afiqzudinhadi/linkedIn_auto_jobs_applier_with_AI
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Revert "fixed some issues"

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This reverts commit 6540bbbb40.
This commit is contained in:
queukat 2024-09-09 23:39:12 +03:00
parent 6540bbbb40
commit b554357be9
9 changed files with 77 additions and 127 deletions
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48
src/gpt.py
View file

@ -6,7 +6,8 @@ import time
from abc import ABC, abstractmethod
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Union
from typing import Dict, List
from typing import Union
import httpx
from Levenshtein import distance
@ -37,7 +38,7 @@ class OpenAIModel(AIModel):
def invoke(self, prompt: str) -> str:
print("invoke in openai")
response = self.model.invoke(prompt)
return response.content
return response
class ClaudeModel(AIModel):
@ -48,7 +49,7 @@ class ClaudeModel(AIModel):
def invoke(self, prompt: str) -> str:
response = self.model.invoke(prompt)
return response.content
return response
class OllamaModel(AIModel):
@ -58,14 +59,14 @@ class OllamaModel(AIModel):
def invoke(self, prompt: str) -> str:
response = self.model.invoke(prompt)
return response.content
return response
class AIAdapter:
def __init__(self, config: dict, api_key: str):
self.model = self._create_model(config, api_key)
def _create_model(self, config: dict, api_key: str) -> Union[OpenAIModel, OllamaModel, ClaudeModel]:
def _create_model(self, config: dict, api_key: str) -> AIModel:
llm_model_type = config['llm_model_type']
llm_model = config['llm_model']
llm_api_url = config['llm_api_url']
@ -78,7 +79,7 @@ class AIAdapter:
elif llm_model_type == "ollama":
return OllamaModel(api_key, llm_model, llm_api_url)
else:
raise ValueError(f"Unsupported model type: {llm_model_type}")
raise ValueError(f"Unsupported model type: {model_type}")
def invoke(self, prompt: str) -> str:
return self.model.invoke(prompt)
@ -108,34 +109,25 @@ class LLMLogger:
logger.debug("Prompts are of type StringPromptValue")
prompts = prompts.text
logger.debug("Prompts converted to text: %s", prompts)
elif isinstance(prompts, dict):
logger.debug("Prompts are of type dict")
elif isinstance(prompts, Dict):
logger.debug("Prompts are of type Dict")
try:
if "messages" in prompts:
logger.debug("Prompts contain 'messages' key")
prompts = {
f"prompt_{i + 1}": prompt["content"]
for i, prompt in enumerate(prompts["messages"])
}
logger.debug("Prompts converted to dictionary: %s", prompts)
else:
logger.debug("Prompts dictionary does not contain 'messages' key")
prompts = {
f"prompt_{i + 1}": prompt.content
for i, prompt in enumerate(prompts.messages)
}
logger.debug("Prompts converted to dictionary: %s", prompts)
except Exception as e:
logger.error("Error converting prompts to dictionary: %s", str(e))
raise
else:
logger.debug("Prompts are of unknown type, attempting default conversion")
try:
if hasattr(prompts, "messages"):
logger.debug("Prompts have 'messages' attribute")
prompts = {
f"prompt_{i + 1}": prompt.content
for i, prompt in enumerate(prompts.messages)
}
logger.debug("Prompts converted to dictionary using default method: %s", prompts)
else:
logger.error("Prompts do not have 'messages' attribute, and default conversion failed")
raise ValueError("Prompts structure is not supported.")
prompts = {
f"prompt_{i + 1}": prompt.content
for i, prompt in enumerate(prompts.messages)
}
logger.debug("Prompts converted to dictionary using default method: %s", prompts)
except Exception as e:
logger.error("Error converting prompts using default method: %s", str(e))
raise
@ -299,7 +291,7 @@ class GPTAnswerer:
def __init__(self, config, llm_api_key):
self.ai_adapter = AIAdapter(config, llm_api_key)
self.llm_cheap = LoggerChatModel(self.ai_adapter.model)
self.llm_cheap = LoggerChatModel(self.ai_adapter)
@property
def job_description(self):

