lukejagg / test-canary

Checklist

README.md

• Rewrite the introduction to provide a brief overview of the project, explaining what it does and why it is useful.
• Add a section describing the machine learning model. Explain what it does, how it is trained, and how it is used in the project.
• Add a section describing the web application. Explain what it does and how it interacts with the machine learning model.
• Add a section explaining the configuration options in the config.yaml file. Describe what each option does and how it affects the model.
• Add a section providing instructions on how to set up and run the project. Include any prerequisites and dependencies.
• Add a section on how to contribute to the project. Include instructions on how to report issues, how to suggest improvements, and the process for submitting pull requests.

Here's the PR! #276.

💎 Sweep Pro: I used GPT-4 to create this ticket. You have unlimited GPT-4 tickets. To retrigger Sweep, edit the issue.

Step 1: 🔍 Code Search

I found the following snippets in your repository. I will now analyze these snippets and come up with a plan.

Some code snippets I looked at (click to expand). If some file is missing from here, you can mention the path in the ticket description.

test-canary/config.yaml

Lines 1 to 20 in d773bfa

    
           # Configuration file 
        
           model: 
        
             name: "ResNet50" 
        
             num_classes: 10 
        
             pretrained: true 
        
           training: 
        
             batch_size: 64 
        
             learning_rate: 0.001 
        
             epochs: 50 
        
           data: 
        
             train_dataset: "train_data.csv" 
        
             test_dataset: "test_data.csv" 
        
             shuffle: true 
        
           logging: 
        
             log_file: "log.txt" 
        
             verbose: false

test-canary/README.md

Line 1 in d773bfa

# test

test-canary/train.py

Lines 1 to 96 in d773bfa

    
           import torch 
        
           import torch.nn as nn 
        
           import torch.optim as optim 
        
           import torchvision 
        
           import torchvision.transforms as transforms 
        
           import yaml 
        
           # Define the Convolutional Neural Network architecture 
        
           class CNN(nn.Module): 
        
               def __init__(self, num_classes): 
        
                   super(CNN, self).__init__() 
        
                   self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1) 
        
                   self.relu1 = nn.ReLU() 
        
                   self.pool = nn.MaxPool2d(kernel_size=2, stride=2) 
        
                   self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1) 
        
                   self.relu2 = nn.ReLU() 
        
                   self.fc = nn.Linear(32 * 8 * 8, num_classes) 
        
               def forward(self, x): 
        
                   x = self.conv1(x) 
        
                   x = self.relu1(x) 
        
                   x = self.pool(x) 
        
                   x = self.conv2(x) 
        
                   x = self.relu2(x) 
        
                   x = self.pool(x) 
        
                   x = x.view(x.size(0), -1) 
        
                   x = self.fc(x) 
        
                   return x 
        
           # Load the configuration file 
        
           with open('config.yaml', 'r') as f: 
        
               config = yaml.safe_load(f) 
        
           # Set up the device for training 
        
           device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
        
           # Define the transformation for the input data 
        
           transform = transforms.Compose([ 
        
               transforms.RandomHorizontalFlip(), 
        
               transforms.RandomCrop(32, padding=4), 
        
               transforms.ToTensor(), 
        
               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), 
        
           ]) 
        
           # Load the CIFAR-10 dataset and apply transformations 
        
           trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) 
        
           trainloader = torch.utils.data.DataLoader(trainset, batch_size=config['training']['batch_size'], 
        
                                                     shuffle=config['data']['shuffle'], num_workers=2) 
        
           testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) 
        
           testloader = torch.utils.data.DataLoader(testset, batch_size=config['training']['batch_size'], shuffle=False, 
        
                                                    num_workers=2) 
        
           # Instantiate the CNN model 
        
           model = CNN(config['model']['num_classes']).to(device) 
        
           # Define the loss function and optimizer 
        
           criterion = nn.CrossEntropyLoss() 
        
           optimizer = optim.SGD(model.parameters(), lr=config['training']['learning_rate'], momentum=0.9) 
        
           # Training loop 
        
           for epoch in range(config['training']['epochs']): 
        
               running_loss = 0.0 
        
               for i, data in enumerate(trainloader, 0): 
        
                   # Get the inputs and labels 
        
                   inputs, labels = data[0].to(device), data[1].to(device) 
        
                   # Zero the parameter gradients 
        
                   optimizer.zero_grad() 
        
                   # Forward + backward + optimize 
        
                   outputs = model(inputs) 
        
                   loss = criterion(outputs, labels) 
        
                   loss.backward() 
        
                   optimizer.step() 
        
                   # Print statistics 
        
                   running_loss += loss.item() 
        
                   if i % 200 == 199:    # Print every 200 mini-batches 
        
                       print(f'[{epoch + 1}, {i + 1}] loss: {running_loss / 200:.3f}') 
        
                       running_loss = 0.0 
        
           print("Training finished.") 
        
