martin/linkedIn_auto_jobs_applier_with_AI
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Merge branch 'main' into bug/rate-limit-error

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Parth Patel 2024-09-03 11:52:37 -04:00 committed by GitHub
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5 changed files with 21 additions and 48 deletions
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2
.gitignore vendored
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@ -12,4 +12,4 @@ generated_cv*
chrome_profile
answers.json
virtual/*
data_folder/*
data*

13
README.md
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@ -105,6 +105,8 @@ LinkedIn_AIHawk steps in as a game-changing solution to these challenges. It's n
## Installation
**Please watch this video to set up your LinkedIn_AIHawk: [How to set up LinkedIn_AIHawk](https://youtu.be/gdW9wogHEUM) - https://youtu.be/gdW9wogHEUM**
0. **Confirmed succesfull runs OSs & Python**: Python 3.10, 3.11.9(64b), 3.12.5(64b) . Windows 10, Ubuntu 22
1. **Download and Install Python:**
Ensure you have the last Python version installed. If not, download and install it from Python's official website. For detailed instructions, refer to the tutorials:
@ -505,11 +507,16 @@ TODO ):
## Troubleshooting
- **Carefully read logs and output :** Most of the errors are verbosely reflected just watch the output and try to find the root couse.
- **If nothing works by unknown reason:** Use tested OS. Reboot and/or update OS. Use new clean venv. Try update Python to the tested version.
- **ChromeDriver Issues:** Ensure ChromeDriver is compatible with your installed Chrome version.
- **Missing Files:** Verify that all necessary files are present in the data folder.
- **Invalid YAML:** Check your YAML files for syntax errors.
If you encounter any issues, you can open an issue on [GitHub](https://github.com/feder-cr/linkedIn_auto_jobs_applier_with_AI/issues). I'll be more than happy to assist you!
- **Invalid YAML:** Check your YAML files for syntax errors . Try to use external YAML validators e.g. https://www.yamllint.com/
- **OpenAI endpoint isues**: Try to check possible limits\blocking at their side
If you encounter any issues, you can open an issue on [GitHub](https://github.com/feder-cr/linkedIn_auto_jobs_applier_with_AI/issues).
Please add valuable details to the subject and to the description. If you need new feature then please reflect this.
I'll be more than happy to assist you!
## Conclusion

6
src/gpt.py
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@ -135,11 +135,7 @@ class LoggerChatModel:
class GPTAnswerer:
def __init__(self, openai_api_key):
self.llm_cheap = LoggerChatModel(
ChatOpenAI(
model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.8
)
)
self.llm_cheap = LoggerChatModel(ChatOpenAI(model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4)
@property
def job_description(self):

36
src/linkedIn_easy_applier.py
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@ -30,7 +30,6 @@ class LinkedInEasyApplier:
self.resume_generator_manager = resume_generator_manager
self.all_data = self._load_questions_from_json()
def _load_questions_from_json(self) -> List[dict]:
output_file = 'answers.json'
try:
@ -49,7 +48,6 @@ class LinkedInEasyApplier:
tb_str = traceback.format_exc()
raise Exception(f"Error loading questions data from JSON file: \nTraceback:\n{tb_str}")
def job_apply(self, job: Any):
self.driver.get(job.link)
time.sleep(random.uniform(3, 5))
@ -91,7 +89,6 @@ class LinkedInEasyApplier:
attempt += 1
raise Exception("No clickable 'Easy Apply' button found")
def _get_job_description(self) -> str:
try:
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()
raise Exception(f"Error getting Job description: \nTraceback:\n{tb_str}")
def _get_job_recruiter(self):
try:
hiring_team_section = WebDriverWait(self.driver, 10).until(
@ -253,16 +249,12 @@ class LinkedInEasyApplier:
if radios:
question_text = section.text.lower()
options = [radio.text.lower() for radio in radios]
existing_answer = None
for item in self.all_data:
if self._sanitize_text(question_text) in item['question'] and item['type'] == 'radio':
existing_answer = item
break
if existing_answer:
self._select_radio(radios, existing_answer['answer'])
return True
self._select_radio(radios, existing_answer['answer'])
return True
answer = self.gpt_answerer.answer_question_from_options(question_text, options)
self._save_questions_to_json({'type': 'radio', 'question': question_text, 'answer': answer})
self._select_radio(radios, answer)
@ -283,12 +275,10 @@ class LinkedInEasyApplier:
answer = self.gpt_answerer.answer_question_textual_wide_range(question_text)
existing_answer = None
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
break
if existing_answer:
self._enter_text(text_field, existing_answer['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._enter_text(text_field, answer)
return True
@ -302,15 +292,12 @@ class LinkedInEasyApplier:
answer_date = self.gpt_answerer.answer_question_date()
answer_text = answer_date.strftime("%Y-%m-%d")
existing_answer = None
for item in self.all_data:
if self._sanitize_text(question_text) in item['question'] and item['type'] == 'date':
existing_answer = item
break
if existing_answer:
self._enter_text(date_field, existing_answer['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._enter_text(date_field, answer_text)
@ -325,16 +312,12 @@ class LinkedInEasyApplier:
if dropdown:
select = Select(dropdown)
options = [option.text for option in select.options]
existing_answer = None
for item in self.all_data:
if self._sanitize_text(question_text) in item['question'] and item['type'] == 'dropdown':
existing_answer = item
break
if existing_answer:
self._select_dropdown_option(dropdown, existing_answer['answer'])
return True
self._select_dropdown_option(dropdown, existing_answer['answer'])
return True
answer = self.gpt_answerer.answer_question_from_options(question_text, options)
self._save_questions_to_json({'type': 'dropdown', 'question': question_text, 'answer': answer})
self._select_dropdown_option(dropdown, answer)
@ -385,7 +368,6 @@ class LinkedInEasyApplier:
tb_str = traceback.format_exc()
raise Exception(f"Error saving questions data to JSON file: \nTraceback:\n{tb_str}")
def _sanitize_text(self, text: str) -> str:
sanitized_text = text.lower()
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):
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):
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))