lukejagg / test-canary

Checklist

README.md

• Add a brief overview of the project at the beginning of the file. This should include the purpose of the project and a high-level description of how it achieves this purpose.
• Add a section titled "Installation and Usage". In this section, provide instructions on how to install and use the project. This should include any prerequisites, such as required software or hardware, as well as step-by-step instructions on how to run the project.
• Add a section titled "File Descriptions". In this section, describe each of the main files in the project. For each file, provide a brief description of its purpose and how it contributes to the overall project.
• Add a section titled "Known Issues and Limitations". In this section, list any known issues or limitations with the project.
• Add a section titled "Contributing". In this section, provide guidelines on how others can contribute to the project. This could include instructions on how to submit issues, propose changes, or contribute code.

Here's the PR! #271.

💎 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/frontend.css

Lines 1 to 34 in d773bfa

    
           /* Add CSS styles to target specific elements in frontend.html */ 
        
           /* Style the heading */ 
        
           h1 { 
        
               color: blue; 
        
               font-size: 24px; 
        
               margin-bottom: 20px; 
        
           } 
        
           /* Style the image */ 
        
           img { 
        
               width: 200px; 
        
               height: auto; 
        
               margin-bottom: 20px; 
        
           } 
        
           /* Style the form */ 
        
           form { 
        
               margin-bottom: 20px; 
        
           } 
        
           /* Style the input fields */ 
        
           input[type="number"], 
        
           select { 
        
               padding: 5px; 
        
               margin-bottom: 10px; 
        
           } 
        
           /* Style the submit button */ 
        
           input[type="submit"] { 
        
               background-color: blue; 
        
               color: white; 
        
               padding: 10px; 
        
               border: none;

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/calculator.py

Lines 1 to 59 in d773bfa

    
           def add(x, y): 
        
               return x + y 
        
           def subtract(x, y): 
        
               return x - y 
        
           def multiply(x, y): 
        
               return x * y 
        
           def divide(x, y): 
        
               if y == 0: 
        
                   raise ValueError("Cannot divide by zero!") 
        
               return x / y 
        
           def calculator(): 
        
               print("Welcome to the Interactive Calculator!") 
        
               while True: 
        
                   print("Please select an operation:") 
        
                   print("1. Add") 
        
                   print("2. Subtract") 
        
                   print("3. Multiply") 
        
                   print("4. Divide") 
        
                   print("5. Quit") 
        
                   choice = input("Enter your choice (1-5): ") 
        
                   if choice == '5': 
        
                       print("Thank you for using the calculator. Goodbye!") 
        
                       break 
        
                   try: 
        
                       num1 = float(input("Enter the first number: ")) 
        
                       num2 = float(input("Enter the second number: ")) 
        
                   except ValueError: 
        
                       print("Invalid input. Please enter numeric values.") 
        
                       continue 
        
                   if choice == '1': 
        
                       result = add(num1, num2) 
        
                       print(f"Result: {num1} + {num2} = {result}") 
        
                   elif choice == '2': 
        
                       result = subtract(num1, num2) 
        
                       print(f"Result: {num1} - {num2} = {result}") 
        
                   elif choice == '3': 
        
                       result = multiply(num1, num2) 
        
                       print(f"Result: {num1} * {num2} = {result}") 
        
                   elif choice == '4': 
        
                       try: 
        
                           result = divide(num1, num2) 
        
                           print(f"Result: {num1} / {num2} = {result}") 
        
                       except ValueError as e: 
        
                           print(str(e)) 
        
                   else: 
        
                       print("Invalid choice. Please select a valid operation.") 
        
                   print()  # Print an empty line for readability 
        
           calculator()

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: • Add a brief overview of the project at the beginning of the file. This should include the purpose of the project and a high-level description of how it achieves this purpose. • Add a section titled "Installation and Usage". In this section, provide instructions on how to install and use the project. This should include any prerequisites, such as required software or hardware, as well as step-by-step instructions on how to run the project. • Add a section titled "File Descriptions". In this section, describe each of the main files in the project. For each file, provide a brief description of its purpose and how it contributes to the overall project. • Add a section titled "Known Issues and Limitations". In this section, list any known issues or limitations with the project. • Add a section titled "Contributing". In this section, provide guidelines on how others can contribute to the project. This could include instructions on how to submit issues, propose changes, or contribute code.

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:

Improve README.md with comprehensive project documentation
sweep/improve-readme-documentation

Description

This PR aims to improve the README.md file by providing comprehensive project documentation. The current README.md file lacks important information about the project, its usage, installation instructions, file descriptions, known issues, and contribution guidelines. This PR addresses these issues by adding the following sections to the README.md file:

Overview

A brief overview of the project is provided, including its purpose and a high-level description of how it achieves this purpose.

