Feat/comment bot #55
Feat/comment bot #55
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…dian calculations, and optimized diff processing for AI review, focusing on structured diff output and performance.
…view comments for enhanced tracking.
…nabling comment-specific processing, and restructured code review workflow for direct execution.
… comments, added PR number extraction, and enhanced error handling.
… comments, utilizing PR number extraction, and implemented robust error handling for enhanced reliability.
… review comment access via PyGithub and removed redundant logic.
…ments access, removed redundant logic and added print debugging.
…ads using PR comment IDs, improving accuracy and adding debug logging for detailed review comment analysis.
…R review threads via review IDs, improving accuracy and implementing debug logging.
…review threads using review IDs, improving accuracy, with added debug logging including body, id, and reply-to ID.
…review threads using comment IDs, improving accuracy, with added debug logging including user, body, and reply-to ID.
…ment IDs, bypassing review object, enhanced accuracy, and included reply ID in debug logs.
…mment IDs, circumventing review object, improving accuracy, and logging reply IDs.
…user, body and reply ID, also added child comment details for debugging.
… alongside user, body and reply ID, also added child comment details for debugging.
…k, user, body, and reply ID, and child comment details for advanced debugging and AI analysis.
… and child comment details for debugging and AI analysis.
…ent reply creation with new method and debug prints.
…ent processing for diff hunk, user, and reply details.
![github-actions[bot]](https://avatars.githubusercontent.com/in/15368?s=60&v=4){kind=small}
| @@ -57,6 +57,13 @@ def main(): | |||
| print("\nDiff content:") | |||
| print(diff_content) | |||
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| total = sum(numbers) | |||
There was a problem hiding this comment.
Finding: Consider using the statistics module for calculating median, which is more efficient and concise.
There was a problem hiding this comment.
Tentu! Berikut adalah contoh bagaimana Anda dapat menggunakan modul statistics untuk menghitung nilai tengah (median) dari daftar angka:
import statistics
numbers = [1, 2, 3, 4, 5]
# Menggunakan modul statistics untuk menghitung median
median_value = statistics.median(numbers)
print("Median:", median_value)Dalam contoh ini, statistics.median(numbers) akan mengembalikan nilai tengah dari daftar numbers, yaitu 3.
Modul statistics juga menyediakan fungsi-fungsi lain yang berguna untuk analisis statistik, seperti:
mean()untuk menghitung rata-ratamode()untuk menemukan modus (nilai yang paling sering muncul)stdev()untuk menghitung standar deviasi
Contoh penggunaan lainnya:
import statistics
data = [1, 2, 3, 4, 5, 6, 7, 8, 9]
# Menghitung rata-rata
mean_value = statistics.mean(data)
print("Mean:", mean_value)
# Menemukan modus
mode_value = statistics.mode(data)
print("Mode:", mode_value)
# Menghitung standar deviasi
stdev_value = statistics.stdev(data)
print("Standard Deviation:", stdev_value)Dengan menggunakan statistics, kode Anda akan lebih mudah dibaca dan efisien.
| @@ -45,7 +45,7 @@ def __init__(self, name, age): | |||
| def print_info(self): | |||
| print(f"Name: {self.name}") | |||
| print(f"Age: {self.age}") | |||
| print(f"Birth Year: {datetime.datetime.today().year - self.age}") # Hardcoded year | |||
| print(f"Birth Year: {2024 - 2028}") # Hardcoded year | |||
There was a problem hiding this comment.
Finding: Consider using a variable or function to dynamically calculate the birth year instead of hardcoding it.
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@corivai-review Last Processed SHA: ef6c34a |
…ent processing with direct API interaction, and configuring git user details via github actions.
…up, and centralized API key input for enhanced code review and comment processing actions.
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@corivai-review Last Processed SHA: 22cc66e |
…s for enhanced action performance.
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@corivai-review Last Processed SHA: ca03b90 |
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