Merge pull request #5 from KOSASIH/deepsource-autofix-1281c1a9

refactor: remove unused imports

Author: KOSASIH

core/disaster_recovery_system/disaster_recovery.py

@@ -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
@@ -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
 
@@ -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}")

core/gpu_acceleration/gpu_acceleration.py

@@ -1,6 +1,6 @@
-import numpy as np
 import cupy
 
+
 class GPUCAccelerator:
     def __init__(self, gpu_id):
         self.gpu_id = gpu_id
@@ -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)

core/nlp_chatbot/ai_chatbot.py

@@ -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):
@@ -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!"