Merge remote-tracking branch 'upstream/v3' into v3
This commit is contained in:
Manu Altieri
commit
40cf3e4d34
13 changed files with 725 additions and 308 deletions
167
.gitignore
vendored
167
.gitignore
vendored
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@ -1,14 +1,155 @@
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||||||
*.csv
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# Byte-compiled / optimized / DLL files
|
||||||
__pycache__/**
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__pycache__/
|
||||||
.idea/**
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*.py[cod]
|
||||||
open_ai_calls.log
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*$py.class
|
||||||
test*
|
|
||||||
openaiSelenium*
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# C extensions
|
||||||
open_ai_calls.json
|
*.so
|
||||||
_*
|
|
||||||
|
# Distribution / packaging
|
||||||
|
.Python
|
||||||
|
build/
|
||||||
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develop-eggs/
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||||||
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dist/
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||||||
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downloads/
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||||||
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eggs/
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||||||
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.eggs/
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||||||
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lib/
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||||||
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lib64/
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||||||
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parts/
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||||||
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sdist/
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||||||
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var/
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||||||
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wheels/
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||||||
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pip-wheel-metadata/
|
||||||
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share/python-wheels/
|
||||||
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*.egg-info/
|
||||||
|
.installed.cfg
|
||||||
|
*.egg
|
||||||
|
MANIFEST
|
||||||
|
|
||||||
|
# PyInstaller
|
||||||
|
# Usually these files are written by a python script from a template
|
||||||
|
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||||
|
*.manifest
|
||||||
|
*.spec
|
||||||
|
|
||||||
|
# Installer logs
|
||||||
|
pip-log.txt
|
||||||
|
pip-delete-this-directory.txt
|
||||||
|
|
||||||
|
# Unit test / coverage reports
|
||||||
|
htmlcov/
|
||||||
|
.tox/
|
||||||
|
.nox/
|
||||||
|
.coverage
|
||||||
|
.coverage.*
|
||||||
|
.cache
|
||||||
|
nosetests.xml
|
||||||
|
coverage.xml
|
||||||
|
*.cover
|
||||||
|
*.py,cover
|
||||||
|
.hypothesis/
|
||||||
|
.pytest_cache/
|
||||||
|
|
||||||
|
# Translations
|
||||||
|
*.mo
|
||||||
|
*.pot
|
||||||
|
|
||||||
|
# Django stuff:
|
||||||
|
*.log
|
||||||
|
local_settings.py
|
||||||
|
db.sqlite3
|
||||||
|
db.sqlite3-journal
|
||||||
|
|
||||||
|
# Flask stuff:
|
||||||
|
instance/
|
||||||
|
.webassets-cache
|
||||||
|
|
||||||
|
# Scrapy stuff:
|
||||||
|
.scrapy
|
||||||
|
|
||||||
|
# Sphinx documentation
|
||||||
|
docs/_build/
|
||||||
|
_build/
|
||||||
|
|
||||||
|
# PyBuilder
|
||||||
|
target/
|
||||||
|
|
||||||
|
# Jupyter Notebook
|
||||||
|
.ipynb_checkpoints
|
||||||
|
|
||||||
|
# IPython
|
||||||
|
profile_default/
|
||||||
|
ipython_config.py
|
||||||
|
|
||||||
|
# pyenv
|
||||||
|
.python-version
|
||||||
|
|
||||||
|
# pipenv
|
||||||
|
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
||||||
|
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||||
|
# having no cross-platform support, pipenv’s dependency resolution may lead to different
|
||||||
|
# Pipfile.lock files generated on each colleague’s machine.
|
||||||
|
# Thus, uncomment the following line if the pipenv environment is expected to be identical
|
||||||
|
# across all environments.
|
||||||
|
#Pipfile.lock
|
||||||
|
|
||||||
|
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
|
||||||
|
__pypackages__/
|
||||||
|
|
||||||
|
# Celery stuff
|
||||||
|
celerybeat-schedule
|
||||||
|
celerybeat.pid
|
||||||
|
|
||||||
|
# SageMath parsed files
|
||||||
|
*.sage.py
|
||||||
|
|
||||||
|
# Environments
|
||||||
|
.env
|
||||||
.venv
|
.venv
|
||||||
generated_cv*
|
env/
|
||||||
.vscode
|
venv/
|
||||||
chrome_profile
|
ENV/
|
||||||
answers.json
|
env.bak/
|
||||||
data*
|
venv.bak/
|
||||||
|
|
||||||
|
# Spyder project settings
|
||||||
|
.spyderproject
|
||||||
|
.spyproject
|
||||||
|
|
||||||
|
# Rope project settings
|
||||||
|
.ropeproject
|
||||||
|
|
||||||
|
# mkdocs documentation
|
||||||
|
/site
|
||||||
|
|
||||||
|
# mypy
|
||||||
|
.mypy_cache/
|
||||||
|
|
||||||
|
# PyCharm and all JetBrains IDEs
|
||||||
|
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
|
||||||
|
.idea/
|
||||||
|
*.iml
|
||||||
|
|
||||||
|
# Visual Studio Code
|
||||||
|
.vscode/
|
||||||
|
|
||||||
|
# Visual Studio 2015/2017/2019/2022
|
||||||
|
.vs/
|
||||||
|
*.opendb
|
||||||
|
*.VC.db
|
||||||
|
|
||||||
|
# User-specific files
|
||||||
|
*.suo
|
||||||
|
*.user
|
||||||
|
*.userosscache
|
||||||
|
*.sln.docstates
|
||||||
|
|
||||||
|
# Mono Auto Generated Files
|
||||||
|
mono_crash.*
|
||||||
|
|
||||||
|
# Project Specific
|
||||||
|
data_folder/output/*
|
||||||
|
generated_cv/*
|
||||||
|
chrome_profile/*
|
||||||
|
answers.json
|
||||||
27
README.md
27
README.md
|
|
@ -133,6 +133,11 @@ LinkedIn_AIHawk steps in as a game-changing solution to these challenges. It's n
|
||||||
source virtual/bin/activate
|
source virtual/bin/activate
|
||||||
```
|
```
|
||||||
|
|
||||||
|
or for Windows-based machines -
|
||||||
|
```bash
|
||||||
|
.\virtual\Scripts\activate
|
||||||
|
```
|
||||||
|
|
||||||
5. **Install the required packages:**
|
5. **Install the required packages:**
|
||||||
```bash
|
```bash
|
||||||
pip install -r requirements.txt
|
pip install -r requirements.txt
|
||||||
|
|
@ -148,7 +153,7 @@ This file contains sensitive information. Never share or commit this file to ver
|
||||||
- Replace with your LinkedIn account email address
|
- Replace with your LinkedIn account email address
|
||||||
- `password: [Your LinkedIn password]`
|
- `password: [Your LinkedIn password]`
|
||||||
- Replace with your LinkedIn account password
|
- Replace with your LinkedIn account password
|
||||||
- `llm_api_key: [Your OpenAI or Ollama API key]`
|
- `llm_api_key: [Your OpenAI or Ollama API key or Gemini API key]`
|
||||||
- Replace with your OpenAI API key for GPT integration
|
- Replace with your OpenAI API key for GPT integration
|
||||||
- To obtain an API key, follow the tutorial at: https://medium.com/@lorenzozar/how-to-get-your-own-openai-api-key-f4d44e60c327
|
- To obtain an API key, follow the tutorial at: https://medium.com/@lorenzozar/how-to-get-your-own-openai-api-key-f4d44e60c327
|
||||||
- Note: You need to add credit to your OpenAI account to use the API. You can add credit by visiting the [OpenAI billing dashboard](https://platform.openai.com/account/billing).
|
- Note: You need to add credit to your OpenAI account to use the API. You can add credit by visiting the [OpenAI billing dashboard](https://platform.openai.com/account/billing).
|
||||||
|
|
@ -157,6 +162,7 @@ This file contains sensitive information. Never share or commit this file to ver
|
||||||
`{'error': {'message': 'Rate limit reached for gpt-4o-mini in organization <org> on requests per day (RPD): Limit 200, Used 200, Requested 1.}}`
|
`{'error': {'message': 'Rate limit reached for gpt-4o-mini in organization <org> on requests per day (RPD): Limit 200, Used 200, Requested 1.}}`
|
||||||
OpenAI will update your account automatically, but it might take some time, ranging from a couple of hours to a few days.
|
OpenAI will update your account automatically, but it might take some time, ranging from a couple of hours to a few days.
|
||||||
You can find more about your organization limits on the [official page](https://platform.openai.com/settings/organization/limits).
|
You can find more about your organization limits on the [official page](https://platform.openai.com/settings/organization/limits).
|
||||||
|
- For obtaining Gemini API key visit [Google AI for Devs](https://ai.google.dev/gemini-api/docs/api-key)
|
||||||
|
|
||||||
|
|
||||||
### 2. config.yaml
|
### 2. config.yaml
|
||||||
|
|
@ -220,17 +226,19 @@ This file defines your job search parameters and bot behavior. Each section cont
|
||||||
#### 2.1 config.yaml - Customize LLM model endpoint
|
#### 2.1 config.yaml - Customize LLM model endpoint
|
||||||
|
|
||||||
- `llm_model_type`:
|
- `llm_model_type`:
|
||||||
- Choose the model type, supported: openai / ollama / claude
|
- Choose the model type, supported: openai / ollama / claude / gemini
|
||||||
- `llm_model`:
|
- `llm_model`:
|
||||||
- Choose the LLM model, currently supported:
|
- Choose the LLM model, currently supported:
|
||||||
- openai: gpt-4o
|
- openai: gpt-4o
|
||||||
- ollama: llama2, mistral:v0.3
|
- ollama: llama2, mistral:v0.3
|
||||||
- claude: any model
|
- claude: any model
|
||||||
|
- gemini: any model
|
||||||
- `llm_api_url`:
|
- `llm_api_url`:
|
||||||
- Link of the API endpoint for the LLM model
|
- Link of the API endpoint for the LLM model
|
||||||
- openai: https://api.pawan.krd/cosmosrp/v1
|
- openai: https://api.pawan.krd/cosmosrp/v1
|
||||||
- ollama: http://127.0.0.1:11434/
|
- ollama: http://127.0.0.1:11434/
|
||||||
- claude: https://api.anthropic.com/v1
|
- claude: https://api.anthropic.com/v1
|
||||||
|
- gemini: no api_url
|
||||||
- Note: To run local Ollama, follow the guidelines here: [Guide to Ollama deployment](https://github.com/ollama/ollama)
|
- Note: To run local Ollama, follow the guidelines here: [Guide to Ollama deployment](https://github.com/ollama/ollama)
|
||||||
|
|
||||||
### 3. plain_text_resume.yaml
|
### 3. plain_text_resume.yaml
|
||||||
|
|
@ -272,7 +280,8 @@ Each section has specific fields to fill out:
|
||||||
- This section outlines your academic background, including degrees earned and relevant coursework.
|
- This section outlines your academic background, including degrees earned and relevant coursework.
|
||||||
- **degree**: The type of degree obtained (e.g., Bachelor's Degree, Master's Degree).
|
- **degree**: The type of degree obtained (e.g., Bachelor's Degree, Master's Degree).
|
||||||
- **university**: The name of the university or institution where you studied.
|
- **university**: The name of the university or institution where you studied.
|
||||||
- **gpa**: Your Grade Point Average or equivalent measure of academic performance.
|
- **final_evaluation_grade**: Your Grade Point Average or equivalent measure of academic performance.
|
||||||
|
- **start_date**: The start year of your studies.
|
||||||
- **graduation_year**: The year you graduated.
|
- **graduation_year**: The year you graduated.
|
||||||
- **field_of_study**: The major or focus area of your studies.
|
- **field_of_study**: The major or focus area of your studies.
|
||||||
- **exam**: A list of courses or subjects taken along with their respective grades.
|
- **exam**: A list of courses or subjects taken along with their respective grades.
|
||||||
|
|
@ -280,11 +289,12 @@ Each section has specific fields to fill out:
|
||||||
- Example:
|
- Example:
|
||||||
```yaml
|
```yaml
|
||||||
education_details:
|
education_details:
|
||||||
- degree: "Bachelor's Degree"
|
- education_level: "Bachelor's Degree"
|
||||||
university: "University of Example"
|
institution: "University of Example"
|
||||||
gpa: "3.8/4"
|
|
||||||
graduation_year: "2022"
|
|
||||||
field_of_study: "Software Engineering"
|
field_of_study: "Software Engineering"
|
||||||
|
final_evaluation_grade: "4/4"
|
||||||
|
start_date: "2021"
|
||||||
|
year_of_completion: "2023"
|
||||||
exam:
|
exam:
|
||||||
Algorithms: "A"
|
Algorithms: "A"
|
||||||
Data Structures: "B+"
|
Data Structures: "B+"
|
||||||
|
|
@ -354,7 +364,8 @@ Each section has specific fields to fill out:
|
||||||
|
|
||||||
- `certifications:`
|
- `certifications:`
|
||||||
- Include any professional certifications you have earned.
|
- Include any professional certifications you have earned.
|
||||||
- **certification_name**: The name of the certification.
