Are you fascinated by the idea of creating a tool that can solve complex math problems with ease? Imagine having a personal math tutor at your fingertips, ready to explain step-by-step solutions in a clear and understandable manner. With the power of OpenAI, you can turn this idea into reality. In this tutorial, we'll walk you through the process of building your own Math Solver using OpenAI's GPT-3.5-turbo model. Let's dive in!
Introduction
In this tutorial, we'll guide you through creating your own AI-powered Math Solver using OpenAI's powerful language model. Before we begin, let's take a moment to understand what we'll be building. Our Math Solver will take a math question as input and provide a detailed solution, explaining each step and the concepts used. This tool can be incredibly useful for students, educators, and anyone who wants to enhance their math skills. We'll be using Python and the OpenAI API to create this project.
What You Will Learn
In this tutorial blog, we will cover:
Setting up the OpenAI API
Writing the code to interact with OpenAI's GPT-3.5-turbo model
Building a user interface with Panel
Integrating everything to create a functional Math Solver
By the end of this tutorial, you'll have a fully operational Math Solver that you can customize and expand upon.
Prerequisites
Before we dive in, make sure you have the following:
A basic understanding of Python programming
An OpenAI API key
An internet connection
If you've read my previous tutorials, such as those on creating a Python Code Debugger or Fact Checker, you'll find that much of the code structure here is similar. The key components like setting up the OpenAI API, handling user inputs, and constructing the user interface are consistent across these projects. The primary difference is in the prompt that we pass to the AI, which tailors the tool's functionality to specific tasks. Whether you're building a Math Solver, a Python Code Debugger, the approach is largely the same, allowing you to adapt these techniques to a wide range of AI-driven applications.
Setting Up the Environment
First, we need to install the OpenAI Python package. Open your terminal and run:
pip install openai==0.28This command installs the necessary package for interacting with the OpenAI API.
Writing the Code
Let's start by importing the required libraries and setting up our OpenAI API key.
import openai
import os
openai.api_key = 'your-api-key-here'Replace 'your-api-key-here'Â with your actual OpenAI API key.
Creating the Completion Function
Next, we'll define a function to get responses from the OpenAI model. This function sends a prompt to the model and returns the generated response.
def get_completion(prompt, model="gpt-3.5-turbo"):
messages = [{"role": "user", "content": prompt}]
response = openai.ChatCompletion.create(
model = model,
messages = messages,
temperature = 0, # this is the degree of randomness of the model's output
)
return response.choices[0].message["content"]Now, let's break down this code step-by-step in a way that a beginner can easily understand.
The Function: get_completion
This function is designed to take a prompt (a question or statement) and get a response from OpenAI's language model. Here's how it works:
Function Definition
def get_completion(prompt, model="gpt-3.5-turbo"):def: This keyword is used to define a new function in Python.
get_completion: This is the name of our function.
prompt: This is a parameter that the function takes. It's the question or statement we want to send to the OpenAI model.
model="gpt-3.5-turbo": This is another parameter with a default value. If you don't provide a model when you call the function, it will use "gpt-3.5-turbo" by default.
Preparing the Message
messages = [{"role": "user", "content": prompt}]messages: This is a list that will contain our message to the OpenAI model.
[{"role": "user", "content": prompt}]: This creates a dictionary (a kind of list) with two key-value pairs:
"role": "user": This indicates that the message is coming from the user.
"content": prompt: This is the actual message or question we want to send to the model.
Getting a Response from OpenAI
response = openai.ChatCompletion.create(
model = model,
messages = messages,
temperature = 0, # this is the degree of randomness of the model's output
)openai.ChatCompletion.create(): This function sends our message to the OpenAI model and gets a response.
model=model: We're specifying which model to use. By default, it's "gpt-3.5-turbo".
messages=messages: We're sending the list of messages we created earlier.
temperature=0: This controls how random or creative the model's responses will be. A temperature of 0 makes the model's output very predictable.
Returning the Response
return response.choices[0].message["content"]return: This keyword is used to send back the result from the function.
response.choices[0].message["content"]: This accesses the content of the response from the model.
response.choices: The response can contain multiple choices (answers).
[0]: We're choosing the first response.
.message["content"]: We're getting the actual text content of the response.
Building the User Interface
Now, let's create the user interface using Panel. First, import Panel and initialize it.
import panel as pn
pn.extension()We'll then define the main function to handle user input and display the math solutions.
def collect_messages(debug=False):
user_input = inp.value_input
if debug: print(f"Input = {user_input}")
if user_input == "":
return
inp.value = ''
prompt = f"""
Please solve the provided math question in a step by step guided manner in an easy to understand format with appropriate
text formatting.