Installation and Usage

Instructions on how to install and use the project are included. This section includes any prerequisites, such as required software or hardware, as well as step-by-step instructions on how to run the project.

File Descriptions

Each of the main files in the project is described, including a brief description of its purpose and how it contributes to the overall project.

Known Issues and Limitations

Any known issues or limitations with the project are clearly stated in this section.

Contributing

Guidelines on how others can contribute to the project are provided. This includes instructions on how to submit issues, propose changes, or contribute code.

Summary

This PR improves the README.md file by adding comprehensive project documentation. It provides an overview of the project, installation and usage instructions, file descriptions, known issues and limitations, and contribution guidelines. These changes will make it easier for other developers to understand, use, and contribute to the project.

Step 4: ⌨️ Coding

File	Instructions	Progress	Error logs
`README.md`	Modify README.md with contents: • Add a brief overview of the project at the beginning of the file. This should include the purpose of the project and a high-level description of how it achieves this purpose. • Add a section titled "Installation and Usage". In this section, provide instructions on how to install and use the project. This should include any prerequisites, such as required software or hardware, as well as step-by-step instructions on how to run the project. • Add a section titled "File Descriptions". In this section, describe each of the main files in the project. For each file, provide a brief description of its purpose and how it contributes to the overall project. • Add a section titled "Known Issues and Limitations". In this section, list any known issues or limitations with the project. • Add a section titled "Contributing". In this section, provide guidelines on how others can contribute to the project. This could include instructions on how to submit issues, propose changes, or contribute code.	✅ Commit `5e374a2`	No errors.

Step 5: 🔁 Code Review

Here are my self-reviews of my changes at sweep/improve-readme-documentation.

Here is the 1st review

No changes required. The updates to the README.md file are comprehensive and well-written, providing a clear and detailed description of the project, its usage, and contribution guidelines. Great job!

I finished incorporating these changes.

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We released a demo of our chunker, where you can find the corresponding blog and code.

💡 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

	/* Add CSS styles to target specific elements in frontend.html */

	/* Style the heading */
	h1 {
	color: blue;
	font-size: 24px;
	margin-bottom: 20px;
	}

	/* Style the image */
	img {
	width: 200px;
	height: auto;
	margin-bottom: 20px;
	}

	/* Style the form */
	form {
	margin-bottom: 20px;
	}

	/* Style the input fields */
	input[type="number"],
	select {
	padding: 5px;
	margin-bottom: 10px;
	}

	/* Style the submit button */
	input[type="submit"] {
	background-color: blue;
	color: white;
	padding: 10px;
	border: none;

	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}%')

	def add(x, y):
	return x + y

	def subtract(x, y):
	return x - y

	def multiply(x, y):
	return x * y

	def divide(x, y):
	if y == 0:
	raise ValueError("Cannot divide by zero!")
	return x / y

	def calculator():
	print("Welcome to the Interactive Calculator!")

	while True:
	print("Please select an operation:")
	print("1. Add")
	print("2. Subtract")
	print("3. Multiply")
	print("4. Divide")
	print("5. Quit")

	choice = input("Enter your choice (1-5): ")

	if choice == '5':
	print("Thank you for using the calculator. Goodbye!")
	break

	try:
	num1 = float(input("Enter the first number: "))
	num2 = float(input("Enter the second number: "))
	except ValueError:
	print("Invalid input. Please enter numeric values.")
	continue

	if choice == '1':
	result = add(num1, num2)
	print(f"Result: {num1} + {num2} = {result}")
	elif choice == '2':
	result = subtract(num1, num2)
	print(f"Result: {num1} - {num2} = {result}")
	elif choice == '3':
	result = multiply(num1, num2)
	print(f"Result: {num1} * {num2} = {result}")
	elif choice == '4':
	try:
	result = divide(num1, num2)
	print(f"Result: {num1} / {num2} = {result}")
	except ValueError as e:
	print(str(e))
	else:
	print("Invalid choice. Please select a valid operation.")

	print() # Print an empty line for readability

	calculator()

sweep: improve readme even more

Here's the PR! #271.

Step 1: 🔍 Code Search

Step 2: 🧐 Snippet Analysis

Step 3: 📝 Planning

Description

Overview

Installation and Usage

File Descriptions

Known Issues and Limitations

Contributing

Summary

Step 4: ⌨️ Coding

Step 5: 🔁 Code Review