如何使用AI生成Git提交消息？-DEV365 开发者社区

为什么不使用可用解决方案？所有人都使用chatgpt。但是我没有信用;）
我当然想学习一些东西！

如何生成git提交消息？

git允许您创建钩子。让我们使用全球一个。全局钩工作而无需修改每个git repo。

为钩子创建一个目录：

$ mkdir ~/.config/git/hooks/

让git知道钩子在哪里：

$ git config core.hooksPath ~/.config/git/hooks/

长话短说，prepare-commit-msg是我们需要的。我们需要更新的文件作为第一个参数传递。
创建一个简单的脚本：

#!/bin/sh

echo "Fancy commit message" > $1

使其可执行：

$ chmod +z ~/.confog/git/hooks/prepare-commit-msg

它有效吗？让我们提出一些事情...是的，我们在提交消息的末尾有一条消息。

让我们生成一些东西：

生成提交消息

让我们脱机地构建一些有效的东西。 AI？是的，让我们使用AI！

我们需要模型吗？

让我们看一下huggingface！

是：https://huggingface.co/mamiksik/T5-commit-message-generation，但没有文档:(
但是，如果您看起来更深，您会发现https://huggingface.co/spaces/mamiksik/commit-message-generator

我们可以使用一些修改的https://huggingface.co/spaces/mamiksik/commit-message-generator/blob/main/app.py。

我们可以在钩中使用任何shell脚本，让我们使用python。

让我们看看那里有什么：

import re

import gradio as gr
import torch
from transformers import T5ForConditionalGeneration, RobertaTokenizer


tokenizer = RobertaTokenizer.from_pretrained("mamiksik/CommitPredictorT5PL", revision="fb08d01")
model = T5ForConditionalGeneration.from_pretrained("mamiksik/CommitPredictorT5PL", revision="fb08d01")

def parse_files(patch):
    accumulator = []
    lines = patch.splitlines()

    filename_before = None
    for line in lines:
        if line.startswith("index") or line.startswith("diff"):
            continue
        if line.startswith("---"):
            filename_before = line.split(" ", 1)[1][1:]
            continue

        if line.startswith("+++"):
            filename_after = line.split(" ", 1)[1][1:]

            if filename_before == filename_after:
                accumulator.append(f"<ide><path>{filename_before}")
            else:
                accumulator.append(f"<add><path>{filename_after}")
                accumulator.append(f"<del><path>{filename_before}")
            continue

        line = re.sub("@@[^@@]*@@", "", line)
        if len(line) == 0:
            continue

        if line[0] == "+":
            line = line.replace("+", "<add>", 1)
        elif line[0] == "-":
            line = line.replace("-", "<del>", 1)
        else:
            line = f"<ide>{line}"

        accumulator.append(line)

    return '\n'.join(accumulator)


def predict(patch, max_length, min_length, num_beams, prediction_count):
    input_text = parse_files(patch)
    with torch.no_grad():
        token_count = tokenizer(input_text, return_tensors="pt").input_ids.shape[1]

        input_ids = tokenizer(
            input_text,
            truncation=True,
            padding=True,
            return_tensors="pt",
        ).input_ids

        outputs = model.generate(
            input_ids,
            max_length=max_length,
            min_length=min_length,
            num_beams=num_beams,
            num_return_sequences=prediction_count,
        )

    result = tokenizer.batch_decode(outputs, skip_special_tokens=True)
    return token_count, input_text, {k: 0 for k in result}


iface = gr.Interface(fn=predict, inputs=[
    gr.Textbox(label="Patch (as generated by git diff)"),
    gr.Slider(1, 128, value=40, label="Max message length"),
    gr.Slider(1, 128, value=5, label="Min message length"),
    gr.Slider(1, 10, value=7, label="Number of beams"),
    gr.Slider(1, 15, value=5, label="Number of predictions"),
], outputs=[
    gr.Textbox(label="Token count"),
    gr.Textbox(label="Parsed patch"),
    gr.Label(label="Predictions")
], examples=[
["""
diff --git a/.github/workflows/pylint.yml b/.github/workflows/codestyle_checks.yml
similarity index 86%
rename from .github/workflows/pylint.yml
rename to .github/workflows/codestyle_checks.yml
index a5d5c4d9..8cbf9713 100644
--- a/.github/workflows/pylint.yml
+++ b/.github/workflows/codestyle_checks.yml
@@ -20,3 +20,6 @@ jobs:
     - name: Analysing the code with pylint
       run: |
         pylint --rcfile=.pylintrc webapp core
+    - name: Analysing the code with flake8
+      run: |
+        flake8
""", 40, 5, 7, 5]
]
)

if __name__ == "__main__":
    iface.launch()