           # Evaluate the model on the test set 
        
           correct = 0 
        
           total = 0 
        
           with torch.no_grad(): 
        
               for data in testloader: 
        
                   images, labels = data[0].to(device), data[1].to(device) 
        
                   outputs = model(images) 
        
                   _, predicted = torch.max(outputs.data, 1) 
        
                   total += labels.size(0) 
        
                   correct += (predicted == labels).sum().item() 
        
           print(f'Accuracy on the test set: {(100 * correct / total):.2f}%')

test-canary/frontend.html

Lines 1 to 22 in d773bfa

    
           <!DOCTYPE html> 
        
           <html> 
        
           <head> 
        
               <title>Balloon Shop</title> 
        
               <link rel="stylesheet" type="text/css" href="frontend.css"> 
        
           </head> 
        
           <body> 
        
               <h1>Welcome to the Balloon Shop!</h1> 
        
               <img src="balloon.jpg" alt="Balloons"> 
        
               <form> 
        
                   <label for="quantity">Quantity:</label> 
        
                   <input type="number" id="quantity" name="quantity" min="1" max="10"> 
        
                   <br> 
        
                   <label for="color">Color:</label> 
        
                   <select id="color" name="color"> 
        
                       <option value="red">Red</option> 
        
                       <option value="blue">Blue</option> 
        
                       <option value="yellow">Yellow</option> 
        
                   </select> 
        
                   <br> 
        
                   <input type="submit" value="Order"> 
        
               </form>

Step 2: 🧐 Snippet Analysis

From looking through the relevant snippets, I decided to make the following modifications:

File Path	Proposed Changes
`README.md`	Modify README.md with contents: • Rewrite the introduction to provide a brief overview of the project, explaining what it does and why it is useful. • Add a section describing the machine learning model. Explain what it does, how it is trained, and how it is used in the project. • Add a section describing the web application. Explain what it does and how it interacts with the machine learning model. • Add a section explaining the configuration options in the config.yaml file. Describe what each option does and how it affects the model. • Add a section providing instructions on how to set up and run the project. Include any prerequisites and dependencies. • Add a section on how to contribute to the project. Include instructions on how to report issues, how to suggest improvements, and the process for submitting pull requests.

Step 3: 📝 Planning

I have created a plan for writing the pull request. I am now working my plan and coding the required changes to address this issue. Here is the planned pull request:

Update README.md with project description, machine learning model details, web application information, configuration options, setup instructions, and contribution guidelines
sweep/update-readme

Description

This PR updates the README.md file to provide comprehensive documentation for the project. It includes a brief introduction about the project, a description of the machine learning model used, details about the web application, explanations of the configuration options in the config.yaml file, instructions on how to set up and run the project, and guidelines for contributing to the project.

Summary of Changes

Added a brief introduction about the project, explaining what it does and why it is useful.

Added a section describing the machine learning model, including its purpose, training process, and usage in the project.

Added a section describing the web application, explaining its functionality and how it interacts with the machine learning model.

Added a section explaining the configuration options in the config.yaml file, detailing what each option does and how it affects the model.

Added a section providing instructions on how to set up and run the project, including any prerequisites and dependencies.

Added a section on how to contribute to the project, including instructions on reporting issues, suggesting improvements, and submitting pull requests.

Please review these changes and let me know if any further modifications are required.

Step 4: ⌨️ Coding

File	Instructions	Progress	Error logs
`README.md`	Modify README.md with contents: • Rewrite the introduction to provide a brief overview of the project, explaining what it does and why it is useful. • Add a section describing the machine learning model. Explain what it does, how it is trained, and how it is used in the project. • Add a section describing the web application. Explain what it does and how it interacts with the machine learning model. • Add a section explaining the configuration options in the config.yaml file. Describe what each option does and how it affects the model. • Add a section providing instructions on how to set up and run the project. Include any prerequisites and dependencies. • Add a section on how to contribute to the project. Include instructions on how to report issues, how to suggest improvements, and the process for submitting pull requests.	✅ Commit `4a70802`	No errors.