|
- name: "PMP"
|
||||||
|
description: "Certification for project management professionals, issued by the Project Management Institute (PMI)"
|
||||||
|
|
||||||
- Example:
|
- Example:
|
||||||
```yaml
|
```yaml
|
||||||
|
|
|
||||||
|
|
@ -1,6 +1,6 @@
|
||||||
remote: [true/false]
|
remote: [true/false]
|
||||||
|
|
||||||
experienceLevel:
|
experience_level:
|
||||||
internship: [true/false]
|
internship: [true/false]
|
||||||
entry: [true/false]
|
entry: [true/false]
|
||||||
associate: [true/false]
|
associate: [true/false]
|
||||||
|
|
@ -31,17 +31,21 @@ locations:
|
||||||
- Country1
|
- Country1
|
||||||
- Country2
|
- Country2
|
||||||
|
|
||||||
applyOnceAtCompany: [true/false]
|
apply_once_at_company: [true/false]
|
||||||
|
|
||||||
distance: 100
|
distance: 100
|
||||||
|
|
||||||
companyBlacklist:
|
company_blacklist:
|
||||||
- Company1
|
- Company1
|
||||||
- Company2
|
- Company2
|
||||||
|
|
||||||
titleBlacklist:
|
title_blacklist:
|
||||||
- word1
|
- word1
|
||||||
- word2
|
- word2
|
||||||
|
|
||||||
|
job_applicants_threshold:
|
||||||
|
min_applicants: 0
|
||||||
|
max_applicants: 100
|
||||||
|
|
||||||
llm_model_type: openai
|
llm_model_type: openai
|
||||||
llm_model: gpt-4o
|
llm_model: gpt-4o
|
||||||
|
|
|
||||||
|
|
@ -1,7 +1,7 @@
|
||||||
personal_information:
|
personal_information:
|
||||||
name: "[Your Name]"
|
name: "[Your Name]"
|
||||||
surname: "[Your Surname]"
|
surname: "[Your Surname]"
|
||||||
date_of_birth: "[DD/MM/YYYY]"
|
date_of_birth: "[Your Date of Birth]"
|
||||||
country: "[Your Country]"
|
country: "[Your Country]"
|
||||||
city: "[Your City]"
|
city: "[Your City]"
|
||||||
address: "[Your Address]"
|
address: "[Your Address]"
|
||||||
|
|
@ -12,74 +12,92 @@ personal_information:
|
||||||
linkedin: "[Your LinkedIn Profile URL]"
|
linkedin: "[Your LinkedIn Profile URL]"
|
||||||
|
|
||||||
education_details:
|
education_details:
|
||||||
- degree: "[Your Degree]"
|
- education_level: "[Your Education Level]"
|
||||||
university: "[Your University]"
|
institution: "[Your Institution]"
|
||||||
gpa: "[Your GPA]"
|
|
||||||
graduation_year: "[Year of Graduation]"
|
|
||||||
field_of_study: "[Your Field of Study]"
|
field_of_study: "[Your Field of Study]"
|
||||||
|
final_evaluation_grade: "[Your Final Evaluation Grade]"
|
||||||
|
start_date: "[Start Date]"
|
||||||
|
year_of_completion: "[Year of Completion]"
|
||||||
exam:
|
exam:
|
||||||
[Course Name 1]: "[Grade]"
|
exam_name_1: "[Grade]"
|
||||||
[Course Name 2]: "[Grade]"
|
exam_name_2: "[Grade]"
|
||||||
[Course Name 3]: "[Grade]"
|
exam_name_3: "[Grade]"
|
||||||
[Course Name 4]: "[Grade]"
|
exam_name_4: "[Grade]"
|
||||||
[Course Name 5]: "[Grade]"
|
exam_name_5: "[Grade]"
|
||||||
|
exam_name_6: "[Grade]"
|
||||||
|
|
||||||
experience_details:
|
experience_details:
|
||||||
- position: "[Your Job Title]"
|
- position: "[Your Position]"
|
||||||
company: "[Company Name]"
|
company: "[Company Name]"
|
||||||
employment_period: "[Start Date] - [End Date]"
|
employment_period: "[Employment Period]"
|
||||||
location: "[Location]"
|
location: "[Location]"
|
||||||
industry: "[Industry]"
|
industry: "[Industry]"
|
||||||
key_responsibilities:
|
key_responsibilities:
|
||||||
- responsibility_1: "[Key Responsibility 1]"
|
- responsibility_1: "[Responsibility Description]"
|
||||||
- responsibility_2: "[Key Responsibility 2]"
|
- responsibility_2: "[Responsibility Description]"
|
||||||
- responsibility_3: "[Key Responsibility 3]"
|
- responsibility_3: "[Responsibility Description]"
|
||||||
skills_acquired:
|
skills_acquired:
|
||||||
- "[Skill 1]"
|
- "[Skill]"
|
||||||
- "[Skill 2]"
|
- "[Skill]"
|
||||||
- "[Skill 3]"
|
- "[Skill]"
|
||||||
|
|
||||||
|
- position: "[Your Position]"
|
||||||
|
company: "[Company Name]"
|
||||||
|
employment_period: "[Employment Period]"
|
||||||
|
location: "[Location]"
|
||||||
|
industry: "[Industry]"
|
||||||
|
key_responsibilities:
|
||||||
|
- responsibility_1: "[Responsibility Description]"
|
||||||
|
- responsibility_2: "[Responsibility Description]"
|
||||||
|
- responsibility_3: "[Responsibility Description]"
|
||||||
|
skills_acquired:
|
||||||
|
- "[Skill]"
|
||||||
|
- "[Skill]"
|
||||||
|
- "[Skill]"
|
||||||
|
|
||||||
projects:
|
projects:
|
||||||
- name: "[Project Name]"
|
- name: "[Project Name]"
|
||||||
description: "[Brief Description of the Project]"
|
description: "[Project Description]"
|
||||||
link: "[Project URL]"
|
link: "[Project Link]"
|
||||||
|
|
||||||
- name: "[Project Name]"
|
- name: "[Project Name]"
|
||||||
description: "[Brief Description of the Project]"
|
description: "[Project Description]"
|
||||||
link: "[Project URL]"
|
link: "[Project Link]"
|
||||||
|
|
||||||
achievements:
|
achievements:
|
||||||
- name: "[Achievement Title]"
|
- name: "[Achievement Name]"
|
||||||
description: "[Brief Description of the Achievement]"
|
description: "[Achievement Description]"
|
||||||
- name: "[Achievement Title]"
|
- name: "[Achievement Name]"
|
||||||
description: "[Brief Description of the Achievement]"
|
description: "[Achievement Description]"
|
||||||
|
|
||||||
certifications:
|
certifications:
|
||||||
- "[Certification Name]"
|
- name: "[Certification Name]"
|
||||||
|
description: "[Certification Description]"
|
||||||
|
- name: "[Certification Name]"
|
||||||
|
description: "[Certification Description]"
|
||||||
|
|
||||||
languages:
|
languages:
|
||||||
- language: "[Language Name]"
|
- language: "[Language]"
|
||||||
proficiency: "[Proficiency Level]"
|
proficiency: "[Proficiency Level]"
|
||||||
- language: "[Language Name]"
|
- language: "[Language]"
|
||||||
proficiency: "[Proficiency Level]"
|
proficiency: "[Proficiency Level]"
|
||||||
|
|
||||||
interests:
|
interests:
|
||||||
- "[Interest 1]"
|
- "[Interest]"
|
||||||
- "[Interest 2]"
|
- "[Interest]"
|
||||||
- "[Interest 3]"
|
- "[Interest]"
|
||||||
- "[Interest 4]"
|
|
||||||
- "[Interest 5]"
|
|
||||||
|
|
||||||
availability:
|
availability:
|
||||||
notice_period: "[Notice Period]"
|
notice_period: "[Notice Period]"
|
||||||
|
|
||||||
salary_expectations:
|
salary_expectations:
|
||||||
salary_range_usd: "[Expected Salary Range in USD]"
|
salary_range_usd: "[Salary Range]"
|
||||||
|
|
||||||
self_identification:
|
self_identification:
|
||||||
gender: "[Gender]"
|
gender: "[Gender]"
|
||||||
pronouns: "[Pronouns]"
|
pronouns: "[Pronouns]"
|
||||||
veteran: "[Veteran Status]"
|
veteran: "[Yes/No]"
|
||||||
disability: "[Disability Status]"
|
disability: "[Yes/No]"
|
||||||
ethnicity: "[Ethnicity]"
|
ethnicity: "[Ethnicity]"
|
||||||
|
|
||||||
legal_authorization:
|
legal_authorization:
|
||||||
|
|
@ -98,4 +116,4 @@ work_preferences:
|
||||||
open_to_relocation: "[Yes/No]"
|
open_to_relocation: "[Yes/No]"
|
||||||
willing_to_complete_assessments: "[Yes/No]"
|
willing_to_complete_assessments: "[Yes/No]"
|
||||||
willing_to_undergo_drug_tests: "[Yes/No]"
|
willing_to_undergo_drug_tests: "[Yes/No]"
|
||||||
willing_to_undergo_background_checks: "[Yes/No]"
|
willing_to_undergo_background_checks: "[Yes/No]"
|
||||||
|
|
@ -1,6 +1,6 @@
|
||||||
remote: true
|
remote: true
|
||||||
|
|
||||||
experienceLevel:
|
experience_level:
|
||||||
internship: true
|
internship: true
|
||||||
entry: true
|
entry: true
|
||||||
associate: true
|
associate: true
|
||||||
|
|
@ -29,15 +29,21 @@ positions:
|
||||||
locations:
|
locations:
|
||||||
- USA
|
- USA
|
||||||
|
|
||||||
applyOnceAtCompany: [true/false]
|
apply_once_at_company: [true/false]
|
||||||
|
|
||||||
distance: 100
|
distance: 100
|
||||||
|
|
||||||
companyBlacklist:
|
company_blacklist:
|
||||||
- Noir
|
- Noir
|
||||||
- Crossover
|
- Crossover
|
||||||
|
|
||||||
titleBlacklist:
|
title_blacklist:
|
||||||
|
- word1
|
||||||
|
- word2
|
||||||
|
|
||||||
|
job_applicants_threshold:
|
||||||
|
min_applicants: 0
|
||||||
|
max_applicants: 100
|
||||||
|
|
||||||
llm_model_type: openai
|
llm_model_type: openai
|
||||||
llm_model: 'gpt-4o'
|
llm_model: 'gpt-4o'
|
||||||
|
|
|
||||||
|
|
@ -1,118 +1,124 @@
|
||||||
personal_information:
|
personal_information:
|
||||||
name: "Liam"
|
name: "Giovanni"
|
||||||
surname: "Murphy"
|
surname: "Bianchi"
|
||||||
date_of_birth: "15/08/1995"
|
date_of_birth: "12/02/1988"
|
||||||
country: "Ireland"
|
country: "Italy"
|
||||||
city: "Galway"
|
city: "Rome"
|
||||||
address: "Galway City Center"
|
address: "Via Nazionale, 45"
|
||||||
phone_prefix: "+353"
|
phone_prefix: "+39"
|
||||||
phone: "871234567"
|
phone: "3345678901"
|
||||||
email: "liam.murphy@gmail.com"
|
email: "giovanni.bianchi@example.com"
|
||||||
github: "https://github.com/liam-murphy"
|
github: "https://github.com/giovanni-bianchi"
|
||||||
linkedin: "https://www.linkedin.com/in/liam-murphy/"
|
linkedin: "https://www.linkedin.com/in/giovanni-bianchi/"
|
||||||
|
|
||||||
education_details:
|
education_details:
|
||||||
- degree: "Bachelor's Degree"
|
- education_level: "Master's Degree"
|
||||||
university: "National University of Ireland, Galway"
|
institution: "University of Rome"
|
||||||
gpa: "4/4"
|
field_of_study: "Computer Engineering"
|
||||||
graduation_year: "2020"
|
final_evaluation_grade: "110/110"
|
||||||
field_of_study: "Computer Science"
|
start_date: "2011"
|
||||||
|
year_of_completion: "2013"
|
||||||
exam:
|
exam:
|
||||||
Information Theory and Inference: "4"
|
Computer Networks: "30/30"
|
||||||
Algorithm Analysis and Design: "4"
|
Advanced Algorithms: "30/30"
|
||||||
Object-Oriented Languages and Programming: "4"
|
Database Systems: "30/30"
|
||||||
Linear Algebra and Numerical Analysis: "4"
|
Embedded Systems: "30/30"
|
||||||
Database: "4"
|
Artificial Intelligence: "30/30"
|
||||||
|
|
||||||
experience_details:
|
experience_details:
|
||||||
- position: "Co-Founder & Software Engineer"
|
- position: "Senior Software Engineer"
|
||||||
company: "CryptoWave Solutions"
|
company: "TechSolutions"
|
||||||
employment_period: "03/2021 - Present"
|
employment_period: "01/2018 - Present"
|
||||||
location: "Ireland"
|
location: "Rome, Italy"
|
||||||
industry: "Blockchain Technology"
|
industry: "Software Development"
|
||||||
key_responsibilities:
|
key_responsibilities:
|
||||||
- responsibility_1: "Co-founded and led a startup specializing in app and software development with a focus on blockchain technology"
|
- responsibility_1: "Led a team of developers in designing and implementing enterprise software solutions"
|
||||||
- responsibility_2: "Provided blockchain consultations for 10+ companies, enhancing their software capabilities with secure, decentralized solutions"
|
- responsibility_2: "Architected scalable systems to handle high-volume data processing"
|
||||||
- responsibility_3: "Developed blockchain applications, integrated cutting-edge technology to meet client needs and drive industry innovation"
|
- responsibility_3: "Optimized application performance and reduced downtime by 20%"
|
||||||
skills_acquired:
|
skills_acquired:
|
||||||
- "Blockchain development"
|
- "Software architecture"
|
||||||
- "Software engineering"
|
- "Team leadership"
|
||||||
- "Consultancy"
|
- "Performance optimization"
|
||||||
|
|
||||||
- position: "Research Intern"
|
- position: "Software Developer"
|
||||||
company: "National University of Ireland, Galway"
|
company: "Innovatech"
|
||||||
employment_period: "11/2022 - 03/2023"
|
employment_period: "06/2015 - 12/2017"
|
||||||
location: "Galway, Ireland"
|
location: "Milan, Italy"
|
||||||
industry: "IoT Security Research"
|
industry: "Technology"
|
||||||
key_responsibilities:
|
key_responsibilities:
|
||||||
- responsibility_1: "Conducted in-depth research on IoT security, focusing on binary instrumentation and runtime monitoring"
|
- responsibility_1: "Developed and maintained web applications using modern technologies"
|
||||||
- responsibility_2: "Performed in-depth study of the MQTT protocol and Falco"
|
- responsibility_2: "Collaborated with UX/UI designers to enhance user experience"
|
||||||
- responsibility_3: "Developed multiple software components including MQTT packet analysis library, Falco adapter, and RML monitor in Prolog"
|
- responsibility_3: "Implemented automated testing procedures to ensure code quality"
|
||||||
- responsibility_4: "Authored thesis 'Binary Instrumentation for Runtime Monitoring of Internet of Things Systems Using Falco'"
|
|
||||||
skills_acquired:
|
skills_acquired:
|
||||||
- "IoT security"
|
- "Web development"
|
||||||
- "Binary instrumentation"
|
- "User experience design"
|
||||||