17
src/linkedIn_easy_applier.py
View file

@ -5,8 +5,7 @@ import random
import re
import time
import traceback
from pathlib import Path
from typing import List, Optional, Any, Tuple, Set
from typing import List, Optional, Any, Tuple
from httpx import HTTPStatusError
from reportlab.lib.pagesizes import A4
@ -24,13 +23,11 @@ from src.utils import logger
class LinkedInEasyApplier:
def __init__(self, driver: Any, resume_dir: Optional[str], set_old_answers: Set[Tuple[str, str, str]],
def __init__(self, driver: Any, resume_dir: Optional[str], set_old_answers: List[Tuple[str, str, str]],
gpt_answerer: Any, resume_generator_manager):
logger.debug("Initializing LinkedInEasyApplier")
if resume_dir is None or not os.path.exists(resume_dir):
resume_dir = None
else:
resume_dir = Path(resume_dir)
self.driver = driver
self.resume_path = resume_dir
self.set_old_answers = set_old_answers
@ -541,19 +538,17 @@ class LinkedInEasyApplier:
lines = split_text_by_width(cover_letter_text, "Helvetica", 12, max_width)
line_height = 14
max_lines_per_page = int(available_height // line_height)
for line in lines:
text_height = text_object.getY()
if text_height > bottom_margin:
text_object.textLine(line)
else:
if text_height - line_height < bottom_margin:
c.drawText(text_object)
c.showPage()
text_object = c.beginText(50, page_height - 50)
text_object.setFont("Helvetica", 12)
text_object.textLine(line)
text_object.textLine(line)
c.drawText(text_object)
c.save()

9
src/linkedIn_job_manager.py
View file

@ -47,10 +47,10 @@ class LinkedInJobManager:
def set_parameters(self, parameters):
logger.debug("Setting parameters for LinkedInJobManager")
self.company_blacklist = parameters.get('company_blacklist', []) or []
self.title_blacklist = parameters.get('title_blacklist', []) or []
self.title_blacklist = parameters.get('titleBlacklist', []) or []
self.positions = parameters.get('positions', [])
self.locations = parameters.get('locations', [])
self.apply_once_at_company = parameters.get('apply_once_at_company', False)
self.apply_once_at_company = parameters.get('applyOnceAtCompany', False)
self.base_search_url = self.get_base_search_url(parameters)
self.seen_jobs = []
@ -272,7 +272,7 @@ class LinkedInJobManager:
logger.debug(f"Applicants text found: {applicants_text}")
# Extract numeric digits from the text (e.g., "70 applicants" -> "70")
applicants_count = ''.join([char for char in str(applicants_text) if char.isdigit()])
applicants_count = ''.join(filter(str.isdigit, applicants_text))
logger.debug(f"Extracted applicants count: {applicants_count}")
if applicants_count:
@ -370,7 +370,7 @@ class LinkedInJobManager:
url_parts = []
if parameters['remote']:
url_parts.append("f_CF=f_WRA")
experience_levels = [str(i + 1) for i, (level, v) in enumerate(parameters.get('experience_level', {}).items()) if
experience_levels = [str(i + 1) for i, (level, v) in enumerate(parameters.get('experienceLevel', {}).items()) if
v]
if experience_levels:
url_parts.append(f"f_E={','.join(experience_levels)}")
@ -429,6 +429,7 @@ class LinkedInJobManager:
link_seen = link in self.seen_jobs
is_blacklisted = title_blacklisted or company_blacklisted or link_seen
logger.debug("Job blacklisted status: %s", is_blacklisted)
return is_blacklisted
return title_blacklisted or company_blacklisted or link_seen

5
src/utils.py
View file

@ -179,8 +179,3 @@ def printyellow(text):
reset = "\033[0m"
logger.debug("Printing text in yellow: %s", text)
print(f"{yellow}{text}{reset}")
def stringWidth(text, font, font_size):
bbox = font.getbbox(text)
return bbox[2] - bbox[0]