Step 5: 🔁 Code Review

Here are my self-reviews of my changes at sweep/update-readme_1.

Here is the 1st review

No changes required. The modifications in the README.md file are correct and fully implemented. The changes align with the issue's request to rewrite the README file. Good job!

I finished incorporating these changes.

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💡 To recreate the pull request edit the issue title or description.
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	# Configuration file

	model:
	name: "ResNet50"
	num_classes: 10
	pretrained: true

	training:
	batch_size: 64
	learning_rate: 0.001
	epochs: 50

	data:
	train_dataset: "train_data.csv"
	test_dataset: "test_data.csv"
	shuffle: true

	logging:
	log_file: "log.txt"
	verbose: false

	import torch
	import torch.nn as nn
	import torch.optim as optim
	import torchvision
	import torchvision.transforms as transforms
	import yaml

	# Define the Convolutional Neural Network architecture
	class CNN(nn.Module):
	def __init__(self, num_classes):
	super(CNN, self).__init__()
	self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
	self.relu1 = nn.ReLU()
	self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
	self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
	self.relu2 = nn.ReLU()
	self.fc = nn.Linear(32 * 8 * 8, num_classes)

	def forward(self, x):
	x = self.conv1(x)
	x = self.relu1(x)
	x = self.pool(x)
	x = self.conv2(x)
	x = self.relu2(x)
	x = self.pool(x)
	x = x.view(x.size(0), -1)
	x = self.fc(x)
	return x

	# Load the configuration file
	with open('config.yaml', 'r') as f:
	config = yaml.safe_load(f)

	# Set up the device for training
	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

	# Define the transformation for the input data
	transform = transforms.Compose([
	transforms.RandomHorizontalFlip(),
	transforms.RandomCrop(32, padding=4),
	transforms.ToTensor(),
	transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
	])

	# Load the CIFAR-10 dataset and apply transformations
	trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
	trainloader = torch.utils.data.DataLoader(trainset, batch_size=config['training']['batch_size'],
	shuffle=config['data']['shuffle'], num_workers=2)

	testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
	testloader = torch.utils.data.DataLoader(testset, batch_size=config['training']['batch_size'], shuffle=False,
	num_workers=2)

	# Instantiate the CNN model
	model = CNN(config['model']['num_classes']).to(device)

	# Define the loss function and optimizer
	criterion = nn.CrossEntropyLoss()
	optimizer = optim.SGD(model.parameters(), lr=config['training']['learning_rate'], momentum=0.9)

	# Training loop
	for epoch in range(config['training']['epochs']):
	running_loss = 0.0
	for i, data in enumerate(trainloader, 0):
	# Get the inputs and labels
	inputs, labels = data[0].to(device), data[1].to(device)

	# Zero the parameter gradients
	optimizer.zero_grad()

	# Forward + backward + optimize
	outputs = model(inputs)
	loss = criterion(outputs, labels)
	loss.backward()
	optimizer.step()

	# Print statistics
	running_loss += loss.item()
	if i % 200 == 199: # Print every 200 mini-batches
	print(f'[{epoch + 1}, {i + 1}] loss: {running_loss / 200:.3f}')
	running_loss = 0.0

	print("Training finished.")

	# Evaluate the model on the test set
	correct = 0
	total = 0
	with torch.no_grad():
	for data in testloader:
	images, labels = data[0].to(device), data[1].to(device)
	outputs = model(images)
	_, predicted = torch.max(outputs.data, 1)
	total += labels.size(0)
	correct += (predicted == labels).sum().item()

	print(f'Accuracy on the test set: {(100 * correct / total):.2f}%')

	<!DOCTYPE html>
	<html>
	<head>
	<title>Balloon Shop</title>
	<link rel="stylesheet" type="text/css" href="frontend.css">
	</head>
	<body>
	<h1>Welcome to the Balloon Shop!</h1>
	<img src="balloon.jpg" alt="Balloons">
	<form>
	<label for="quantity">Quantity:</label>
	<input type="number" id="quantity" name="quantity" min="1" max="10">
	<br>
	<label for="color">Color:</label>
	<select id="color" name="color">
	<option value="red">Red</option>
	<option value="blue">Blue</option>
	<option value="yellow">Yellow</option>
	</select>
	<br>
	<input type="submit" value="Order">
	</form>

Sweep: rewrite the readme.