- "MQTT protocol"
|
- "Automated testing"
|
||||||
- "Prolog programming"
|
|
||||||
|
|
||||||
- position: "Software Engineer"
|
- position: "Junior Developer"
|
||||||
company: "University Hospital Galway"
|
company: "StartUp Hub"
|
||||||
employment_period: "05/2022 - 11/2022"
|
employment_period: "01/2014 - 05/2015"
|
||||||
location: "Galway, Ireland"
|
location: "Florence, Italy"
|
||||||
industry: "Healthcare IT"
|
industry: "Startups"
|
||||||
key_responsibilities:
|
key_responsibilities:
|
||||||
- responsibility_1: "Integrated and enforced robust security protocols"
|
- responsibility_1: "Assisted in the development of mobile applications and web platforms"
|
||||||
- responsibility_2: "Developed and maintained a critical software tool for password validation used by over 1,600 employees"
|
- responsibility_2: "Participated in code reviews and contributed to software design discussions"
|
||||||
- responsibility_3: "Played an integral role in the hospital's cybersecurity team"
|
- responsibility_3: "Resolved bugs and implemented feature enhancements"
|
||||||
skills_acquired:
|
skills_acquired:
|
||||||
- "Cybersecurity"
|
- "Mobile app development"
|
||||||
- "Software development"
|
- "Code reviews"
|
||||||
- "Password validation"
|
- "Bug fixing"
|
||||||
|
|
||||||
projects:
|
projects:
|
||||||
- name: "JobBot"
|
- name: "E-Commerce Platform"
|
||||||
description: "AI-driven tool to automate and personalize job applications on LinkedIn, gained over 3000 stars on GitHub, improving efficiency and reducing application time"
|
description: "Developed a scalable e-commerce platform with advanced features like real-time inventory tracking and user analytics"
|
||||||
link: "https://github.com/liam-murphy/jobbot"
|
link: "https://github.com/giovanni-bianchi/ecommerce-platform"
|
||||||
- name: "mqtt-packet-parser"
|
- name: "Smart Home Automation"
|
||||||
description: "Developed a Node.js module for parsing MQTT packets, improved parsing efficiency by 40%"
|
description: "Created a smart home automation system integrating various IoT devices for remote control and monitoring"
|
||||||
link: "https://github.com/liam-murphy/mqtt-packet-parser"
|
link: "https://github.com/giovanni-bianchi/smart-home-automation"
|
||||||
|
|
||||||
achievements:
|
achievements:
|
||||||
- name: "Winner of an Irish public competition"
|
- name: "Top Innovator Award"
|
||||||
description: "Won first place in a public competition with a perfect score of 70/70, securing a Software Developer position at University Hospital Galway"
|
description: "Recognized for innovative solutions and contributions to high-impact projects at TechSolutions"
|
||||||
- name: "Galway Merit Scholarship"
|
- name: "Best Young Developer"
|
||||||
description: "Awarded annually from 2018 to 2020 in recognition of academic excellence and contribution"
|
description: "Awarded for outstanding performance and contributions during the first three years at Innovatech"
|
||||||
- name: "GitHub Recognition"
|
|
||||||
description: "Gained over 3000 stars on GitHub with JobBot project"
|
|
||||||
|
|
||||||
certifications:
|
certifications:
|
||||||
- "C1"
|
- name: "Certified Ethical Hacker (CEH)"
|
||||||
|
description: "Certification for expertise in ethical hacking and cybersecurity practices"
|
||||||
|
- name: "AWS Certified DevOps Engineer"
|
||||||
|
description: "Certification for DevOps practices and using AWS for cloud services"
|
||||||
|
- name: "Microsoft Certified: Azure Solutions Architect Expert"
|
||||||
|
description: "Certification for designing and implementing Azure solutions"
|
||||||
|
- name: "Certified Kubernetes Administrator (CKA)"
|
||||||
|
description: "Certification for managing and orchestrating Kubernetes clusters"
|
||||||
|
- name: "Certified Data Privacy Professional (CDPP)"
|
||||||
|
description: "Certification for ensuring data privacy and compliance with regulations"
|
||||||
|
|
||||||
languages:
|
languages:
|
||||||
- language: "English"
|
- language: "Italian"
|
||||||
proficiency: "Native"
|
proficiency: "Native"
|
||||||
- language: "Spanish"
|
- language: "English"
|
||||||
proficiency: "Professional"
|
proficiency: "Fluent"
|
||||||
|
|
||||||
interests:
|
interests:
|
||||||
- "Full-Stack Development"
|
- "Cloud Computing"
|
||||||
- "Software Architecture"
|
- "Cybersecurity"
|
||||||
- "IoT system design and development"
|
- "IoT Development"
|
||||||
- "Artificial Intelligence"
|
- "Artificial Intelligence"
|
||||||
- "Cloud Technologies"
|
- "Data Privacy"
|
||||||
|
|
||||||
availability:
|
availability:
|
||||||
notice_period: "immediately"
|
notice_period: "2 months"
|
||||||
|
|
||||||
salary_expectations:
|
salary_expectations:
|
||||||
salary_range_usd: "100000"
|
salary_range_usd: "90000 - 110000"
|
||||||
|
|
||||||
self_identification:
|
self_identification:
|
||||||
gender: "Male"
|
gender: "Male"
|
||||||
pronouns: "He"
|
pronouns: "He/Him"
|
||||||
veteran: "No"
|
veteran: "No"
|
||||||
disability: "No"
|
disability: "No"
|
||||||
ethnicity: "white"
|
ethnicity: "White"
|
||||||
|
|
||||||
legal_authorization:
|
legal_authorization:
|
||||||
eu_work_authorization: "Yes"
|
eu_work_authorization: "Yes"
|
||||||
|
|
@ -130,4 +136,4 @@ work_preferences:
|
||||||
open_to_relocation: "Yes"
|
open_to_relocation: "Yes"
|
||||||
willing_to_complete_assessments: "Yes"
|
willing_to_complete_assessments: "Yes"
|
||||||
willing_to_undergo_drug_tests: "Yes"
|
willing_to_undergo_drug_tests: "Yes"
|
||||||
willing_to_undergo_background_checks: "Yes"
|
willing_to_undergo_background_checks: "Yes"
|
||||||
|
|
|
||||||
4
main.py
4
main.py
|
|
@ -9,7 +9,7 @@ from selenium.webdriver.chrome.service import Service as ChromeService
|
||||||
from webdriver_manager.chrome import ChromeDriverManager
|
from webdriver_manager.chrome import ChromeDriverManager
|
||||||
from selenium.common.exceptions import WebDriverException, TimeoutException
|
from selenium.common.exceptions import WebDriverException, TimeoutException
|
||||||
from lib_resume_builder_AIHawk import Resume,StyleManager,FacadeManager,ResumeGenerator
|
from lib_resume_builder_AIHawk import Resume,StyleManager,FacadeManager,ResumeGenerator
|
||||||
from src.utils import chromeBrowserOptions
|
from src.utils import chrome_browser_options
|
||||||
from src.gpt import GPTAnswerer
|
from src.gpt import GPTAnswerer
|
||||||
from src.linkedIn_authenticator import LinkedInAuthenticator
|
from src.linkedIn_authenticator import LinkedInAuthenticator
|
||||||
from src.linkedIn_bot_facade import LinkedInBotFacade
|
from src.linkedIn_bot_facade import LinkedInBotFacade
|
||||||
|
|
@ -149,7 +149,7 @@ class FileManager:
|
||||||
|
|
||||||
def init_browser() -> webdriver.Chrome:
|
def init_browser() -> webdriver.Chrome:
|
||||||
try:
|
try:
|
||||||
options = chromeBrowserOptions()
|
options = chrome_browser_options()
|
||||||
service = ChromeService(ChromeDriverManager().install())
|
service = ChromeService(ChromeDriverManager().install())
|
||||||
return webdriver.Chrome(service=service, options=options)
|
return webdriver.Chrome(service=service, options=options)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
|
|
|
||||||
|
|
@ -13,4 +13,13 @@ webdriver-manager==4.0.2
|
||||||
click
|
click
|
||||||
git+https://github.com/feder-cr/lib_resume_builder_AIHawk.git
|
git+https://github.com/feder-cr/lib_resume_builder_AIHawk.git
|
||||||
linkedin-api
|
linkedin-api
|
||||||
pdfminer.six==20221105
|
pdfminer.six==20221105
|
||||||
|
inputimeout==1.0.4
|
||||||
|
langchain-ollama==0.1.3
|
||||||
|
langchain-anthropic==0.1.3
|
||||||
|
langchain-google-genai==1.0.10
|
||||||
|
jsonschema==4.23.0
|
||||||
|
jsonschema-specifications==2023.12.1
|
||||||
|
httpx~=0.27.2
|
||||||
|
python-dotenv~=1.0.1
|
||||||
|
PyYAML~=6.0.2
|
||||||
|
|
|
||||||
133
src/gpt.py
133
src/gpt.py
|
|
@ -3,11 +3,11 @@ import os
|
||||||
import re
|
import re
|
||||||
import textwrap
|
import textwrap
|
||||||
import time
|
import time
|
||||||
from datetime import datetime
|
|
||||||
from abc import ABC, abstractmethod
|
from abc import ABC, abstractmethod
|
||||||
from typing import Dict, List, Union
|
from datetime import datetime
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
from typing import Dict, List
|
from typing import Dict, List
|
||||||
|
from typing import Union
|
||||||
|
|
||||||
import httpx
|
import httpx
|
||||||
from Levenshtein import distance
|
from Levenshtein import distance
|
||||||
|
|
@ -16,39 +16,42 @@ from langchain_core.messages.ai import AIMessage
|
||||||
from langchain_core.output_parsers import StrOutputParser
|
from langchain_core.output_parsers import StrOutputParser
|
||||||
from langchain_core.prompt_values import StringPromptValue
|
from langchain_core.prompt_values import StringPromptValue
|
||||||
from langchain_core.prompts import ChatPromptTemplate
|
from langchain_core.prompts import ChatPromptTemplate
|
||||||
from langchain_openai import ChatOpenAI
|
|
||||||
|
|
||||||
import src.strings as strings
|
import src.strings as strings
|
||||||
from src.utils import logger
|
from src.utils import logger
|
||||||
|
|
||||||
load_dotenv()
|
load_dotenv()
|
||||||
|
|
||||||
|
|
||||||
class AIModel(ABC):
|
class AIModel(ABC):
|
||||||
@abstractmethod
|
@abstractmethod
|
||||||
def invoke(self, prompt: str) -> str:
|
def invoke(self, prompt: str) -> str:
|
||||||
pass
|
pass
|
||||||
|
|
||||||
|
|
||||||
class OpenAIModel(AIModel):
|
class OpenAIModel(AIModel):
|
||||||
def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
|
def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
|
||||||
from langchain_openai import ChatOpenAI
|
from langchain_openai import ChatOpenAI
|
||||||
self.model = ChatOpenAI(model_name=llm_model, openai_api_key=api_key,
|
self.model = ChatOpenAI(model_name=llm_model, openai_api_key=api_key,
|
||||||
temperature=0.4, base_url=llm_api_url)
|
temperature=0.4, base_url=llm_api_url)
|
||||||
|
|
||||||
def invoke(self, prompt: str) -> str:
|
def invoke(self, prompt: str) -> str:
|
||||||
print("invoke in openai")
|
print("invoke in openai")
|
||||||
response = self.model.invoke(prompt)
|
response = self.model.invoke(prompt)
|
||||||
return response
|
return response
|
||||||
|
|
||||||
|
|
||||||
class ClaudeModel(AIModel):
|
class ClaudeModel(AIModel):
|
||||||
def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
|
def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
|
||||||
from langchain_anthropic import ChatAnthropic
|
from langchain_anthropic import ChatAnthropic
|
||||||
self.model = ChatAnthropic(model=llm_model, api_key=api_key,
|
self.model = ChatAnthropic(model=llm_model, api_key=api_key,
|
||||||
temperature=0.4, base_url=llm_api_url)
|
temperature=0.4, base_url=llm_api_url)
|
||||||
|
|
||||||
def invoke(self, prompt: str) -> str:
|
def invoke(self, prompt: str) -> str:
|
||||||
response = self.model.invoke(prompt)
|
response = self.model.invoke(prompt)
|
||||||
return response
|
return response
|
||||||
|
|
||||||
|
|
||||||
class OllamaModel(AIModel):
|
class OllamaModel(AIModel):
|
||||||
def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
|
def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
|
||||||
from langchain_ollama import ChatOllama
|
from langchain_ollama import ChatOllama
|
||||||
|
|
@ -58,6 +61,17 @@ class OllamaModel(AIModel):
|
||||||
response = self.model.invoke(prompt)
|
response = self.model.invoke(prompt)
|
||||||
return response
|
return response
|
||||||
|
|
||||||
|
|
||||||
|
class GeminiModel(AIModel):
|
||||||
|
def __init__(self, api_key:str, llm_model: str, llm_api_url: str):
|
||||||
|
from langchain_google_genai import ChatGoogleGenerativeAI
|
||||||
|
self.model = ChatGoogleGenerativeAI(model=llm_model, google_api_key=api_key)
|
||||||
|
|
||||||
|
def invoke(self, prompt: str) -> str:
|
||||||
|
response = self.model.invoke(prompt)
|
||||||
|
return response
|
||||||
|
|
||||||
|
|
||||||
class AIAdapter:
|
class AIAdapter:
|
||||||
def __init__(self, config: dict, api_key: str):
|
def __init__(self, config: dict, api_key: str):
|
||||||
self.model = self._create_model(config, api_key)
|
self.model = self._create_model(config, api_key)
|
||||||
|
|
@ -66,24 +80,27 @@ class AIAdapter:
|
||||||
llm_model_type = config['llm_model_type']
|
llm_model_type = config['llm_model_type']
|
||||||
llm_model = config['llm_model']
|
llm_model = config['llm_model']
|
||||||
llm_api_url = config['llm_api_url']
|
llm_api_url = config['llm_api_url']
|
||||||
