ahmedhesham301/jobfit-ai
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الشفرة المشكلات طلبات السحب Actions Packages المشاريع الإصدارات الويكي النشاط

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هذا الالتزام موجود في:
Ahmed Hesham
2025-10-01 12:49:40 +03:00
الأصل 89bd9aae70
التزام 65bd214a61
8 ملفات معدلة مع 131 إضافات و137 حذوفات
تنزيل ملف التصحيح تنزيل ملف الاختلاف توسيع جميع الملفات طي جميع الملفات

6
.dockerignore Normal file
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@@ -0,0 +1,6 @@
__pycache__
.venv
.vscode
.dockerignore
.env
jobs.csv

3
.gitignore مباع
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@@ -2,4 +2,5 @@ jobs.csv
.venv
__pycache__
.env
.vscode
.vscode
.env2

11
Dockerfile Normal file
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FROM python:3.12.3-slim
WORKDIR /jobfitai
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD [ "python3", "main.py"]

3
ai.py
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@@ -1,12 +1,9 @@
# To run this code you need to install the following dependencies:
# pip install google-genai
import base64
import os
from google import genai
from google.genai import types
def generate(description, instruction, api_key):
client = genai.Client(api_key=api_key)

93
filter.py Normal file
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import logging
import time
from ai import generate
import json
from google.genai.errors import ServerError, ClientError
total_fail = 0
total_overload = 0
total_fail_overload = 0
total_empty_response = 0
total_fail_empty_response = 0
def filter_jobs(jobs, cv, api_keys, good_fit_jobs):
key_number = 0
for i, job in jobs.iterrows():
# print("index is :", i) # for debugging
if (i + 1) % 10 == 0 and i != 0:
logging.warning("sleeping to avoid API rate limits")
time.sleep(60)
try_count = 3
while try_count > 0:
try:
cleaned_description = "\n".join(
[line for line in job["description"].splitlines() if line.strip()]
)
ai_response = generate(cleaned_description, cv, api_keys[key_number])
ai_response_dict = json.loads(ai_response)
break
except json.JSONDecodeError as e:
try_count -= 1
total_empty_response += 1
if try_count == 0:
total_fail += 1
total_fail_empty_response += 1
logging.warning("Sleeping after JSONDecodeError")
time.sleep(6)
except ServerError as e:
if e.details["error"]["code"] == 503:
try_count -= 1
total_overload += 1
if try_count == 0:
total_fail += 1
total_fail_overload += 1
logging.warning("sleeping to after The model is overloaded.")
print(e.details)
time.sleep(10)
else:
logging.critical(e.details)
return 1
except ClientError as e:
if e.details["error"]["code"] == 429:
logging.warning("api limit hit")
key_number += 1
if key_number > len(api_keys) - 1:
logging.critical("All api keys hit the limit")
return 1
else:
logging.critical(e.details)
return 1
else:
logging.critical("All attempts failed")
continue
if ai_response_dict["percentage"] > 50:
good_fit_jobs.append(
{
"title": job["title"],
"url": job["job_url"],
"percentage": ai_response_dict["percentage"],
"why I'm I a good fit": ai_response_dict["why I'm I a good fit"],
"what I'm I missing": ai_response_dict["what I'm I missing"],
}
)
print_stats
return good_fit_jobs
def print_stats():
stats = f"""total fail: {total_fail}
total empty responses: {total_empty_response} fail: {total_fail_empty_response}
Total overloads: {total_overload} fail: {total_fail_overload}"""
print(stats)

4
jobs.py
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@@ -1,5 +1,5 @@
from jobspy import scrape_jobs
import logging
def getJobs(jobTitle, results_wanted, hours_old):
jobs = scrape_jobs(
@@ -22,7 +22,7 @@ def getJobs(jobTitle, results_wanted, hours_old):
linkedin_fetch_description=True, # gets more info such as description, direct job url (slower)
# proxies=["208.195.175.46:65095", "208.195.175.45:65095", "localhost"],
)
print(f"Found {len(jobs)} jobs")
logging.warning(f"Found {len(jobs)} {jobTitle} jobs")
# print(jobs)
return jobs
# jobs.to_csv(

