martin/linkedIn_auto_jobs_applier_with_AI
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cover letter fixed

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This commit is contained in:
feder-cr 2024-09-03 16:35:28 +02:00
parent 165a6150f8
commit 6d72788f80
3 changed files with 10 additions and 39 deletions
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1
.gitignore vendored
View File

@ -11,3 +11,4 @@ generated_cv*
.vscode .vscode
chrome_profile chrome_profile
answers.json answers.json
data*

36
src/linkedIn_easy_applier.py
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@ -30,7 +30,6 @@ class LinkedInEasyApplier:
self.resume_generator_manager = resume_generator_manager self.resume_generator_manager = resume_generator_manager
self.all_data = self._load_questions_from_json() self.all_data = self._load_questions_from_json()
def _load_questions_from_json(self) -> List[dict]: def _load_questions_from_json(self) -> List[dict]:
output_file = 'answers.json' output_file = 'answers.json'
try: try:
@ -49,7 +48,6 @@ class LinkedInEasyApplier:
tb_str = traceback.format_exc() tb_str = traceback.format_exc()
raise Exception(f"Error loading questions data from JSON file: \nTraceback:\n{tb_str}") raise Exception(f"Error loading questions data from JSON file: \nTraceback:\n{tb_str}")
def job_apply(self, job: Any): def job_apply(self, job: Any):
self.driver.get(job.link) self.driver.get(job.link)
time.sleep(random.uniform(3, 5)) time.sleep(random.uniform(3, 5))
@ -91,7 +89,6 @@ class LinkedInEasyApplier:
attempt += 1 attempt += 1
raise Exception("No clickable 'Easy Apply' button found") raise Exception("No clickable 'Easy Apply' button found")
def _get_job_description(self) -> str: def _get_job_description(self) -> str:
try: try:
see_more_button = self.driver.find_element(By.XPATH, '//button[@aria-label="Click to see more description"]') see_more_button = self.driver.find_element(By.XPATH, '//button[@aria-label="Click to see more description"]')
@ -107,7 +104,6 @@ class LinkedInEasyApplier:
tb_str = traceback.format_exc() tb_str = traceback.format_exc()
raise Exception(f"Error getting Job description: \nTraceback:\n{tb_str}") raise Exception(f"Error getting Job description: \nTraceback:\n{tb_str}")
def _get_job_recruiter(self): def _get_job_recruiter(self):
try: try:
hiring_team_section = WebDriverWait(self.driver, 10).until( hiring_team_section = WebDriverWait(self.driver, 10).until(
@ -253,16 +249,12 @@ class LinkedInEasyApplier:
if radios: if radios:
question_text = section.text.lower() question_text = section.text.lower()
options = [radio.text.lower() for radio in radios] options = [radio.text.lower() for radio in radios]
existing_answer = None existing_answer = None
for item in self.all_data: for item in self.all_data:
if self._sanitize_text(question_text) in item['question'] and item['type'] == 'radio': if self._sanitize_text(question_text) in item['question'] and item['type'] == 'radio':
existing_answer = item existing_answer = item
break self._select_radio(radios, existing_answer['answer'])
if existing_answer: return True
self._select_radio(radios, existing_answer['answer'])
return True
answer = self.gpt_answerer.answer_question_from_options(question_text, options) answer = self.gpt_answerer.answer_question_from_options(question_text, options)
self._save_questions_to_json({'type': 'radio', 'question': question_text, 'answer': answer}) self._save_questions_to_json({'type': 'radio', 'question': question_text, 'answer': answer})
self._select_radio(radios, answer) self._select_radio(radios, answer)
@ -283,12 +275,10 @@ class LinkedInEasyApplier:
answer = self.gpt_answerer.answer_question_textual_wide_range(question_text) answer = self.gpt_answerer.answer_question_textual_wide_range(question_text)
existing_answer = None existing_answer = None
for item in self.all_data: for item in self.all_data:
if item['question'] == self._sanitize_text(question_text) and item['type'] == question_type: if 'cover' not in item['question'] and item['question'] == self._sanitize_text(question_text) and item['type'] == question_type:
existing_answer = item existing_answer = item
break self._enter_text(text_field, existing_answer['answer'])
if existing_answer: return True
self._enter_text(text_field, existing_answer['answer'])