print('Using {0} with {1} from {2}'.format(llm_model_type, llm_model, llm_api_url))
|
print('Using {0} with {1} from {2}'.format(
|
||||||
|
llm_model_type, llm_model, llm_api_url))
|
||||||
|
|
||||||
if llm_model_type == "openai":
|
if llm_model_type == "openai":
|
||||||
return OpenAIModel(api_key, llm_model, llm_api_url)
|
return OpenAIModel(api_key, llm_model, llm_api_url)
|
||||||
elif llm_model_type == "claude":
|
elif llm_model_type == "claude":
|
||||||
return ClaudeModel(api_key, llm_model, llm_api_url)
|
return ClaudeModel(api_key, llm_model, llm_api_url)
|
||||||
elif llm_model_type == "ollama":
|
elif llm_model_type == "ollama":
|
||||||
return OllamaModel(api_key, llm_model, llm_api_url)
|
return OllamaModel(api_key, llm_model, llm_api_url)
|
||||||
|
elif llm_model_type == "gemini":
|
||||||
|
return GeminiModel(api_key, llm_model, llm_api_url)
|
||||||
else:
|
else:
|
||||||
raise ValueError(f"Unsupported model type: {model_type}")
|
raise ValueError(f"Unsupported model type: {llm_model_type}")
|
||||||
|
|
||||||
def invoke(self, prompt: str) -> str:
|
def invoke(self, prompt: str) -> str:
|
||||||
return self.model.invoke(prompt)
|
return self.model.invoke(prompt)
|
||||||
|
|
||||||
|
|
||||||
class LLMLogger:
|
class LLMLogger:
|
||||||
|
|
||||||
|
def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]):
|
||||||
def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
|
|
||||||
|
|
||||||
self.llm = llm
|
self.llm = llm
|
||||||
logger.debug("LLMLogger successfully initialized with LLM: %s", llm)
|
logger.debug("LLMLogger successfully initialized with LLM: %s", llm)
|
||||||
|
|
@ -95,7 +112,8 @@ class LLMLogger:
|
||||||
logger.debug("Parsed reply received: %s", parsed_reply)
|
logger.debug("Parsed reply received: %s", parsed_reply)
|
||||||
|
|
||||||
try:
|
try:
|
||||||
calls_log = os.path.join(Path("data_folder/output"), "open_ai_calls.json")
|
calls_log = os.path.join(
|
||||||
|
Path("data_folder/output"), "open_ai_calls.json")
|
||||||
logger.debug("Logging path determined: %s", calls_log)
|
logger.debug("Logging path determined: %s", calls_log)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error("Error determining the log path: %s", str(e))
|
logger.error("Error determining the log path: %s", str(e))
|
||||||
|
|
@ -114,18 +132,22 @@ class LLMLogger:
|
||||||
}
|
}
|
||||||
logger.debug("Prompts converted to dictionary: %s", prompts)
|
logger.debug("Prompts converted to dictionary: %s", prompts)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error("Error converting prompts to dictionary: %s", str(e))
|
logger.error(
|
||||||
|
"Error converting prompts to dictionary: %s", str(e))
|
||||||
raise
|
raise
|
||||||
else:
|
else:
|
||||||
logger.debug("Prompts are of unknown type, attempting default conversion")
|
logger.debug(
|
||||||
|
"Prompts are of unknown type, attempting default conversion")
|
||||||
try:
|
try:
|
||||||
prompts = {
|
prompts = {
|
||||||
f"prompt_{i + 1}": prompt.content
|
f"prompt_{i + 1}": prompt.content
|
||||||
for i, prompt in enumerate(prompts.messages)
|
for i, prompt in enumerate(prompts.messages)
|
||||||
}
|
}
|
||||||
logger.debug("Prompts converted to dictionary using default method: %s", prompts)
|
logger.debug(
|
||||||
|
"Prompts converted to dictionary using default method: %s", prompts)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error("Error converting prompts using default method: %s", str(e))
|
logger.error(
|
||||||
|
"Error converting prompts using default method: %s", str(e))
|
||||||
raise
|
raise
|
||||||
|
|
||||||
try:
|
try:
|
||||||
|
|
@ -140,7 +162,8 @@ class LLMLogger:
|
||||||
output_tokens = token_usage["output_tokens"]
|
output_tokens = token_usage["output_tokens"]
|
||||||
input_tokens = token_usage["input_tokens"]
|
input_tokens = token_usage["input_tokens"]
|
||||||
total_tokens = token_usage["total_tokens"]
|
total_tokens = token_usage["total_tokens"]
|
||||||
logger.debug("Token usage - Input: %d, Output: %d, Total: %d", input_tokens, output_tokens, total_tokens)
|
logger.debug("Token usage - Input: %d, Output: %d, Total: %d",
|
||||||
|
input_tokens, output_tokens, total_tokens)
|
||||||
except KeyError as e:
|
except KeyError as e:
|
||||||
logger.error("KeyError in parsed_reply structure: %s", str(e))
|
logger.error("KeyError in parsed_reply structure: %s", str(e))
|
||||||
raise
|
raise
|
||||||
|
|
@ -155,7 +178,8 @@ class LLMLogger:
|
||||||
try:
|
try:
|
||||||
prompt_price_per_token = 0.00000015
|
prompt_price_per_token = 0.00000015
|
||||||
completion_price_per_token = 0.0000006
|
completion_price_per_token = 0.0000006
|
||||||
total_cost = (input_tokens * prompt_price_per_token) + (output_tokens * completion_price_per_token)
|
total_cost = (input_tokens * prompt_price_per_token) + \
|
||||||
|
(output_tokens * completion_price_per_token)
|
||||||
logger.debug("Total cost calculated: %f", total_cost)
|
logger.debug("Total cost calculated: %f", total_cost)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error("Error calculating total cost: %s", str(e))
|
logger.error("Error calculating total cost: %s", str(e))
|
||||||
|
|
@ -174,12 +198,14 @@ class LLMLogger:
|
||||||
}
|
}
|
||||||
logger.debug("Log entry created: %s", log_entry)
|
logger.debug("Log entry created: %s", log_entry)
|
||||||
except KeyError as e:
|
except KeyError as e:
|
||||||
logger.error("Error creating log entry: missing key %s in parsed_reply", str(e))
|
logger.error(
|
||||||
|
"Error creating log entry: missing key %s in parsed_reply", str(e))
|
||||||
raise
|
raise
|
||||||
|
|
||||||
try:
|
try:
|
||||||
with open(calls_log, "a", encoding="utf-8") as f:
|
with open(calls_log, "a", encoding="utf-8") as f:
|
||||||
json_string = json.dumps(log_entry, ensure_ascii=False, indent=4)
|
json_string = json.dumps(
|
||||||
|
log_entry, ensure_ascii=False, indent=4)
|
||||||
f.write(json_string + "\n")
|
f.write(json_string + "\n")
|
||||||
logger.debug("Log entry written to file: %s", calls_log)
|
logger.debug("Log entry written to file: %s", calls_log)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
|
|
@ -189,25 +215,25 @@ class LLMLogger:
|
||||||
|
|
||||||
class LoggerChatModel:
|
class LoggerChatModel:
|
||||||
|
|
||||||
|
def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]):
|
||||||
def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
|
|
||||||
|
|
||||||
self.llm = llm
|
self.llm = llm
|
||||||
logger.debug("LoggerChatModel successfully initialized with LLM: %s", llm)
|
logger.debug(
|
||||||
|
"LoggerChatModel successfully initialized with LLM: %s", llm)
|
||||||
|
|
||||||
def __call__(self, messages: List[Dict[str, str]]) -> str:
|
def __call__(self, messages: List[Dict[str, str]]) -> str:
|
||||||
|
|
||||||
logger.debug("Entering __call__ method with messages: %s", messages)
|
logger.debug("Entering __call__ method with messages: %s", messages)
|
||||||
while True:
|
while True:
|
||||||
try:
|
try:
|
||||||
logger.debug("Attempting to call the LLM with messages")
|
logger.debug("Attempting to call the LLM with messages")
|
||||||
reply = self.llm(messages)
|
|
||||||
|
reply = self.llm.invoke(messages)
|
||||||
logger.debug("LLM response received: %s", reply)
|
logger.debug("LLM response received: %s", reply)
|
||||||
|
|
||||||
parsed_reply = self.parse_llmresult(reply)
|
parsed_reply = self.parse_llmresult(reply)
|
||||||
logger.debug("Parsed LLM reply: %s", parsed_reply)
|
logger.debug("Parsed LLM reply: %s", parsed_reply)
|
||||||
|
|
||||||
LLMLogger.log_request(prompts=messages, parsed_reply=parsed_reply)
|
LLMLogger.log_request(
|
||||||
|
prompts=messages, parsed_reply=parsed_reply)
|
||||||
logger.debug("Request successfully logged")
|
logger.debug("Request successfully logged")
|
||||||
|
|
||||||
return reply
|
return reply
|
||||||
|
|
@ -243,11 +269,11 @@ class LoggerChatModel:
|
||||||
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error("Unexpected error occurred: %s", str(e))
|
logger.error("Unexpected error occurred: %s", str(e))
|
||||||
logger.info("Waiting for 30 seconds before retrying due to an unexpected error.")
|
logger.info(
|
||||||
|
"Waiting for 30 seconds before retrying due to an unexpected error.")
|
||||||
time.sleep(30)
|
time.sleep(30)
|
||||||
continue
|
continue
|
||||||
|
|
||||||
|
|
||||||
def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]:
|
def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]:
|
||||||
logger.debug("Parsing LLM result: %s", llmresult)
|
logger.debug("Parsing LLM result: %s", llmresult)
|
||||||
|
|
||||||
|
|
@ -277,11 +303,13 @@ class LoggerChatModel:
|
||||||
return parsed_result
|
return parsed_result
|
||||||
|
|
||||||
except KeyError as e:
|
except KeyError as e:
|
||||||
logger.error("KeyError while parsing LLM result: missing key %s", str(e))
|
logger.error(
|
||||||
|
"KeyError while parsing LLM result: missing key %s", str(e))
|
||||||
raise
|
raise
|
||||||
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error("Unexpected error while parsing LLM result: %s", str(e))
|
logger.error(
|
||||||
|
"Unexpected error while parsing LLM result: %s", str(e))
|
||||||
raise
|
raise
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -297,7 +325,8 @@ class GPTAnswerer:
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def find_best_match(text: str, options: list[str]) -> str:
|
def find_best_match(text: str, options: list[str]) -> str:
|
||||||
logger.debug("Finding best match for text: '%s' in options: %s", text, options)
|
logger.debug(
|
||||||
|
"Finding best match for text: '%s' in options: %s", text, options)
|
||||||
distances = [
|
distances = [
|
||||||
(option, distance(text.lower(), option.lower())) for option in options
|
(option, distance(text.lower(), option.lower())) for option in options
|
||||||
]
|
]
|
||||||
|
|
@ -323,10 +352,12 @@ class GPTAnswerer:
|
||||||
def set_job(self, job):
|
def set_job(self, job):
|
||||||
logger.debug("Setting job: %s", job)
|
logger.debug("Setting job: %s", job)
|
||||||
self.job = job
|
self.job = job
|
||||||
self.job.set_summarize_job_description(self.summarize_job_description(self.job.description))
|
self.job.set_summarize_job_description(
|
||||||
|
self.summarize_job_description(self.job.description))
|
||||||
|
|
||||||
def set_job_application_profile(self, job_application_profile):
|
def set_job_application_profile(self, job_application_profile):
|
||||||
logger.debug("Setting job application profile: %s", job_application_profile)
|
logger.debug("Setting job application profile: %s",
|
||||||
|
job_application_profile)
|
||||||
self.job_application_profile = job_application_profile
|
self.job_application_profile = job_application_profile
|
||||||
|
|
||||||
def summarize_job_description(self, text: str) -> str:
|
def summarize_job_description(self, text: str) -> str:
|
||||||
|
|
@ -334,7 +365,8 @@ class GPTAnswerer:
|
||||||
strings.summarize_prompt_template = self._preprocess_template_string(
|
strings.summarize_prompt_template = self._preprocess_template_string(
|
||||||
strings.summarize_prompt_template
|
strings.summarize_prompt_template
|
||||||
)
|
)
|
||||||
prompt = ChatPromptTemplate.from_template(strings.summarize_prompt_template)
|
prompt = ChatPromptTemplate.from_template(
|
||||||
|
strings.summarize_prompt_template)
|
||||||
chain = prompt | self.llm_cheap | StrOutputParser()
|
chain = prompt | self.llm_cheap | StrOutputParser()
|
||||||
output = chain.invoke({"text": text})
|
output = chain.invoke({"text": text})
|
||||||
logger.debug("Summary generated: %s", output)
|
logger.debug("Summary generated: %s", output)
|
||||||
|
|
@ -454,33 +486,40 @@ class GPTAnswerer:
|
||||||
chain = prompt | self.llm_cheap | StrOutputParser()
|
chain = prompt | self.llm_cheap | StrOutputParser()
|
||||||
output = chain.invoke({"question": question})
|
output = chain.invoke({"question": question})
|
||||||
|
|
||||||
match = re.search(r"(Personal information|Self Identification|Legal Authorization|Work Preferences|Education Details|Experience Details|Projects|Availability|Salary Expectations|Certifications|Languages|Interests|Cover letter)", output, re.IGNORECASE)
|
match = re.search(
|
||||||
|
r"(Personal information|Self Identification|Legal Authorization|Work Preferences|Education Details|Experience Details|Projects|Availability|Salary Expectations|Certifications|Languages|Interests|Cover letter)",
|
||||||
|
output, re.IGNORECASE)
|
||||||
if not match:
|
if not match:
|
||||||
raise ValueError("Could not extract section name from the response.")