Explanation for the use of any concept, theorem, axiom or identity involved in the solution should also be provided if any don't mention.
If you are not able to solve a question then politely apologize.
You will solve only mathematics question no other subject would be entertained. If the user provides a non-mathematical
question then ask the user to provide mathematical question.
The question is delimited with triple backticks.
Question: '''{user_input}'''
"""
global context
response, context = get_completion(prompt), context
context.append({'role':'Math solver', 'content':f"{response}"})
panels.append(
pn.Row('Question:', pn.pane.Markdown(user_input, width=600)))
panels.append(
pn.Row('Solver:', pn.pane.Markdown(response, width=600, style={'background-color': '#e0e1dd'})))
return pn.Column(*panels)Let's break down this code step-by-step.
The Function: collect_messages
This function handles the collection of user input (a math question), sends it to the OpenAI model to get a response, and then displays both the question and the answer.
Function Definition
def collect_messages(debug=False):def: This keyword is used to define a new function in Python.
collect_messages: This is the name of our function.
debug=False: This is a parameter with a default value. If debug is set to True, the function will print the user input for debugging purposes.
Getting User Input
user_input = inp.value_inputuser_input: This variable stores the value of the user's input from a text input widget (inp).
Debugging (Optional)
if debug: print(f"Input = {user_input}")if debug: This checks if the debug parameter is set to True.
print(f"Input = {user_input}"): If debug is True, this line prints the user input. This helps with debugging to see what the input was.
Handling Empty Input
if user_input == "":
return
inp.value = ''if user_input == "": This checks if the user input is an empty string (i.e., the user didn't type anything).
return: If the input is empty, the function stops and returns nothing.
inp.value = '': If there was input, this clears the input field for the next question.
Creating the Prompt for OpenAI
Crafting an effective prompt is vital when fine-tuning large language models (LLMs) to perform specific tasks like solving math problems. The prompt sets the stage for how the model interprets the user's query and generates the response, ensuring the output is both accurate and easy to understand.
prompt = f"""
Please solve the provided math question in a step by step guided manner in an easy to understand format with appropriate
text formatting.
Explanation for the use of any concept, theorem, axiom or identity involved in the solution should also be provided if any don't mention.
If you are not able to solve a question then politely apologize.
You will solve only mathematics question no other subject would be entertained. If the user provides a non-mathematical
question then ask the user to provide mathematical question.
The question is delimited with triple backticks.
Question: '''{user_input}'''
"""This prompt is carefully structured to ensure the LLM provides a detailed, step-by-step solution to the math problem, with clear explanations for any concepts used. By including specific instructions, the prompt guides the model to deliver responses that are not only accurate but also user-friendly.
In the context of fine-tuning, such a prompt plays a crucial role in shaping the LLM's behavior. It helps ensure that the model remains focused on solving math problems, responds appropriately to non-mathematical queries, and maintains a polite tone throughout the interaction. By fine-tuning with a prompt like this, the model becomes more adept at handling math-related tasks, enhancing the overall user experience and ensuring that the outputs align with the intended purpose.
prompt: This variable stores a formatted string that instructs the OpenAI model on how to respond to the user's input.
f""" ... """: This is a multi-line f-string, which allows us to include the user_input directly in the string.
Getting the Response from OpenAI and Managing Context
global context
response, context = get_completion(prompt), context
context.append({'role':'Math solver', 'content':f"{response}"})global context: This tells the function to use the context variable from the global scope, not just within the function.
response, context = get_completion(prompt), context: This line calls the get_completion function to get the response from OpenAI and assigns it to response. The context remains unchanged.
context.append({'role':'Math solver', 'content':f"{response}"}): This adds the response to the context list, which keeps track of the conversation history.
Displaying the Input and Output
panels.append(
pn.Row('Question:', pn.pane.Markdown(user_input, width=600)))
panels.append(
pn.Row('Solver:', pn.pane.Markdown(response, width=600, style={'background-color': '#e0e1dd'})))panels.append(...): This adds new rows to the panels list.
pn.Row('Question:', pn.pane.Markdown(user_input, width=600)): This creates a row displaying the user's question.
pn.Row('Solver:', pn.pane.Markdown(response, width=600, style={'background-color': '#e0e1dd'})): This creates a row displaying the model's response, with a background color for distinction.
Returning the Result
return pn.Column(*panels)return pn.Column(*panels): This combines all the rows in the panels list into a single column layout and returns it. This column will display both the question and the answer.