return True
self._save_questions_to_json({'type': question_type, 'question': question_text, 'answer': answer}) self._save_questions_to_json({'type': question_type, 'question': question_text, 'answer': answer})
self._enter_text(text_field, answer) self._enter_text(text_field, answer)
return True return True
@ -302,15 +292,12 @@ class LinkedInEasyApplier:
answer_date = self.gpt_answerer.answer_question_date() answer_date = self.gpt_answerer.answer_question_date()
answer_text = answer_date.strftime("%Y-%m-%d") answer_text = answer_date.strftime("%Y-%m-%d")
existing_answer = None existing_answer = None
for item in self.all_data: for item in self.all_data:
if self._sanitize_text(question_text) in item['question'] and item['type'] == 'date': if self._sanitize_text(question_text) in item['question'] and item['type'] == 'date':
existing_answer = item existing_answer = item
break self._enter_text(date_field, existing_answer['answer'])
if existing_answer: return True
self._enter_text(date_field, existing_answer['answer'])
return True
self._save_questions_to_json({'type': 'date', 'question': question_text, 'answer': answer_text}) self._save_questions_to_json({'type': 'date', 'question': question_text, 'answer': answer_text})
self._enter_text(date_field, answer_text) self._enter_text(date_field, answer_text)
@ -325,16 +312,12 @@ class LinkedInEasyApplier:
if dropdown: if dropdown:
select = Select(dropdown) select = Select(dropdown)
options = [option.text for option in select.options] options = [option.text for option in select.options]
existing_answer = None existing_answer = None
for item in self.all_data: for item in self.all_data:
if self._sanitize_text(question_text) in item['question'] and item['type'] == 'dropdown': if self._sanitize_text(question_text) in item['question'] and item['type'] == 'dropdown':
existing_answer = item existing_answer = item
break self._select_dropdown_option(dropdown, existing_answer['answer'])
if existing_answer: return True
self._select_dropdown_option(dropdown, existing_answer['answer'])
return True
answer = self.gpt_answerer.answer_question_from_options(question_text, options) answer = self.gpt_answerer.answer_question_from_options(question_text, options)
self._save_questions_to_json({'type': 'dropdown', 'question': question_text, 'answer': answer}) self._save_questions_to_json({'type': 'dropdown', 'question': question_text, 'answer': answer})
self._select_dropdown_option(dropdown, answer) self._select_dropdown_option(dropdown, answer)
@ -385,7 +368,6 @@ class LinkedInEasyApplier:
tb_str = traceback.format_exc() tb_str = traceback.format_exc()
raise Exception(f"Error saving questions data to JSON file: \nTraceback:\n{tb_str}") raise Exception(f"Error saving questions data to JSON file: \nTraceback:\n{tb_str}")
def _sanitize_text(self, text: str) -> str: def _sanitize_text(self, text: str) -> str:
sanitized_text = text.lower() sanitized_text = text.lower()
sanitized_text = sanitized_text.strip() sanitized_text = sanitized_text.strip()

12
src/linkedIn_job_manager.py
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@ -53,18 +53,6 @@ class LinkedInJobManager:
def set_resume_generator_manager(self, resume_generator_manager): def set_resume_generator_manager(self, resume_generator_manager):
self.resume_generator_manager = resume_generator_manager self.resume_generator_manager = resume_generator_manager
""" def old_question(self):
self.set_old_answers = {}
file_path = 'data_folder/output/old_Questions.csv'
if os.path.exists(file_path):
with open(file_path, 'r', newline='', encoding='utf-8', errors='ignore') as file:
csv_reader = csv.reader(file, delimiter=',', quotechar='"')
for row in csv_reader:
if len(row) == 3:
answer_type, question_text, answer = row
self.set_old_answers[(answer_type.lower(), question_text.lower())] = answer"""
def start_applying(self): def start_applying(self):
self.easy_applier_component = LinkedInEasyApplier(self.driver, self.resume_path, self.set_old_answers, self.gpt_answerer, self.resume_generator_manager) self.easy_applier_component = LinkedInEasyApplier(self.driver, self.resume_path, self.set_old_answers, self.gpt_answerer, self.resume_generator_manager)
searches = list(product(self.positions, self.locations)) searches = list(product(self.positions, self.locations))