|
raise ValueError(
|
||||||
|
"Could not extract section name from the response.")
|
||||||
|
|
||||||
section_name = match.group(1).lower().replace(" ", "_")
|
section_name = match.group(1).lower().replace(" ", "_")
|
||||||
|
|
||||||
if section_name == "cover_letter":
|
if section_name == "cover_letter":
|
||||||
chain = chains.get(section_name)
|
chain = chains.get(section_name)
|
||||||
output = chain.invoke({"resume": self.resume, "job_description": self.job_description})
|
output = chain.invoke(
|
||||||
|
{"resume": self.resume, "job_description": self.job_description})
|
||||||
logger.debug("Cover letter generated: %s", output)
|
logger.debug("Cover letter generated: %s", output)
|
||||||
return output
|
return output
|
||||||
resume_section = getattr(self.resume, section_name, None) or getattr(self.job_application_profile, section_name,
|
resume_section = getattr(self.resume, section_name, None) or getattr(self.job_application_profile, section_name,
|
||||||
None)
|
None)
|
||||||
if resume_section is None:
|
if resume_section is None:
|
||||||
logger.error("Section '%s' not found in either resume or job_application_profile.", section_name)
|
logger.error(
|
||||||
|
"Section '%s' not found in either resume or job_application_profile.", section_name)
|
||||||
raise ValueError(f"Section '{section_name}' not found in either resume or job_application_profile.")
|
raise ValueError(f"Section '{section_name}' not found in either resume or job_application_profile.")
|
||||||
chain = chains.get(section_name)
|
chain = chains.get(section_name)
|
||||||
if chain is None:
|
if chain is None:
|
||||||
logger.error("Chain not defined for section '%s'", section_name)
|
logger.error("Chain not defined for section '%s'", section_name)
|
||||||
raise ValueError(f"Chain not defined for section '{section_name}'")
|
raise ValueError(f"Chain not defined for section '{section_name}'")
|
||||||
output = chain.invoke({"resume_section": resume_section, "question": question})
|
output = chain.invoke(
|
||||||
|
{"resume_section": resume_section, "question": question})
|
||||||
logger.debug("Question answered: %s", output)
|
logger.debug("Question answered: %s", output)
|
||||||
return output
|
return output
|
||||||
|
|
||||||
def answer_question_numeric(self, question: str, default_experience: int = 3) -> int:
|
def answer_question_numeric(self, question: str, default_experience: int = 3) -> int:
|
||||||
logger.debug("Answering numeric question: %s", question)
|
logger.debug("Answering numeric question: %s", question)
|
||||||
func_template = self._preprocess_template_string(strings.numeric_question_template)
|
func_template = self._preprocess_template_string(
|
||||||
|
strings.numeric_question_template)
|
||||||
prompt = ChatPromptTemplate.from_template(func_template)
|
prompt = ChatPromptTemplate.from_template(func_template)
|
||||||
chain = prompt | self.llm_cheap | StrOutputParser()
|
chain = prompt | self.llm_cheap | StrOutputParser()
|
||||||
output_str = chain.invoke(
|
output_str = chain.invoke(
|
||||||
|
|
@ -491,7 +530,8 @@ class GPTAnswerer:
|
||||||
output = self.extract_number_from_string(output_str)
|
output = self.extract_number_from_string(output_str)
|
||||||
logger.debug("Extracted number: %d", output)
|
logger.debug("Extracted number: %d", output)
|
||||||
except ValueError:
|
except ValueError:
|
||||||
logger.warning("Failed to extract number, using default experience: %d", default_experience)
|
logger.warning(
|
||||||
|
"Failed to extract number, using default experience: %d", default_experience)
|
||||||
output = default_experience
|
output = default_experience
|
||||||
return output
|
return output
|
||||||
|
|
||||||
|
|
@ -507,17 +547,20 @@ class GPTAnswerer:
|
||||||
|
|
||||||
def answer_question_from_options(self, question: str, options: list[str]) -> str:
|
def answer_question_from_options(self, question: str, options: list[str]) -> str:
|
||||||
logger.debug("Answering question from options: %s", question)
|
logger.debug("Answering question from options: %s", question)
|
||||||
func_template = self._preprocess_template_string(strings.options_template)
|
func_template = self._preprocess_template_string(
|
||||||
|
strings.options_template)
|
||||||
prompt = ChatPromptTemplate.from_template(func_template)
|
prompt = ChatPromptTemplate.from_template(func_template)
|
||||||
chain = prompt | self.llm_cheap | StrOutputParser()
|
chain = prompt | self.llm_cheap | StrOutputParser()
|
||||||
output_str = chain.invoke({"resume": self.resume, "question": question, "options": options})
|
output_str = chain.invoke(
|
||||||
|
{"resume": self.resume, "question": question, "options": options})
|
||||||
logger.debug("Raw output for options question: %s", output_str)
|
logger.debug("Raw output for options question: %s", output_str)
|
||||||
best_option = self.find_best_match(output_str, options)
|
best_option = self.find_best_match(output_str, options)
|
||||||
logger.debug("Best option determined: %s", best_option)
|
logger.debug("Best option determined: %s", best_option)
|
||||||
return best_option
|
return best_option
|
||||||
|
|
||||||
def resume_or_cover(self, phrase: str) -> str:
|
def resume_or_cover(self, phrase: str) -> str:
|
||||||
logger.debug("Determining if phrase refers to resume or cover letter: %s", phrase)
|
logger.debug(
|
||||||
|
"Determining if phrase refers to resume or cover letter: %s", phrase)
|
||||||
prompt_template = """
|
prompt_template = """
|
||||||
Given the following phrase, respond with only 'resume' if the phrase is about a resume, or 'cover' if it's about a cover letter.
|
Given the following phrase, respond with only 'resume' if the phrase is about a resume, or 'cover' if it's about a cover letter.
|
||||||
If the phrase contains only one word 'upload', consider it as 'cover'.
|
If the phrase contains only one word 'upload', consider it as 'cover'.
|
||||||
|
|
|
||||||
|
|
@ -8,7 +8,7 @@ import traceback
|
||||||
from typing import List, Optional, Any, Tuple
|
from typing import List, Optional, Any, Tuple
|
||||||
|
|
||||||
from httpx import HTTPStatusError
|
from httpx import HTTPStatusError
|
||||||
from reportlab.lib.pagesizes import letter
|
from reportlab.lib.pagesizes import A4
|
||||||
from reportlab.pdfgen import canvas
|
from reportlab.pdfgen import canvas
|
||||||
from selenium.common.exceptions import NoSuchElementException, TimeoutException
|
from selenium.common.exceptions import NoSuchElementException, TimeoutException
|
||||||
from selenium.webdriver import ActionChains
|
from selenium.webdriver import ActionChains
|
||||||
|
|
@ -37,7 +37,6 @@ class LinkedInEasyApplier:
|
||||||
|
|
||||||
logger.debug("LinkedInEasyApplier initialized successfully")
|
logger.debug("LinkedInEasyApplier initialized successfully")
|
||||||
|
|
||||||
|
|
||||||
def _load_questions_from_json(self) -> List[dict]:
|
def _load_questions_from_json(self) -> List[dict]:
|
||||||
output_file = 'answers.json'
|
output_file = 'answers.json'
|
||||||
logger.debug("Loading questions from JSON file: %s", output_file)
|
logger.debug("Loading questions from JSON file: %s", output_file)
|
||||||
|
|
@ -60,10 +59,8 @@ class LinkedInEasyApplier:
|
||||||
logger.error("Error loading questions data from JSON file: %s", tb_str)
|
logger.error("Error loading questions data from JSON file: %s", tb_str)
|
||||||
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 check_for_premium_redirect(self, job: Any, max_attempts=3):
|
def check_for_premium_redirect(self, job: Any, max_attempts=3):
|
||||||
"""Проверяет, был ли выполнен редирект на страницу LinkedIn Premium.
|
|
||||||
В случае редиректа возвращает пользователя на исходную страницу вакансии."""
|
|
||||||
current_url = self.driver.current_url
|
current_url = self.driver.current_url
|
||||||
attempts = 0
|
attempts = 0
|
||||||
|
|
||||||
|
|
@ -80,7 +77,6 @@ class LinkedInEasyApplier:
|
||||||
raise Exception(
|
raise Exception(
|
||||||
f"Redirected to LinkedIn Premium page and failed to return after {max_attempts} attempts. Job application aborted.")
|
f"Redirected to LinkedIn Premium page and failed to return after {max_attempts} attempts. Job application aborted.")
|
||||||
|
|
||||||
|
|
||||||
def job_apply(self, job: Any):
|
def job_apply(self, job: Any):
|
||||||
logger.debug("Starting job application for job: %s", job)
|
logger.debug("Starting job application for job: %s", job)
|
||||||
|
|
||||||
|
|
@ -167,7 +163,7 @@ class LinkedInEasyApplier:
|
||||||
logger.debug(f"Attempting search using {method['description']}")
|
logger.debug(f"Attempting search using {method['description']}")
|
||||||
|
|
||||||
if method.get('find_elements'):
|
if method.get('find_elements'):
|
||||||
# Поиск всех кнопок "Easy Apply"
|
|
||||||
buttons = self.driver.find_elements(By.XPATH, method['xpath'])
|
buttons = self.driver.find_elements(By.XPATH, method['xpath'])
|
||||||
if buttons:
|
if buttons:
|
||||||
for index, button in enumerate(buttons):
|
for index, button in enumerate(buttons):
|
||||||
|
|
@ -209,7 +205,6 @@ class LinkedInEasyApplier:
|
||||||
logger.error("No clickable 'Easy Apply' button found after 2 attempts. Page source:\n%s", page_source)
|
logger.error("No clickable 'Easy Apply' button found after 2 attempts. Page source:\n%s", page_source)
|
||||||
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:
|
||||||
logger.debug("Getting job description")
|
logger.debug("Getting job description")
|
||||||
try:
|
try:
|
||||||
|
|
@ -514,11 +509,47 @@ class LinkedInEasyApplier:
|
||||||
file_path_pdf = os.path.join(folder_path, f"Cover_Letter_{timestamp}.pdf")
|
file_path_pdf = os.path.join(folder_path, f"Cover_Letter_{timestamp}.pdf")
|
||||||
logger.debug(f"Generated file path for cover letter: {file_path_pdf}")
|
logger.debug(f"Generated file path for cover letter: {file_path_pdf}")
|
||||||
|
|
||||||
c = canvas.Canvas(file_path_pdf, pagesize=letter)
|
c = canvas.Canvas(file_path_pdf, pagesize=A4)
|
||||||
_, height = letter
|
page_width, page_height = A4
|
||||||
text_object = c.beginText(100, height - 100)
|
text_object = c.beginText(50, page_height - 50)
|
||||||
text_object.setFont("Helvetica", 12)
|
text_object.setFont("Helvetica", 12)
|
||||||
text_object.textLines(cover_letter_text)
|
|
||||||
|
max_width = page_width - 100
|
||||||
|
bottom_margin = 50
|
||||||
|
available_height = page_height - bottom_margin - 50
|
||||||
|
|
||||||
|
def split_text_by_width(text, font, font_size, max_width):
|
||||||
|
wrapped_lines = []
|
||||||
|
for line in text.splitlines():
|
||||||
|
|
||||||
|
if utils.stringWidth(line, font, font_size) > max_width:
|
||||||
|
words = line.split()
|
||||||
|
new_line = ""
|
||||||
|
for word in words:
|
||||||
|
if utils.stringWidth(new_line + word + " ", font, font_size) <= max_width:
|
||||||
|
new_line += word + " "
|
||||||
|
else:
|
||||||
|
wrapped_lines.append(new_line.strip())
|
||||||
|
new_line = word + " "
|
||||||
|
wrapped_lines.append(new_line.strip())
|
||||||
|
else:
|
||||||
|
wrapped_lines.append(line)
|
||||||
|
return wrapped_lines
|
||||||
|
|
||||||
|
lines = split_text_by_width(cover_letter_text, "Helvetica", 12, max_width)
|
||||||
|
|
||||||
|
for line in lines:
|
||||||
|
text_height = text_object.getY()
|
||||||
|
if text_height > bottom_margin:
|
||||||
|
text_object.textLine(line)
|
||||||
|
else:
|
||||||
|
|
||||||
|
c.drawText(text_object)
|
||||||
|
c.showPage()
|
||||||
|
text_object = c.beginText(50, page_height - 50)
|
||||||
|
text_object.setFont("Helvetica", 12)
|
||||||
|
text_object.textLine(line)
|
||||||
|
|
||||||
c.drawText(text_object)
|
c.drawText(text_object)
|
||||||
c.save()
|
c.save()
|
||||||
logger.debug(f"Cover letter successfully generated and saved to: {file_path_pdf}")
|
logger.debug(f"Cover letter successfully generated and saved to: {file_path_pdf}")
|
||||||
|
|
@ -632,7 +663,6 @@ class LinkedInEasyApplier:
|
||||||
|
|
||||||
for item in self.all_data:
|
for item in self.all_data:
|
||||||
|
|
||||||
|
|
||||||
logger.debug(
|
logger.debug(
|
||||||
f"Comparing sanitized stored question: '{self._sanitize_text(item['question'])}' and type: '{item.get('type')}' with current question: '{self._sanitize_text(question_text)}' and type: '{question_type}'")
|
f"Comparing sanitized stored question: '{self._sanitize_text(item['question'])}' and type: '{item.get('type')}' with current question: '{self._sanitize_text(question_text)}' and type: '{question_type}'")
|
||||||
|
|
||||||
|
|
@ -659,7 +689,6 @@ class LinkedInEasyApplier:
|
||||||
answer = self.gpt_answerer.answer_question_textual_wide_range(question_text)
|
answer = self.gpt_answerer.answer_question_textual_wide_range(question_text)
|
||||||
logger.debug(f"Generated textual answer: {answer}")
|
logger.debug(f"Generated textual answer: {answer}")
|
||||||
|
|
||||||
|
|
||||||
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)
|
||||||
logger.debug("Entered new answer into the textbox and saved it to JSON.")