我们需要的一切都在这里！我们需要：

获取gitmessage文件以更新
提取git diff
使用当前脚本进行预测
预先启动提交消息到gitmessage文件

我们需要更新的文件作为第一个参数，因此

import sys

sys.argv[1]

呵呵，这很容易。

获取git差异

import subprocess

subprocess.run(['git', 'diff', '--cached'], capture_output=True).stdout.decode('utf-8')

简单的peasy！

使用当前脚本进行预测

max_message = 40
min_message = 5
num_beams = 10
num_predictions = 1

msg = predict(diff, max_message, min_message, num_beams, num_predictions)

将我们的消息预先发送到gitmessage文件

with open(sys.argv[1], 'r+') as f:
    content = f.read()
    f.seek(0)
    f.write(msg + '\n' + content)
    f.close()

就是这样。几乎没有清理，这是我们的最终脚本。

#!/usr/bin/env python
print("Generating commit message", end="", flush=True)

import sys
import re
import subprocess
import torch
from transformers import T5ForConditionalGeneration, RobertaTokenizer

def parse_files(patch):
    accumulator = []
    lines = patch.splitlines()

    filename_before = None
    for line in lines:
        print(".", end="", flush=True)
        if line.startswith("index") or line.startswith("diff"):
            continue
        if line.startswith("---"):
            filename_before = line.split(" ", 1)[1][1:]
            continue

        if line.startswith("+++"):
            filename_after = line.split(" ", 1)[1][1:]

            if filename_before == filename_after:
                accumulator.append(f"<ide><path>{filename_before}")
            else:
                accumulator.append(f"<add><path>{filename_after}")
                accumulator.append(f"<del><path>{filename_before}")
            continue

        line = re.sub("@@[^@@]*@@", "", line)
        if len(line) == 0:
            continue

        if line[0] == "+":
            line = line.replace("+", "<add>", 1)
        elif line[0] == "-":
            line = line.replace("-", "<del>", 1)
        else:
            line = f"<ide>{line}"

        accumulator.append(line)

    return '\n'.join(accumulator)

def predict(patch, max_length, min_length, num_beams, prediction_count):
    print(".", end="", flush=True)
    input_text = parse_files(patch)

    tokenizer = RobertaTokenizer.from_pretrained("mamiksik/CommitPredictorT5PL", revision="fb08d01", low_cpu_mem_usage=True)
    print(".", end="", flush=True)
    model = T5ForConditionalGeneration.from_pretrained("mamiksik/CommitPredictorT5PL", revision="fb08d01", low_cpu_mem_usage=True)
    print(".", end="", flush=True)

    with torch.no_grad():
        input_ids = tokenizer(
            input_text,
            truncation=True,
            padding=True,
            return_tensors="pt",
        ).input_ids
        print(".", end="", flush=True)
        outputs = model.generate(
            input_ids,
            max_length=max_length,
            min_length=min_length,
            num_beams=num_beams,
            num_return_sequences=prediction_count,
        )
        print(".", end="", flush=True)

    result = tokenizer.batch_decode(outputs, skip_special_tokens=True)
    return result[0]

if __name__ == "__main__":
    diff = subprocess.run(['git', 'diff', '--cached'], capture_output=True).stdout.decode('utf-8')

    max_message = 40
    min_message = 5
    num_beams = 10
    num_predictions = 1

    msg = predict(diff, max_message, min_message, num_beams, num_predictions)

    with open(sys.argv[1], 'r+') as f:
        content = f.read()
        f.seek(0)
        f.write(msg + '\n' + content)
        f.close()

    print("Done!\n")

它在CPU上很快，但是加载模型需要很多次。无论如何3s还可以。
就这样。有用。至少对我而言。