Assembling the Components
Finally, let's assemble the components to create a complete Math Solver application. This code sets up a text input for the math question, a button to trigger the solver, and a panel to display the questions and solutions.
panels = [] # collect display
context = [ {'role':'Math solver', 'content':"You are a Math solver."} ]
inp = pn.widgets.TextInput(placeholder='Enter question here...', sizing_mode='stretch_width')
button_conversation = pn.widgets.Button(name="Solve", button_type= "default")
interactive_conversation = pn.bind(collect_messages, button_conversation)
dashboard = pn.Column(
inp,
pn.Row(button_conversation),
pn.panel(interactive_conversation, loading_indicator=True, height=300),
)
dashboardSetting Up the Interface
This part of the code is responsible for setting up the user interface using the Panel library. It creates the input field, button, and layout to display the questions and answers.
Initialize Panels List
panels = []Â # collect displaypanels: This is an empty list that will hold the different display components (the question and the answer).
Initialize Context
context = [ {'role':'Math solver', 'content':"You are a Math solver."} ]context: This list contains an initial message to set the context for the Math Solver. It tells the model that it is supposed to act as a Math solver.
Create Input Field
inp = pn.widgets.TextInput(placeholder='Enter question here...', sizing_mode='stretch_width')inp: This is a text input widget where the user can type their math question.
placeholder='Enter question here…': This is the placeholder text that appears inside the input field before the user types anything.
sizing_mode='stretch_width': This makes the input field stretch to fit the width of the container.
Create Solve Button
button_conversation = pn.widgets.Button(name="Solve", button_type= "default")button_conversation: This is a button widget that the user will click to submit their question.
name="Solve": This sets the label of the button to "Solve".
button_type="default": This sets the style of the button.
Bind Function to Button
interactive_conversation = pn.bind(collect_messages, button_conversation)interactive_conversation: This binds the collect_messages function to the button_conversation button. When the button is clicked, the collect_messages function will be called.
Create Dashboard Layout
dashboard = pn.Column(
inp,
pn.Row(button_conversation),
pn.panel(interactive_conversation, loading_indicator=True, height=300),
)
dashboarddashboard: This is a column layout that contains all the elements of our interface.
pn.Column(...): This creates a vertical layout.
inp: Adds the input field to the column.
pn.Row(button_conversation): Adds the button to the column in a row layout.
pn.panel(interactive_conversation, loading_indicator=True, height=300): Adds the interactive conversation panel to the column. The loading_indicator=True shows a loading animation while the response is being processed, and height=300 sets the height of the panel.
Complete Code
Here’s the entire code in one block for easy copying:
!pip install openai==0.28
import openai
import os
openai.api_key = 'your-api-key-here'
def get_completion(prompt, model="gpt-3.5-turbo"):
messages = [{"role": "user", "content": prompt}]
response = openai.ChatCompletion.create(
model = model,
messages = messages,
temperature = 0,
)
return response.choices[0].message["content"]
import panel as pn
pn.extension()
def collect_messages(debug=False):
user_input = inp.value_input
if debug: print(f"Input = {user_input}")
if user_input == "":
return
inp.value = ''
prompt = f"""
Please solve the provided math question in a step by step guided manner in an easy to understand format with appropriate
text formatting.
Explanation for the use of any concept, theorem, axiom or identity involved in the solution should also be provided if any don't mention.
If you are not able to solve a question then politely apologize.
You will solve only mathematics question no other subject would be entertained. If the user provides a non-mathematical
question then ask the user to provide mathematical question.
The question is delimited with triple backticks.
Question: '''{user_input}'''
"""
global context
response, context = get_completion(prompt), context
context.append({'role':'Math solver', 'content':f"{response}"})
panels.append(
pn.Row('Question:', pn.pane.Markdown(user_input, width=600)))
panels.append(
pn.Row('Solver:', pn.pane.Markdown(response, width=600, style={'background-color': '#e0e1dd'})))
return pn.Column(*panels)
panels = [] # collect display
context = [ {'role':'Math solver', 'content':"You are a Math solver."} ]
inp = pn.widgets.TextInput(placeholder='Enter question here...', sizing_mode='stretch_width')
button_conversation = pn.widgets.Button(name="Solve", button_type= "default")
interactive_conversation = pn.bind(collect_messages, button_conversation)
dashboard = pn.Column(
inp,
pn.Row(button_conversation),
pn.panel(interactive_conversation, loading_indicator=True, height=300),
)
dashboardWe have just built a powerful Math Solver using OpenAI. This project demonstrates the incredible potential of AI in solving complex problems and providing educational support. Feel free to expand this project by adding more features or integrating it with other applications.
If you have any questions or suggestions, feel free to leave a comment below.Â
Happy coding!
screenshots
Here, I am sharing some screenshots that I took after running the project. This will help you understand how it works.
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Demo Video
Other Blogs Links
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