|
logger.debug("Entered new answer into the textbox and saved it to JSON.")
|
||||||
|
|
@ -692,7 +721,6 @@ class LinkedInEasyApplier:
|
||||||
logger.debug("Entered existing date answer")
|
logger.debug("Entered existing date answer")
|
||||||
return True
|
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)
|
||||||
logger.debug("Entered new date answer")
|
logger.debug("Entered new date answer")
|
||||||
|
|
@ -701,12 +729,12 @@ class LinkedInEasyApplier:
|
||||||
|
|
||||||
def _find_and_handle_dropdown_question(self, section: WebElement) -> bool:
|
def _find_and_handle_dropdown_question(self, section: WebElement) -> bool:
|
||||||
try:
|
try:
|
||||||
|
|
||||||
question = section.find_element(By.CLASS_NAME, 'jobs-easy-apply-form-element')
|
question = section.find_element(By.CLASS_NAME, 'jobs-easy-apply-form-element')
|
||||||
question_text = question.find_element(By.TAG_NAME, 'label').text.lower()
|
|
||||||
logger.debug(f"Processing dropdown or combobox question: {question_text}")
|
|
||||||
|
|
||||||
dropdowns = question.find_elements(By.TAG_NAME, 'select')
|
dropdowns = question.find_elements(By.TAG_NAME, 'select')
|
||||||
|
if not dropdowns:
|
||||||
|
dropdowns = section.find_elements(By.CSS_SELECTOR, '[data-test-text-entity-list-form-select]')
|
||||||
|
|
||||||
if dropdowns:
|
if dropdowns:
|
||||||
dropdown = dropdowns[0]
|
dropdown = dropdowns[0]
|
||||||
select = Select(dropdown)
|
select = Select(dropdown)
|
||||||
|
|
@ -714,6 +742,9 @@ class LinkedInEasyApplier:
|
||||||
|
|
||||||
logger.debug(f"Dropdown options found: {options}")
|
logger.debug(f"Dropdown options found: {options}")
|
||||||
|
|
||||||
|
question_text = question.find_element(By.TAG_NAME, 'label').text.lower()
|
||||||
|
logger.debug(f"Processing dropdown or combobox question: {question_text}")
|
||||||
|
|
||||||
current_selection = select.first_selected_option.text
|
current_selection = select.first_selected_option.text
|
||||||
logger.debug(f"Current selection: {current_selection}")
|
logger.debug(f"Current selection: {current_selection}")
|
||||||
|
|
||||||
|
|
@ -738,9 +769,15 @@ class LinkedInEasyApplier:
|
||||||
logger.debug(f"Selected new dropdown answer: {answer}")
|
logger.debug(f"Selected new dropdown answer: {answer}")
|
||||||
return True
|
return True
|
||||||
|
|
||||||
return False
|
else:
|
||||||
|
|
||||||
|
logger.debug(f"No dropdown found. Logging elements for debugging.")
|
||||||
|
elements = section.find_elements(By.XPATH, ".//*")
|
||||||
|
logger.debug(f"Elements found: {[element.tag_name for element in elements]}")
|
||||||
|
return False
|
||||||
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.warning(f"Failed to handle dropdown or combobox question: {e}")
|
logger.warning(f"Failed to handle dropdown or combobox question: {e}", exc_info=True)
|
||||||
return False
|
return False
|
||||||
|
|
||||||
def _is_numeric_field(self, field: WebElement) -> bool:
|
def _is_numeric_field(self, field: WebElement) -> bool:
|
||||||
|
|
|
||||||
|
|
@ -5,6 +5,7 @@ import time
|
||||||
from itertools import product
|
from itertools import product
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
|
||||||
|
from inputimeout import inputimeout, TimeoutOccurred
|
||||||
from selenium.common.exceptions import NoSuchElementException
|
from selenium.common.exceptions import NoSuchElementException
|
||||||
from selenium.webdriver.common.by import By
|
from selenium.webdriver.common.by import By
|
||||||
|
|
||||||
|
|
@ -45,13 +46,18 @@ class LinkedInJobManager:
|
||||||
|
|
||||||
def set_parameters(self, parameters):
|
def set_parameters(self, parameters):
|
||||||
logger.debug("Setting parameters for LinkedInJobManager")
|
logger.debug("Setting parameters for LinkedInJobManager")
|
||||||
self.company_blacklist = parameters.get('companyBlacklist', []) or []
|
self.company_blacklist = parameters.get('company_blacklist', []) or []
|
||||||
self.title_blacklist = parameters.get('titleBlacklist', []) or []
|
self.title_blacklist = parameters.get('title_blacklist', []) or []
|
||||||
self.positions = parameters.get('positions', [])
|
self.positions = parameters.get('positions', [])
|
||||||
self.locations = parameters.get('locations', [])
|
self.locations = parameters.get('locations', [])
|
||||||
self.apply_once_at_company = parameters.get('applyOnceAtCompany', False)
|
self.apply_once_at_company = parameters.get('apply_once_at_company', False)
|
||||||
self.base_search_url = self.get_base_search_url(parameters)
|
self.base_search_url = self.get_base_search_url(parameters)
|
||||||
self.seen_jobs = []
|
self.seen_jobs = []
|
||||||
|
|
||||||
|
job_applicants_threshold = parameters.get('job_applicants_threshold', {})
|
||||||
|
self.min_applicants = job_applicants_threshold.get('min_applicants', 0)
|
||||||
|
self.max_applicants = job_applicants_threshold.get('max_applicants', float('inf'))
|
||||||
|
|
||||||
resume_path = parameters.get('uploads', {}).get('resume', None)
|
resume_path = parameters.get('uploads', {}).get('resume', None)
|
||||||
self.resume_path = Path(resume_path) if resume_path and Path(resume_path).exists() else None
|
self.resume_path = Path(resume_path) if resume_path and Path(resume_path).exists() else None
|
||||||
self.output_file_directory = Path(parameters['outputFileDirectory'])
|
self.output_file_directory = Path(parameters['outputFileDirectory'])
|
||||||
|
|
@ -109,32 +115,80 @@ class LinkedInJobManager:
|
||||||
utils.printyellow("Applying to jobs on this page has been completed!")
|
utils.printyellow("Applying to jobs on this page has been completed!")
|
||||||
|
|
||||||
time_left = minimum_page_time - time.time()
|
time_left = minimum_page_time - time.time()
|
||||||
|
|
||||||
|
# Ask user if they want to skip waiting, with timeout
|
||||||
if time_left > 0:
|
if time_left > 0:
|
||||||
utils.printyellow(f"Sleeping for {time_left} seconds.")
|
try:
|
||||||
logger.debug("Sleeping for %d seconds", time_left)
|
user_input = inputimeout(
|
||||||
time.sleep(time_left)
|
prompt=f"Sleeping for {time_left} seconds. Press 'y' to skip waiting. Timeout 60 seconds : ",
|
||||||
minimum_page_time = time.time() + minimum_time
|
timeout=60).strip().lower()
|
||||||
|
except TimeoutOccurred:
|
||||||
|
user_input = '' # No input after timeout
|
||||||
|
if user_input == 'y':
|
||||||
|
logger.debug("User chose to skip waiting.")
|
||||||
|
utils.printyellow("User skipped waiting.")
|
||||||
|
else:
|
||||||
|
logger.debug(f"Sleeping for {time_left} seconds as user chose not to skip.")
|
||||||
|
utils.printyellow(f"Sleeping for {time_left} seconds.")
|
||||||
|
time.sleep(time_left)
|
||||||
|
|
||||||
|
minimum_page_time = time.time() + minimum_time
|
||||||
|
|
||||||
if page_sleep % 5 == 0:
|
if page_sleep % 5 == 0:
|
||||||
sleep_time = random.randint(5, 34)
|
sleep_time = random.randint(5, 34)
|
||||||
utils.printyellow(f"Sleeping for {sleep_time / 60} minutes.")
|
try:
|
||||||
logger.debug("Sleeping for %d seconds", sleep_time)
|
user_input = inputimeout(
|
||||||
time.sleep(sleep_time)
|
prompt=f"Sleeping for {sleep_time / 60} minutes. Press 'y' to skip waiting. Timeout 60 seconds : ",
|
||||||
|
timeout=60).strip().lower()
|
||||||
|
except TimeoutOccurred:
|
||||||
|
user_input = '' # No input after timeout
|
||||||
|
if user_input == 'y':
|
||||||
|
logger.debug("User chose to skip waiting.")
|
||||||
|
utils.printyellow("User skipped waiting.")
|
||||||
|
else:
|
||||||
|
logger.debug(f"Sleeping for {sleep_time} seconds.")
|
||||||
|
utils.printyellow(f"Sleeping for {sleep_time / 60} minutes.")
|
||||||
|
time.sleep(sleep_time)
|
||||||
page_sleep += 1
|
page_sleep += 1
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error("Unexpected error during job search: %s", e)
|
logger.error("Unexpected error during job search: %s", e)
|
||||||
utils.printred(f"Unexpected error: {e}")
|
utils.printred(f"Unexpected error: {e}")
|
||||||
continue
|
continue
|
||||||
|
|
||||||
time_left = minimum_page_time - time.time()
|
time_left = minimum_page_time - time.time()
|
||||||
|
|
||||||
if time_left > 0:
|
if time_left > 0:
|
||||||
utils.printyellow(f"Sleeping for {time_left} seconds.")
|
try:
|
||||||
logger.debug("Sleeping for %d seconds", time_left)
|
user_input = inputimeout(
|
||||||
time.sleep(time_left)
|
prompt=f"Sleeping for {time_left} seconds. Press 'y' to skip waiting. Timeout 60 seconds : ",
|
||||||
minimum_page_time = time.time() + minimum_time
|
timeout=60).strip().lower()
|
||||||
|
except TimeoutOccurred:
|
||||||
|
user_input = '' # No input after timeout
|
||||||
|
if user_input == 'y':
|
||||||
|
logger.debug("User chose to skip waiting.")
|
||||||
|
utils.printyellow("User skipped waiting.")
|
||||||
|
else:
|
||||||
|
logger.debug(f"Sleeping for {time_left} seconds as user chose not to skip.")
|
||||||
|
utils.printyellow(f"Sleeping for {time_left} seconds.")
|
||||||
|
time.sleep(time_left)
|
||||||
|
|
||||||
|
minimum_page_time = time.time() + minimum_time
|
||||||
|
|
||||||
if page_sleep % 5 == 0:
|
if page_sleep % 5 == 0:
|
||||||
sleep_time = random.randint(50, 90)
|
sleep_time = random.randint(50, 90)
|
||||||
utils.printyellow(f"Sleeping for {sleep_time / 60} minutes.")
|
try:
|
||||||
logger.debug("Sleeping for %d seconds", sleep_time)
|
user_input = inputimeout(
|
||||||
time.sleep(sleep_time)
|
prompt=f"Sleeping for {sleep_time / 60} minutes. Press 'y' to skip waiting: ",
|
||||||
|
timeout=60).strip().lower()
|
||||||
|
except TimeoutOccurred:
|
||||||
|
user_input = '' # No input after timeout
|
||||||
|
if user_input == 'y':
|
||||||
|
logger.debug("User chose to skip waiting.")
|
||||||
|
utils.printyellow("User skipped waiting.")
|
||||||
|
else:
|
||||||
|
logger.debug(f"Sleeping for {sleep_time} seconds.")
|
||||||
|
utils.printyellow(f"Sleeping for {sleep_time / 60} minutes.")
|
||||||
|
time.sleep(sleep_time)
|
||||||
page_sleep += 1
|
page_sleep += 1
|
||||||
|
|
||||||
def get_jobs_from_page(self):
|
def get_jobs_from_page(self):
|
||||||
|
|
@ -183,16 +237,82 @@ class LinkedInJobManager:
|
||||||
pass
|
pass
|
||||||
|
|
||||||
job_results = self.driver.find_element(By.CLASS_NAME, "jobs-search-results-list")
|
job_results = self.driver.find_element(By.CLASS_NAME, "jobs-search-results-list")
|
||||||
utils.scroll_slow(self.driver, job_results)
|
# utils.scroll_slow(self.driver, job_results)
|
||||||
utils.scroll_slow(self.driver, job_results, step=300, reverse=True)
|
# utils.scroll_slow(self.driver, job_results, step=300, reverse=True)
|
||||||
|
|
||||||
job_list_elements = self.driver.find_elements(By.CLASS_NAME, 'scaffold-layout__list-container')[
|
job_list_elements = self.driver.find_elements(By.CLASS_NAME, 'scaffold-layout__list-container')[
|
||||||
0].find_elements(By.CLASS_NAME, 'jobs-search-results__list-item')
|
0].find_elements(By.CLASS_NAME, 'jobs-search-results__list-item')
|
||||||
|
|
||||||
if not job_list_elements:
|
if not job_list_elements:
|
||||||
utils.printyellow("No job class elements found on page, moving to next page.")
