pr-agent/pr_agent/tools/pr_reviewer.py

import copy
import json
import logging
from collections import OrderedDict
from typing import Tuple, List

from jinja2 import Environment, StrictUndefined

from pr_agent.algo.ai_handler import AiHandler
from pr_agent.algo.pr_processing import get_pr_diff, retry_with_fallback_models
from pr_agent.algo.token_handler import TokenHandler
from pr_agent.algo.utils import convert_to_markdown, try_fix_json
from pr_agent.config_loader import settings
from pr_agent.git_providers import get_git_provider
from pr_agent.git_providers.git_provider import get_main_pr_language, IncrementalPR
from pr_agent.servers.help import actions_help_text, bot_help_text


class PRReviewer:
    """
    The PRReviewer class is responsible for reviewing a pull request and generating feedback using an AI model.
    """
    def __init__(self, pr_url: str, cli_mode: bool = False, is_answer: bool = False, args: list = None):
        """
        Initialize the PRReviewer object with the necessary attributes and objects to review a pull request.

        Args:
            pr_url (str): The URL of the pull request to be reviewed.
            cli_mode (bool, optional): Indicates whether the review is being done in command-line interface mode. Defaults to False.
            is_answer (bool, optional): Indicates whether the review is being done in answer mode. Defaults to False.
            args (list, optional): List of arguments passed to the PRReviewer class. Defaults to None.
        """
        self.parse_args(args)

        self.git_provider = get_git_provider()(pr_url, incremental=self.incremental)
        self.main_language = get_main_pr_language(
            self.git_provider.get_languages(), self.git_provider.get_files()
        )
        self.pr_url = pr_url
        self.is_answer = is_answer

        if self.is_answer and not self.git_provider.is_supported("get_issue_comments"):
            raise Exception(f"Answer mode is not supported for {settings.config.git_provider} for now")
        self.ai_handler = AiHandler()
        self.patches_diff = None
        self.prediction = None
        self.cli_mode = cli_mode

        answer_str, question_str = self._get_user_answers()
        self.vars = {
            "title": self.git_provider.pr.title,
            "branch": self.git_provider.get_pr_branch(),
            "description": self.git_provider.get_pr_description(),
            "language": self.main_language,
            "diff": "",  # empty diff for initial calculation
            "require_score": settings.pr_reviewer.require_score_review,
            "require_tests": settings.pr_reviewer.require_tests_review,
            "require_security": settings.pr_reviewer.require_security_review,
            "require_focused": settings.pr_reviewer.require_focused_review,
            'num_code_suggestions': settings.pr_reviewer.num_code_suggestions,
            'question_str': question_str,
            'answer_str': answer_str,
        }

        self.token_handler = TokenHandler(
            self.git_provider.pr,
            self.vars,
            settings.pr_review_prompt.system,
            settings.pr_review_prompt.user
        )

    def parse_args(self, args: List[str]) -> None:
        """
        Parse the arguments passed to the PRReviewer class and set the 'incremental' attribute accordingly.

        Args:
            args: A list of arguments passed to the PRReviewer class.

        Returns:
            None
        """
        is_incremental = False
        if args and len(args) >= 1:
            arg = args[0]
            if arg == "-i":
                is_incremental = True
        self.incremental = IncrementalPR(is_incremental)

    async def review(self) -> None:
        """
        Review the pull request and generate feedback.
        """
        logging.info('Reviewing PR...')
    
        if settings.config.publish_output:
            self.git_provider.publish_comment("Preparing review...", is_temporary=True)
    
        await retry_with_fallback_models(self._prepare_prediction)
    
        logging.info('Preparing PR review...')
        pr_comment = self._prepare_pr_review()
    
        if settings.config.publish_output:
            logging.info('Pushing PR review...')
            self.git_provider.publish_comment(pr_comment)
            self.git_provider.remove_initial_comment()
        
            if settings.pr_reviewer.inline_code_comments:
                logging.info('Pushing inline code comments...')
                self._publish_inline_code_comments()

    async def _prepare_prediction(self, model: str) -> None:
        """
        Prepare the AI prediction for the pull request review.

        Args:
            model: A string representing the AI model to be used for the prediction.

        Returns:
            None
        """
        logging.info('Getting PR diff...')
        self.patches_diff = get_pr_diff(self.git_provider, self.token_handler, model)
        logging.info('Getting AI prediction...')
        self.prediction = await self._get_prediction(model)

    async def _get_prediction(self, model: str) -> str:
        """
        Generate an AI prediction for the pull request review.

        Args:
            model: A string representing the AI model to be used for the prediction.

