault ( qid , []) . append ( guess ) # ... # The rest of your code to compute the score and return the response # ... return jsonify ({ 'score' : score , 'comparison' : comparison , 'already_answered' : already_answered }) Now, the server will include a flag in the JSON response to indicate whether the user has already answered the question. Next, modify your JavaScript function fetchScore() to handle this flag: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 function fetchScore () { var xhr = new XMLHttpRequest (); // ... if ( this . readyState === XMLHttpRequest . DONE & amp ; & amp ; this . status === 200 ) { var result = JSON . parse ( xhr . responseText ); if ( result . error ) { alert ( result . error ); return ; } var scoreDiv = document . getElementById ( "score" ); scoreDiv . textContent = 'Your score: ' + result . score . toFixed ( 2 ) + '. ' + 'This is ' + result . comparison + ' than the average score.' ; if ( result . already_answered ) { scoreDiv . textContent += ' You have already answered this question. This score will not update your record.' ; } fetchHistogram (); } // ... } With these changes, when a user attempts to answer a question they've already answered, they will still see the graph and their score, and a message will be displayed indicating that their score for this question won't be updated. User Make the page also show what the GPT-4 output was from a global variable actual_text when the user hits submit. Make it print it out word-by-word like a typewriter effect at about 5 words per second. Only then does it 1. show you got it right or wrong (after 1 second delay) 2. show your loss and histogram (after 1 more second delay) Assistant To achieve this effect, we can introduce some changes in the JavaScript on your question.html page. We need to use setInterval function to print out the text word by word, and setTimeout to delay the display of the right/wrong message and the loss/histogram. First, let's modify the fetchScore function to get the GPT-4 output and start the "typewriter" effect: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 function fetchScore () { var xhr = new XMLHttpRequest (); // ... if ( this . readyState === XMLHttpRequest . DONE & amp ; & amp ; this . status === 200 ) { var result = JSON . parse ( xhr . responseText ); if ( result . error ) { alert ( result . error ); return ; } // Get the GPT-4 output and start the typewriter effect var gptOutput = result . gpt_output ; var words = gptOutput . split ( ' ' ); var outputDiv = document . getElementById ( "gpt-output" ); var wordIndex = 0 ; var typewriterInterval = setInterval ( function () { outputDiv . textContent += words [ wordIndex ] + ' ' ; wordIndex ++ ; if ( wordIndex >= words . length ) { clearInterval ( typewriterInterval ); // After the GPT-4 output is fully displayed, show the right/wrong message after a delay setTimeout ( function () { // Assume the variable `correct` is returned in the response to indicate if the guess is correct or not outputDiv . textContent += result . correct ? 'You got it right!' : 'You got it wrong.' ; // After the right/wrong message, show the loss and histogram after another delay setTimeout ( function () { var scoreDiv = document . getElementById ( "score" ); scoreDiv . textContent = 'Your score: ' + result . score . toFixed ( 2 ) + '. ' + 'This is ' + result . comparison + ' than the average score.' ; if ( result . already_answered ) { scoreDiv . textContent += ' You have already answered this question. This score will not update your record.' ; } fetchHistogram (); }, 1000 ); }, 1000 ); } }, 200 ); // 200ms delay between each word, which is about 5 words per second } } Please add an HTML element with the id "gpt-output" to your HTML file where you'd like the GPT-4 output to be displayed: 1 < div id = "gpt-output" ></ div > Remember to update your Flask route /score to include the GPT-4 output and whether the guess is correct in the JSON response: 1 2 3 4 5 6 7 8 9 10 11 12 13 @app . route ( '/score' , methods = [ 'POST' ]) def score (): # ... # Compute the score and check the guess... # ... return jsonify ({ 'score' : score , 'comparison' : comparison , 'already_answered' : already_answered , 'gpt_output' : actual_text [ qid ], # assuming you have a global variable `actual_text` that stores the GPT-4 output 'correct' : int ( guess == answers [ qid ]) # convert to int so it can be JSON serialized }) User Can you write some actual_text for these questions Assistant Sure, for this example, we can manually create some example actual_text as responses from GPT-4. Keep in mind that GPT-4 doesn't necessarily output a simple, straightforward response to a question as it depends on the model's settings and the context provided. Let's assume you have these questions and expected answers: 1 2 3 4 5 questions = [( "What is the capital of France?" , "Paris" ), ( "Who wrote the novel '1984'?" , "George Orwell" ), ( "What is the symbol for gold in the periodic table?" , "Au" )] answers = [ 1 , 1 , 1 ] # Assume that GPT-4 got all the answers correct For these questions, GPT-4's responses could look like this: 1 2 3 actual_text = [ "The capital of France is Paris." , "The novel '1984' was written by George Orwell." , "In the periodic table, the symbol for gold is Au." ] You can change actual_text to be whatever text you want to be printed out when the user submits their answer. It's set to match the answers here, but it would usually be the text generated by the model. User I moved the javascript to question.js file how do I edit the main.py Assistant To serve static files such as JavaScript, CSS, and images, Flask uses a folder named static by default. This folder should be located in the same directory as the script where you created the Flask application instance. You need to move your question.js into a directory named static in your project: 1 2 3 4 5 6 / static / question . js / templates / question . html / home . html / main . py Then, in your question.html , you need to use the url_for function to include the JavaScript file: 1 2 3 4 5 6 7 8 9 10 <!DOCTYPE html> < html > < head > < title > Trivia Game </ title > < script src = "{{ url_for('static', filename='question.js') }}" ></ script > </ head > < body > <!-- Your HTML content... --> </ body > </ html > This should work because Flask automatically serves files in the static directory at the /static endpoint, and url_for('static', filename='question.js') will generate the URL to access question.js . Note: Ensure to wrap url_for with double braces, {{ and }} , because this is Jinja2 syntax to