|
utils.printyellow("No job class elements found on page, moving to next page.")
|
||||||
logger.debug("No job class elements found on page, skipping")
|
logger.debug("No job class elements found on page, skipping")
|
||||||
return
|
return
|
||||||
|
|
||||||
job_list = [Job(*self.extract_job_information_from_tile(job_element)) for job_element in job_list_elements]
|
job_list = [Job(*self.extract_job_information_from_tile(job_element)) for job_element in job_list_elements]
|
||||||
|
|
||||||
for job in job_list:
|
for job in job_list:
|
||||||
|
|
||||||
|
try:
|
||||||
|
logger.debug(f"Starting applicant count search for job: {job.title} at {job.company}")
|
||||||
|
|
||||||
|
# Find all job insight elements
|
||||||
|
job_insight_elements = self.driver.find_elements(By.CLASS_NAME,
|
||||||
|
"job-details-jobs-unified-top-card__job-insight")
|
||||||
|
logger.debug(f"Found {len(job_insight_elements)} job insight elements")
|
||||||
|
|
||||||
|
# Initialize applicants_count as None
|
||||||
|
applicants_count = None
|
||||||
|
|
||||||
|
# Iterate over each job insight element to find the one containing the word "applicant"
|
||||||
|
for element in job_insight_elements:
|
||||||
|
logger.debug(f"Checking element text: {element.text}")
|
||||||
|
if "applicant" in element.text.lower():
|
||||||
|
# Found an element containing "applicant"
|
||||||
|
applicants_text = element.text.strip()
|
||||||
|
logger.debug(f"Applicants text found: {applicants_text}")
|
||||||
|
|
||||||
|
# Extract numeric digits from the text (e.g., "70 applicants" -> "70")
|
||||||
|
applicants_count = ''.join(filter(str.isdigit, applicants_text))
|
||||||
|
logger.debug(f"Extracted applicants count: {applicants_count}")
|
||||||
|
|
||||||
|
if applicants_count:
|
||||||
|
if "over" in applicants_text.lower():
|
||||||
|
applicants_count = int(applicants_count) + 1 # Handle "over X applicants"
|
||||||
|
logger.debug(f"Applicants count adjusted for 'over': {applicants_count}")
|
||||||
|
else:
|
||||||
|
applicants_count = int(applicants_count) # Convert the extracted number to an integer
|
||||||
|
break
|
||||||
|
|
||||||
|
# Check if applicants_count is valid (not None) before performing comparisons
|
||||||
|
if applicants_count is not None:
|
||||||
|
# Perform the threshold check for applicants count
|
||||||
|
if applicants_count < self.min_applicants or applicants_count > self.max_applicants:
|
||||||
|
utils.printyellow(
|
||||||
|
f"Skipping {job.title} at {job.company} due to applicants count: {applicants_count}")
|
||||||
|
logger.debug(f"Skipping {job.title} at {job.company}, applicants count: {applicants_count}")
|
||||||
|
self.write_to_file(job, "skipped_due_to_applicants")
|
||||||
|
continue # Skip this job if applicants count is outside the threshold
|
||||||
|
else:
|
||||||
|
logger.debug(f"Applicants count {applicants_count} is within the threshold")
|
||||||
|
else:
|
||||||
|
# If no applicants count was found, log a warning but continue the process
|
||||||
|
logger.warning(
|
||||||
|
f"Applicants count not found for {job.title} at {job.company}, continuing with application.")
|
||||||
|
except NoSuchElementException:
|
||||||
|
# Log a warning if the job insight elements are not found, but do not stop the job application process
|
||||||
|
logger.warning(
|
||||||
|
f"Applicants count elements not found for {job.title} at {job.company}, continuing with application.")
|
||||||
|
except ValueError as e:
|
||||||
|
# Handle errors when parsing the applicants count
|
||||||
|
logger.error(f"Error parsing applicants count for {job.title} at {job.company}: {e}")
|
||||||
|
except Exception as e:
|
||||||
|
# Catch any other exceptions to ensure the process continues
|
||||||
|
logger.error(
|
||||||
|
f"Unexpected error during applicants count processing for {job.title} at {job.company}: {e}")
|
||||||
|
|
||||||
|
# Continue with the job application process regardless of the applicants count check
|
||||||
|
logger.debug(f"Continuing with job application for {job.title} at {job.company}")
|
||||||
|
|
||||||
if self.is_blacklisted(job.title, job.company, job.link):
|
if self.is_blacklisted(job.title, job.company, job.link):
|
||||||
utils.printyellow(f"Blacklisted {job.title} at {job.company}, skipping...")
|
utils.printyellow(f"Blacklisted {job.title} at {job.company}, skipping...")
|
||||||
logger.debug("Job blacklisted: %s at %s", job.title, job.company)
|
logger.debug("Job blacklisted: %s at %s", job.title, job.company)
|
||||||
|
|
@ -200,7 +320,7 @@ class LinkedInJobManager:
|
||||||
continue
|
continue
|
||||||
if self.is_already_applied_to_job(job.title, job.company, job.link):
|
if self.is_already_applied_to_job(job.title, job.company, job.link):
|
||||||
self.write_to_file(job, "skipped")
|
self.write_to_file(job, "skipped")
|
||||||
continue
|
continue
|
||||||
if self.is_already_applied_to_company(job.company):
|
if self.is_already_applied_to_company(job.company):
|
||||||
self.write_to_file(job, "skipped")
|
self.write_to_file(job, "skipped")
|
||||||
continue
|
continue
|
||||||
|
|
@ -250,7 +370,7 @@ class LinkedInJobManager:
|
||||||
url_parts = []
|
url_parts = []
|
||||||
if parameters['remote']:
|
if parameters['remote']:
|
||||||
url_parts.append("f_CF=f_WRA")
|
url_parts.append("f_CF=f_WRA")
|
||||||
experience_levels = [str(i + 1) for i, (level, v) in enumerate(parameters.get('experienceLevel', {}).items()) if
|
experience_levels = [str(i + 1) for i, (level, v) in enumerate(parameters.get('experience_level', {}).items()) if
|
||||||
v]
|
v]
|
||||||
if experience_levels:
|
if experience_levels:
|
||||||
url_parts.append(f"f_E={','.join(experience_levels)}")
|
url_parts.append(f"f_E={','.join(experience_levels)}")
|
||||||
|
|
@ -307,10 +427,8 @@ class LinkedInJobManager:
|
||||||
title_blacklisted = any(word in job_title_words for word in self.title_blacklist)
|
title_blacklisted = any(word in job_title_words for word in self.title_blacklist)
|
||||||
company_blacklisted = company.strip().lower() in (word.strip().lower() for word in self.company_blacklist)
|
company_blacklisted = company.strip().lower() in (word.strip().lower() for word in self.company_blacklist)
|
||||||
link_seen = link in self.seen_jobs
|
link_seen = link in self.seen_jobs
|
||||||
|
|
||||||
is_blacklisted = title_blacklisted or company_blacklisted or link_seen
|
is_blacklisted = title_blacklisted or company_blacklisted or link_seen
|
||||||
logger.debug("Job blacklisted status: %s", is_blacklisted)
|
logger.debug("Job blacklisted status: %s", is_blacklisted)
|
||||||
return is_blacklisted
|
|
||||||
|
|
||||||
return title_blacklisted or company_blacklisted or link_seen
|
return title_blacklisted or company_blacklisted or link_seen
|
||||||
|
|
||||||
|
|
@ -322,8 +440,8 @@ class LinkedInJobManager:
|
||||||
|
|
||||||
def is_already_applied_to_company(self, company):
|
def is_already_applied_to_company(self, company):
|
||||||
if not self.apply_once_at_company:
|
if not self.apply_once_at_company:
|
||||||
return False
|
return False
|
||||||
|
|
||||||
output_files = ["success.json"]
|
output_files = ["success.json"]
|
||||||
for file_name in output_files:
|
for file_name in output_files:
|
||||||
file_path = self.output_file_directory / file_name
|
file_path = self.output_file_directory / file_name
|
||||||
|
|
@ -333,9 +451,9 @@ class LinkedInJobManager:
|
||||||
existing_data = json.load(f)
|
existing_data = json.load(f)
|
||||||
for applied_job in existing_data:
|
for applied_job in existing_data:
|
||||||
if applied_job['company'].strip().lower() == company.strip().lower():
|
if applied_job['company'].strip().lower() == company.strip().lower():
|
||||||
utils.printyellow(f"Already applied at {company} (once per company policy), skipping...")
|
utils.printyellow(
|
||||||
|
f"Already applied at {company} (once per company policy), skipping...")
|
||||||
return True
|
return True
|
||||||
except json.JSONDecodeError:
|
except json.JSONDecodeError:
|
||||||
continue
|
continue
|
||||||
return False
|
return False
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,58 +1,66 @@
|
||||||
|
<<<<<<< HEAD
|
||||||
from typing import Dict, List
|
from typing import Dict, List
|
||||||
from linkedin_api import Linkedin
|
from linkedin_api import Linkedin
|
||||||
from typing import Optional, Union, Literal
|
from typing import Optional, Union, Literal
|
||||||
from urllib.parse import quote, urlencode, parse_qs, urlparse
|
from urllib.parse import quote, urlencode, parse_qs, urlparse
|
||||||
|
=======
|
||||||
|
>>>>>>> upstream/v3
|
||||||
import logging
|
import logging
|
||||||
import json
|
from typing import Dict, List
|
||||||
|
from typing import Optional, Union, Literal
|
||||||
|
from urllib.parse import urlencode
|
||||||
|
|
||||||
|
from linkedin_api import Linkedin
|
||||||
|
|
||||||
# set log to all debug
|
# set log to all debug
|
||||||
logging.basicConfig(level=logging.INFO)
|
logging.basicConfig(level=logging.INFO)
|
||||||
|
|
||||||
|
|
||||||
class LinkedInEvolvedAPI(Linkedin):
|
class LinkedInEvolvedAPI(Linkedin):
|
||||||
already_applied_jobs: List[str] = []
|
already_applied_jobs: List[str] = []
|
||||||
|
|
||||||
def __init__(self, username, password):
|
def __init__(self, username, password):
|
||||||
super().__init__(username, password)
|
super().__init__(username, password)
|
||||||
|
|
||||||
def search_jobs(
|
def search_jobs(
|
||||||
self,
|
self,
|
||||||
keywords: Optional[str] = None,
|
keywords: Optional[str] = None,
|
||||||
companies: Optional[List[str]] = None,
|
companies: Optional[List[str]] = None,
|
||||||
experience: Optional[
|
experience: Optional[
|
||||||
List[
|
List[
|
||||||
Union[
|
Union[
|
||||||
Literal["1"],
|
Literal["1"],
|
||||||
Literal["2"],
|
Literal["2"],
|
||||||
Literal["3"],
|
Literal["3"],
|
||||||
Literal["4"],
|
Literal["4"],
|
||||||
Literal["5"],
|
Literal["5"],
|
||||||
Literal["6"],
|
Literal["6"],
|
||||||
|
]
|
||||||
]
|
]
|
||||||
]
|
] = None,
|
||||||
] = None,
|
job_type: Optional[
|
||||||
job_type: Optional[
|
List[
|
||||||
List[
|
Union[
|
||||||
Union[
|
Literal["F"],
|
||||||
Literal["F"],
|
Literal["C"],
|
||||||
Literal["C"],
|
Literal["P"],
|
||||||
Literal["P"],
|
Literal["T"],
|
||||||
Literal["T"],
|
Literal["I"],
|
||||||
Literal["I"],
|
Literal["V"],
|
||||||
Literal["V"],
|
Literal["O"],
|
||||||
Literal["O"],
|
]
|
||||||
]
|
]
|
||||||
]
|
] = None,
|
||||||
] = None,
|
job_title: Optional[List[str]] = None,
|
||||||
job_title: Optional[List[str]] = None,
|
industries: Optional[List[str]] = None,
|
||||||
industries: Optional[List[str]] = None,
|
location_name: Optional[str] = None,
|
||||||
location_name: Optional[str] = None,
|
remote: Optional[List[Union[Literal["1"], Literal["2"], Literal["3"]]]] = None,
|
||||||
remote: Optional[List[Union[Literal["1"], Literal["2"], Literal["3"]]]] = None,
|
listed_at: None | int = None,
|
||||||
listed_at: None | int = None,
|
distance: Optional[int] = None,
|
||||||
distance: Optional[int] = None,
|
easy_apply: Optional[bool] = True,
|
||||||
easy_apply: Optional[bool] = True,
|
limit=-1,
|
||||||
limit=-1,
|
offset=0,
|
||||||
offset=0,
|
**kwargs,
|
||||||
**kwargs,
|
|
||||||
) -> List[Dict]:
|
) -> List[Dict]:
|
||||||
"""Perform a LinkedIn search for jobs.
|
"""Perform a LinkedIn search for jobs.
|
||||||
|
|
||||||
|
|
@ -154,21 +162,21 @@ class LinkedInEvolvedAPI(Linkedin):
|
||||||
e["job_id"] = trackingUrn
|
e["job_id"] = trackingUrn
|
||||||
if e.get("$type") == "com.linkedin.voyager.dash.jobs.JobPosting":
|
if e.get("$type") == "com.linkedin.voyager.dash.jobs.JobPosting":
|
||||||
new_data.append(e)
|
new_data.append(e)
|
||||||
|
|
||||||
if not new_data:
|
if not new_data:
|
||||||
break
|
break
|
||||||
results.extend(new_data)
|
results.extend(new_data)
|
||||||
if (
|
if (
|
||||||
(-1 < limit <= len(results))
|
(-1 < limit <= len(results))
|
||||||
or len(results) / count >= Linkedin._MAX_REPEATED_REQUESTS
|
or len(results) / count >= Linkedin._MAX_REPEATED_REQUESTS
|
||||||
) or len(elements) == 0:
|
) or len(elements) == 0:
|
||||||
break
|
break
|
||||||
|
|
||||||
self.logger.debug(f"results grew to {len(results)}")
|
self.logger.debug(f"results grew to {len(results)}")
|
||||||
|
|
||||||
return results
|
return results
|
||||||
|
|
||||||
def get_fields_for_easy_apply(self,job_id: str) -> List[Dict]:
|
def get_fields_for_easy_apply(self, job_id: str) -> List[Dict]:
|
||||||
"""Get fields needed for easy apply jobs.