        Returns:
            A string representing the AI prediction for the pull request review.
        """
        variables = copy.deepcopy(self.vars)
        variables["diff"] = self.patches_diff  # update diff

        environment = Environment(undefined=StrictUndefined)
        system_prompt = environment.from_string(settings.pr_review_prompt.system).render(variables)
        user_prompt = environment.from_string(settings.pr_review_prompt.user).render(variables)

        if settings.config.verbosity_level >= 2:
            logging.info(f"\nSystem prompt:\n{system_prompt}")
            logging.info(f"\nUser prompt:\n{user_prompt}")

        response, finish_reason = await self.ai_handler.chat_completion(
            model=model,
            temperature=0.2,
            system=system_prompt,
            user=user_prompt
        )

        return response

    def _prepare_pr_review(self) -> str:
        """
        Prepare the PR review by processing the AI prediction and generating a markdown-formatted text that summarizes the feedback.
        """
        review = self.prediction.strip()
    
        try:
            data = json.loads(review)
        except json.decoder.JSONDecodeError:
            data = try_fix_json(review)

        # Move 'Security concerns' key to 'PR Analysis' section for better display
        if 'PR Feedback' in data and 'Security concerns' in data['PR Feedback']:
            val = data['PR Feedback']['Security concerns']
            del data['PR Feedback']['Security concerns']
            data['PR Analysis']['Security concerns'] = val

        # Filter out code suggestions that can be submitted as inline comments
        if settings.config.git_provider != 'bitbucket' and settings.pr_reviewer.inline_code_comments and 'Code suggestions' in data['PR Feedback']:
            data['PR Feedback']['Code suggestions'] = [
                d for d in data['PR Feedback']['Code suggestions']
                if any(key not in d for key in ('relevant file', 'relevant line in file', 'suggestion content'))
            ]
            if not data['PR Feedback']['Code suggestions']:
                del data['PR Feedback']['Code suggestions']

        # Add incremental review section
        if self.incremental.is_incremental:
            last_commit_url = f"{self.git_provider.get_pr_url()}/commits/{self.git_provider.incremental.first_new_commit_sha}"
            data = OrderedDict(data)
            data.update({'Incremental PR Review': {
                "⏮️ Review for commits since previous PR-Agent review": f"Starting from commit {last_commit_url}"}})
            data.move_to_end('Incremental PR Review', last=False)

        markdown_text = convert_to_markdown(data)
        user = self.git_provider.get_user_id()

        # Add help text if not in CLI mode
        if not self.cli_mode:
            markdown_text += "\n### How to use\n"
            if user and '[bot]' not in user:
                markdown_text += bot_help_text(user)
            else:
                markdown_text += actions_help_text

        # Log markdown response if verbosity level is high
        if settings.config.verbosity_level >= 2:
            logging.info(f"Markdown response:\n{markdown_text}")
    
        return markdown_text

    def _publish_inline_code_comments(self) -> None:
        """
        Publishes inline comments on a pull request with code suggestions generated by the AI model.
        """
        if settings.pr_reviewer.num_code_suggestions == 0:
            return

        review = self.prediction.strip()
        try:
            data = json.loads(review)
        except json.decoder.JSONDecodeError:
            data = try_fix_json(review)

        comments: List[str] = []
        for suggestion in data.get('PR Feedback', {}).get('Code suggestions', []):
            relevant_file = suggestion.get('relevant file', '').strip()
            relevant_line_in_file = suggestion.get('relevant line in file', '').strip()
            content = suggestion.get('suggestion content', '')
            if not relevant_file or not relevant_line_in_file or not content:
                logging.info("Skipping inline comment with missing file/line/content")
                continue

            if self.git_provider.is_supported("create_inline_comment"):
                comment = self.git_provider.create_inline_comment(content, relevant_file, relevant_line_in_file)
                if comment:
                    comments.append(comment)
            else:
                self.git_provider.publish_inline_comment(content, relevant_file, relevant_line_in_file)

        if comments:
            self.git_provider.publish_inline_comments(comments)

    def _get_user_answers(self) -> Tuple[str, str]:
        """
        Retrieves the question and answer strings from the discussion messages related to a pull request.

        Returns:
            A tuple containing the question and answer strings.
        """
        question_str = ""
        answer_str = ""

        if self.is_answer:
            discussion_messages = self.git_provider.get_issue_comments()

            for message in reversed(discussion_messages):
                if "Questions to better understand the PR:" in message.body:
                    question_str = message.body
                elif '/answer' in message.body:
                    answer_str = message.body

                if answer_str and question_str:
                    break

        return question_str, answer_str