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refactor: remove unused imports #5

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merged 2 commits into from
Jul 22, 2024
Merged

refactor: remove unused imports #5

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a0d450f
refactor: remove unused imports
deepsource-autofix[bot] Jul 22, 2024
5559840
style: format code with Autopep8, Black, ClangFormat, dotnet-format, …
deepsource-autofix[bot] Jul 22, 2024
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12 changes: 7 additions & 5 deletions core/disaster_recovery_system/disaster_recovery.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,8 @@
import os
import time

from google.cloud import storage


class DisasterRecoverySystem:
def __init__(self, project_id, bucket_name):
self.project_id = project_id
Expand All @@ -11,15 +12,15 @@ def __init__(self, project_id, bucket_name):
def backup_data(self, data):
# Backup data to Google Cloud Storage
bucket = self.storage_client.get_bucket(self.bucket_name)
blob = bucket.blob('backup_' + str(int(time.time())))
blob = bucket.blob("backup_" + str(int(time.time())))
blob.upload_from_string(data)

def restore_data(self):
# Restore data from Google Cloud Storage
bucket = self.storage_client.get_bucket(self.bucket_name)
blobs = bucket.list_blobs()
for blob in blobs:
if blob.name.startswith('backup_'):
if blob.name.startswith("backup_"):
data = blob.download_as_string()
return data

Expand All @@ -28,8 +29,9 @@ def replicate_data(self, data):
# Using Google Cloud's Disaster Recovery as a Service (DRaaS)
pass


# Example usage
dr_system = DisasterRecoverySystem('my_project', 'my_bucket')
dr_system.backup_data('Hello, world!')
dr_system = DisasterRecoverySystem("my_project", "my_bucket")
dr_system.backup_data("Hello, world!")
restored_data = dr_system.restore_data()
print(f"Restored data: {restored_data}")
3 changes: 2 additions & 1 deletion core/gpu_acceleration/gpu_acceleration.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import numpy as np
import cupy


class GPUCAccelerator:
def __init__(self, gpu_id):
self.gpu_id = gpu_id
Expand All @@ -18,6 +18,7 @@ def convolutional_neural_network(self, inputs, weights):
# Implement convolutional neural network using cuDNN
pass


# Example usage
accelerator = GPUCAccelerator(0)
A = cupy.random.rand(1024, 1024)
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5 changes: 3 additions & 2 deletions core/nlp_chatbot/ai_chatbot.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import nltk
from nltk.tokenize import word_tokenize
from nltk.sentiment import SentimentIntensityAnalyzer
from nltk.tokenize import word_tokenize


class AIChatbot:
def __init__(self, dataset):
Expand All @@ -26,6 +26,7 @@ def analyze_sentiment(self, message):
# Implement sentiment analysis using sentiment intensity analysis
pass


# Example usage
chatbot = AIChatbot(" dataset.csv")
message = "I love this product!"
Expand Down