|
"""Get fields needed for easy apply jobs.
|
||||||
|
|
||||||
:param job_id: Job ID
|
:param job_id: Job ID
|
||||||
|
|
@ -181,14 +189,12 @@ class LinkedInEvolvedAPI(Linkedin):
|
||||||
cookie_str = "; ".join([f"{k}={v}" for k, v in cookies.items()])
|
cookie_str = "; ".join([f"{k}={v}" for k, v in cookies.items()])
|
||||||
|
|
||||||
headers: Dict[str, str] = self._headers()
|
headers: Dict[str, str] = self._headers()
|
||||||
|
|
||||||
|
|
||||||
headers["Accept"] = "application/vnd.linkedin.normalized+json+2.1"
|
headers["Accept"] = "application/vnd.linkedin.normalized+json+2.1"
|
||||||
headers["csrf-token"] = cookies["JSESSIONID"].replace('"', "")
|
headers["csrf-token"] = cookies["JSESSIONID"].replace('"', "")
|
||||||
headers["Cookie"] = cookie_str
|
headers["Cookie"] = cookie_str
|
||||||
headers["Connection"] = "keep-alive"
|
headers["Connection"] = "keep-alive"
|
||||||
|
|
||||||
|
|
||||||
default_params = {
|
default_params = {
|
||||||
"decorationId": "com.linkedin.voyager.dash.deco.jobs.OnsiteApplyApplication-67",
|
"decorationId": "com.linkedin.voyager.dash.deco.jobs.OnsiteApplyApplication-67",
|
||||||
"jobPostingUrn": f"urn:li:fsd_jobPosting:{job_id}",
|
"jobPostingUrn": f"urn:li:fsd_jobPosting:{job_id}",
|
||||||
|
|
@ -217,26 +223,26 @@ class LinkedInEvolvedAPI(Linkedin):
|
||||||
except ValueError:
|
except ValueError:
|
||||||
self.logger.error("Failed to parse JSON response")
|
self.logger.error("Failed to parse JSON response")
|
||||||
return []
|
return []
|
||||||
|
|
||||||
form_components = []
|
form_components = []
|
||||||
|
|
||||||
for item in data.get("included", []):
|
for item in data.get("included", []):
|
||||||
if 'formComponent' in item:
|
if 'formComponent' in item:
|
||||||
urn = item['urn']
|
urn = item['urn']
|
||||||
try:
|
try:
|
||||||
title = item['title']['text']
|
title = item['title']['text']
|
||||||
except TypeError:
|
except TypeError:
|
||||||
title = urn
|
title = urn
|
||||||
|
|
||||||
form_component_type = list(item['formComponent'].keys())[0]
|
form_component_type = list(item['formComponent'].keys())[0]
|
||||||
form_component_details = item['formComponent'][form_component_type]
|
form_component_details = item['formComponent'][form_component_type]
|
||||||
|
|
||||||
component_info = {
|
component_info = {
|
||||||
'title': title,
|
'title': title,
|
||||||
'urn': urn,
|
'urn': urn,
|
||||||
'formComponentType': form_component_type,
|
'formComponentType': form_component_type,
|
||||||
}
|
}
|
||||||
|
|
||||||
if 'textSelectableOptions' in form_component_details:
|
if 'textSelectableOptions' in form_component_details:
|
||||||
options = [
|
options = [
|
||||||
opt['optionText']['text'] for opt in form_component_details['textSelectableOptions']
|
opt['optionText']['text'] for opt in form_component_details['textSelectableOptions']
|
||||||
|
|
@ -244,18 +250,18 @@ class LinkedInEvolvedAPI(Linkedin):
|
||||||
component_info['selectableOptions'] = options
|
component_info['selectableOptions'] = options
|
||||||
elif 'selectableOptions' in form_component_details:
|
elif 'selectableOptions' in form_component_details:
|
||||||
options = [
|
options = [
|
||||||
opt['textSelectableOption']['optionText']['text']
|
opt['textSelectableOption']['optionText']['text']
|
||||||
for opt in form_component_details['selectableOptions']
|
for opt in form_component_details['selectableOptions']
|
||||||
]
|
]
|
||||||
component_info['selectableOptions'] = options
|
component_info['selectableOptions'] = options
|
||||||
|
|
||||||
form_components.append(component_info)
|
form_components.append(component_info)
|
||||||
|
|
||||||
return form_components
|
return form_components
|
||||||
|
|
||||||
def apply_to_job(self,job_id: str, fields: dict, followCompany: bool = True) -> bool:
|
def apply_to_job(self, job_id: str, fields: dict, followCompany: bool = True) -> bool:
|
||||||
return False
|
return False
|
||||||
|
|
||||||
# ToDo: Implement apply to job parser first
|
# ToDo: Implement apply to job parser first
|
||||||
# How need to be implemented:
|
# How need to be implemented:
|
||||||
# 1. Get fields for easy apply job from the previous method (get_fields_for_easy_apply)
|
# 1. Get fields for easy apply job from the previous method (get_fields_for_easy_apply)
|
||||||
|
|
@ -265,11 +271,11 @@ class LinkedInEvolvedAPI(Linkedin):
|
||||||
# {'title': 'Quanti anni di esperienza di lavoro hai con Router?', 'urn': 'urn:li:fsd_formElement:urn:li:jobs_applyformcommon_easyApplyFormElement:(4013860791,9478711764,numeric)', 'formComponentType': 'singleLineTextFormComponent', 'response': '5'}
|
# {'title': 'Quanti anni di esperienza di lavoro hai con Router?', 'urn': 'urn:li:fsd_formElement:urn:li:jobs_applyformcommon_easyApplyFormElement:(4013860791,9478711764,numeric)', 'formComponentType': 'singleLineTextFormComponent', 'response': '5'}
|
||||||
# To fill, you can temporary use input() function to get the data from the user manually for testing purposes (for the further implementation, the question will be asked to AI implementation and automatically filled)
|
# To fill, you can temporary use input() function to get the data from the user manually for testing purposes (for the further implementation, the question will be asked to AI implementation and automatically filled)
|
||||||
# Build a working payload.
|
# Build a working payload.
|
||||||
|
|
||||||
# EXAMPLE OF WORKING PAYLOAD
|
# EXAMPLE OF WORKING PAYLOAD
|
||||||
# 4005350454 is job_id, so need to be replaced with the job_id
|
# 4005350454 is job_id, so need to be replaced with the job_id
|
||||||
|
|
||||||
#{
|
# {
|
||||||
# "followCompany": true,
|
# "followCompany": true,
|
||||||
# "responses": [
|
# "responses": [
|
||||||
# {
|
# {
|
||||||
|
|
@ -349,9 +355,10 @@ class LinkedInEvolvedAPI(Linkedin):
|
||||||
# }
|
# }
|
||||||
# ],
|
# ],
|
||||||
# "trackingId": ""
|
# "trackingId": ""
|
||||||
#}
|
# }
|
||||||
|
|
||||||
# Push the commit to the repository and create a pull request to the v3 branch.
|
# Push the commit to the repository and create a pull request to the v3 branch.
|
||||||
|
<<<<<<< HEAD
|
||||||
|
|
||||||
def create_request_pdf(self, filename: str) -> str | None:
|
def create_request_pdf(self, filename: str) -> str | None:
|
||||||
"""
|
"""
|
||||||
|
|
@ -469,10 +476,13 @@ class LinkedInEvolvedAPI(Linkedin):
|
||||||
with open(file_path, 'rb') as file:
|
with open(file_path, 'rb') as file:
|
||||||
binary_data = file.read()
|
binary_data = file.read()
|
||||||
return binary_data
|
return binary_data
|
||||||
|
=======
|
||||||
|
>>>>>>> upstream/v3
|
||||||
|
|
||||||
def set_job_as_applied(self, job_id: str) -> None:
|
def set_job_as_applied(self, job_id: str) -> None:
|
||||||
self.already_applied_jobs.append(job_id)
|
self.already_applied_jobs.append(job_id)
|
||||||
|
|
||||||
|
<<<<<<< HEAD
|
||||||
def upload_linkedin_resume(self, cv_path: str) -> str | bool:
|
def upload_linkedin_resume(self, cv_path: str) -> str | bool:
|
||||||
url = self.create_request_pdf("resume.pdf")
|
url = self.create_request_pdf("resume.pdf")
|
||||||
if url:
|
if url:
|
||||||
|
|
@ -491,6 +501,14 @@ if __name__ == "__main__":
|
||||||
|
|
||||||
api: LinkedInEvolvedAPI = LinkedInEvolvedAPI(username="", password="")
|
api: LinkedInEvolvedAPI = LinkedInEvolvedAPI(username="", password="")
|
||||||
jobs = api.search_jobs(keywords="Frontend Developer", location_name="Italia", limit=100, easy_apply=True, offset=1, listed_at=None)
|
jobs = api.search_jobs(keywords="Frontend Developer", location_name="Italia", limit=100, easy_apply=True, offset=1, listed_at=None)
|
||||||
|
=======
|
||||||
|
|
||||||
|
## EXAMPLE USAGE
|
||||||
|
if __name__ == "__main__":
|
||||||
|
api: LinkedInEvolvedAPI = LinkedInEvolvedAPI(username="", password="")
|
||||||
|
jobs = api.search_jobs(keywords="Frontend Developer", location_name="Italia", limit=100, easy_apply=True, offset=1,
|
||||||
|
listed_at=None)
|
||||||
|
>>>>>>> upstream/v3
|
||||||
for job in jobs:
|
for job in jobs:
|
||||||
job_id: str = job["job_id"]
|
job_id: str = job["job_id"]
|
||||||
|
|
||||||
|
|
@ -514,7 +532,3 @@ if __name__ == "__main__":
|
||||||
print(field)
|
print(field)
|
||||||
|
|
||||||
break
|
break
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
14
src/utils.py
14
src/utils.py
|
|
@ -8,7 +8,7 @@ from selenium import webdriver
|
||||||
log_file = "app_log.log"
|
log_file = "app_log.log"
|
||||||
|
|
||||||
logging.basicConfig(
|
logging.basicConfig(
|
||||||
level=logging.DEBUG,
|
level=logging.INFO,
|
||||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||||
handlers=[
|
handlers=[
|
||||||
logging.FileHandler(log_file, mode='a', encoding='utf-8'),
|
logging.FileHandler(log_file, mode='a', encoding='utf-8'),
|
||||||
|
|
@ -22,7 +22,7 @@ file_handler = logging.FileHandler(log_file, mode='a', encoding='utf-8')
|
||||||
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
||||||
file_handler.setFormatter(formatter)
|
file_handler.setFormatter(formatter)
|
||||||
logger.addHandler(file_handler)
|
logger.addHandler(file_handler)
|
||||||
logger.setLevel(logging.DEBUG)
|
logger.setLevel(logging.INFO)
|
||||||
|
|
||||||
chromeProfilePath = os.path.join(os.getcwd(), "chrome_profile", "linkedin_profile")
|
chromeProfilePath = os.path.join(os.getcwd(), "chrome_profile", "linkedin_profile")
|
||||||
|
|
||||||
|
|
@ -90,7 +90,13 @@ def scroll_slow(driver, scrollable_element, start=0, end=3600, step=300, reverse
|
||||||
return
|
return
|
||||||
|
|
||||||
position = start
|
position = start
|
||||||
|
previous_position = None # Tracking the previous position to avoid duplicate scrolls
|
||||||
while (step > 0 and position < end) or (step < 0 and position > end):
|
while (step > 0 and position < end) or (step < 0 and position > end):
|
||||||
|
if position == previous_position:
|
||||||
|
# Avoid re-scrolling to the same position
|
||||||
|
logger.debug("Stopping scroll as position hasn't changed: %d", position)
|
||||||
|
break
|
||||||
|
|
||||||
try:
|
try:
|
||||||
driver.execute_script(script_scroll_to, scrollable_element, position)
|
driver.execute_script(script_scroll_to, scrollable_element, position)
|
||||||
logger.debug("Scrolled to position: %d", position)
|
logger.debug("Scrolled to position: %d", position)
|
||||||
|
|
@ -98,11 +104,15 @@ def scroll_slow(driver, scrollable_element, start=0, end=3600, step=300, reverse
|
||||||
logger.error("Error during scrolling: %s", e)
|
logger.error("Error during scrolling: %s", e)
|
||||||
print(f"Error during scrolling: {e}")
|
print(f"Error during scrolling: {e}")
|
||||||
|
|
||||||
|
previous_position = position
|
||||||
position += step
|
position += step
|
||||||
|
|
||||||
|
# Decrease the step but ensure it doesn't reverse direction
|
||||||
step = max(10, abs(step) - 10) * (-1 if reverse else 1)
|
step = max(10, abs(step) - 10) * (-1 if reverse else 1)
|
||||||
|
|
||||||
time.sleep(random.uniform(0.6, 1.5))
|
time.sleep(random.uniform(0.6, 1.5))
|
||||||
|
|
||||||
|
# Ensure the final scroll position is correct
|
||||||
driver.execute_script(script_scroll_to, scrollable_element, end)
|
driver.execute_script(script_scroll_to, scrollable_element, end)
|
||||||
logger.debug("Scrolled to final position: %d", end)
|
logger.debug("Scrolled to final position: %d", end)
|
||||||
time.sleep(0.5)
|
time.sleep(0.5)
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue