import copy import json import logging from typing import List, Tuple 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 load_yaml from pr_agent.config_loader import get_settings from pr_agent.git_providers import get_git_provider from pr_agent.git_providers.git_provider import get_main_pr_language class PRDescription: def __init__(self, pr_url: str, args: list = None): """ Initialize the PRDescription object with the necessary attributes and objects for generating a PR description using an AI model. Args: pr_url (str): The URL of the pull request. args (list, optional): List of arguments passed to the PRDescription class. Defaults to None. """ # Initialize the git provider and main PR language self.git_provider = get_git_provider()(pr_url) self.main_pr_language = get_main_pr_language( self.git_provider.get_languages(), self.git_provider.get_files() ) # Initialize the AI handler self.ai_handler = AiHandler() # Initialize the variables dictionary self.vars = { "title": self.git_provider.pr.title, "branch": self.git_provider.get_pr_branch(), "description": self.git_provider.get_pr_description(full=False), "language": self.main_pr_language, "diff": "", # empty diff for initial calculation "extra_instructions": get_settings().pr_description.extra_instructions, "commit_messages_str": self.git_provider.get_commit_messages() } self.user_description = self.git_provider.get_user_description() # Initialize the token handler self.token_handler = TokenHandler( self.git_provider.pr, self.vars, get_settings().pr_description_prompt.system, get_settings().pr_description_prompt.user, ) # Initialize patches_diff and prediction attributes self.patches_diff = None self.prediction = None async def run(self): """ Generates a PR description using an AI model and publishes it to the PR. """ logging.info('Generating a PR description...') if get_settings().config.publish_output: self.git_provider.publish_comment("Preparing pr description...", is_temporary=True) await retry_with_fallback_models(self._prepare_prediction) logging.info('Preparing answer...') pr_title, pr_body, pr_types, markdown_text, description = self._prepare_pr_answer() if get_settings().config.publish_output: logging.info('Pushing answer...') if get_settings().pr_description.publish_description_as_comment: self.git_provider.publish_comment(pr_body) else: self.git_provider.publish_description(pr_title, pr_body) if self.git_provider.is_supported("get_labels"): current_labels = self.git_provider.get_labels() if current_labels is None: current_labels = [] self.git_provider.publish_labels(pr_types + current_labels) self.git_provider.remove_initial_comment() return "" async def _prepare_prediction(self, model: str) -> None: """ Prepare the AI prediction for the PR description based on the provided model. Args: model (str): The name of the model to be used for generating the prediction. Returns: None Raises: Any exceptions raised by the 'get_pr_diff' and '_get_prediction' functions. """ 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 PR description based on the provided model. Args: model (str): The name of the model to be used for generating the prediction. Returns: str: The generated AI prediction. """ variables = copy.deepcopy(self.vars) variables["diff"] = self.patches_diff # update diff environment = Environment(undefined=StrictUndefined) system_prompt = environment.from_string(get_settings().pr_description_prompt.system).render(variables) user_prompt = environment.from_string(get_settings().pr_description_prompt.user).render(variables) if get_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_answer(self) -> Tuple[str, str, List[str], str]: """ Prepare the PR description based on the AI prediction data. Returns: - title: a string containing the PR title. - pr_body: a string containing the PR body in a markdown format. - pr_types: a list of strings containing the PR types. - markdown_text: a string containing the AI prediction data in a markdown format. used for publishing a comment - user_description: a string containing the user description """ # Load the AI prediction data into a dictionary data = load_yaml(self.prediction.strip()) if get_settings().pr_description.add_original_user_description and self.user_description: data["User Description"] = self.user_description # Initialization pr_types = [] # If the 'PR Type' key is present in the dictionary, split its value by comma and assign it to 'pr_types' if 'PR Type' in data: if type(data['PR Type']) == list: pr_types = data['PR Type'] elif type(data['PR Type']) == str: pr_types = data['PR Type'].split(',') # Remove the 'PR Title' key from the dictionary ai_title = data.pop('PR Title') if get_settings().pr_description.keep_original_user_title: # Assign the original PR title to the 'title' variable title = self.vars["title"] else: # Assign the value of the 'PR Title' key to 'title' variable title = ai_title # Iterate over the remaining dictionary items and append the key and value to 'pr_body' in a markdown format, # except for the items containing the word 'walkthrough' pr_body = "" for idx, (key, value) in enumerate(data.items()): pr_body += f"## {key}:\n" if 'walkthrough' in key.lower(): # for filename, description in value.items(): for file in value: filename = file['filename'].replace("'", "`") description = file['changes in file'] pr_body += f'`{filename}`: {description}\n' else: # if the value is a list, join its items by comma if type(value) == list: value = ', '.join(v for v in value) pr_body += f"{value}\n" if idx < len(data) - 1: pr_body += "\n___\n" markdown_text = f"## Title\n\n{title}\n\n___\n{pr_body}" description = data['PR Description'] if get_settings().config.verbosity_level >= 2: logging.info(f"title:\n{title}\n{pr_body}") return title, pr_body, pr_types, markdown_text, description