113
main.py
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@@ -1,12 +1,10 @@
from jobs import getJobs
from ai import generate
from google.genai.errors import ServerError, ClientError
from alert import send_email
import json
import time
from filter import filter_jobs
import os
import logging
from random import shuffle
import pandas as pd
logging.basicConfig(
level=logging.WARNING, format="%(asctime)s - %(levelname)s - %(message)s"
@@ -16,110 +14,31 @@ SENDER = os.getenv("smtp_email")
PASSWORD = os.getenv("smtp_password")
RECEIVER = os.getenv("receiver_email")
api_keys = os.getenv("api_keys").split(",")
good_fit_jobs = []
# stats
total_fail = 0
total_fail_overload = 0
total_overload = 0
total_empty_response = 0
total_fail_empty_response = 0
shuffle(api_keys)
all_jobs = pd.DataFrame()
good_fit_jobs = []
with open("instruction.txt", "r") as f:
CV = f.read()
def get_jobs(job_title, cv, results_wanted, hours_old):
global total_fail, total_fail_overload, total_overload, total_empty_response, total_fail_empty_response
key_number = 0
def get_jobs(job_title, results_wanted, hours_old):
global all_jobs
jobs = getJobs(job_title, results_wanted, hours_old)
for i, job in jobs.iterrows():
print("index is :", i) # for debugging
if (i + 1) % 10 == 0 and i != 0:
logging.warning("sleeping to avoid API rate limits")
time.sleep(60)
try_count = 3
while try_count > 0:
try:
cleaned_description = "\n".join(
[line for line in job["description"].splitlines() if line.strip()]
)
ai_response = generate(cleaned_description, cv, api_keys[key_number])
ai_response_dict = json.loads(ai_response)
break
except json.JSONDecodeError as e:
try_count -= 1
total_empty_response += 1
if try_count == 0:
total_fail += 1
total_fail_empty_response += 1
logging.warning("Sleeping after JSONDecodeError")
time.sleep(6)
except ServerError as e:
if e.details["error"]["code"] == 503:
try_count -= 1
total_overload += 1
if try_count == 0:
total_fail += 1
total_fail_overload += 1
logging.warning("sleeping to after The model is overloaded.")
print(e.details)
time.sleep(10)
else:
logging.critical(e.details)
return 1
except ClientError as e:
if e.details["error"]["code"] == 429:
logging.warning("api limit hit")
key_number += 1
if key_number > len(api_keys) - 1:
logging.critical("All api keys hit the limit")
return 1
else:
logging.critical(e.details)
return 1
else:
logging.critical("All attempts failed")
continue
if ai_response_dict["percentage"] > 50:
good_fit_jobs.append(
{
"title": job["title"],
"url": job["job_url"],
"percentage": ai_response_dict["percentage"],
"why I'm I a good fit": ai_response_dict["why I'm I a good fit"],
"what I'm I missing": ai_response_dict["what I'm I missing"],
}
)
def print_stats():
stats = f"""total fail: {total_fail}
total empty responses: {total_empty_response} fail: {total_fail_empty_response}
Total overloads: {total_overload} fail: {total_fail_overload}"""
print(stats)
all_jobs = pd.concat([all_jobs, jobs], ignore_index=True)
if __name__ == "__main__":
get_jobs("devops", CV, results_wanted=30, hours_old=2)
get_jobs("backend", CV, results_wanted=30, hours_old=2)
get_jobs("software engineer", CV, results_wanted=30, hours_old=2)
get_jobs("devops", results_wanted=30, hours_old=2)
get_jobs("backend", results_wanted=30, hours_old=2)
get_jobs("software engineer", results_wanted=30, hours_old=2)
get_jobs("cloud", results_wanted=30, hours_old=2)
get_jobs("sre", results_wanted=30, hours_old=2)
get_jobs("intern", results_wanted=30, hours_old=2)
all_jobs.drop_duplicates(inplace=True, ignore_index=True)
filter_jobs(all_jobs, CV, api_keys, good_fit_jobs)
if len(good_fit_jobs) > 0:
send_email(SENDER, RECEIVER, PASSWORD, good_fit_jobs)
else:
print("no good fit jobs")
print_stats()

35
requirements.txt
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@@ -1,35 +1,2 @@
annotated-types==0.7.0
anyio==4.11.0
beautifulsoup4==4.13.5
cachetools==5.5.2
certifi==2025.8.3
charset-normalizer==3.4.3
google-auth==2.40.3
google-genai==1.38.0
h11==0.16.0
httpcore==1.0.9
httpx==0.28.1
idna==3.10
markdownify==0.13.1
numpy==1.26.3
pandas==2.3.2
pyasn1==0.6.1
pyasn1_modules==0.4.2
pydantic==2.11.9
pydantic_core==2.33.2
python-dateutil==2.9.0.post0
python-jobspy==1.1.82
pytz==2025.2
regex==2024.11.6
requests==2.32.5
rsa==4.9.1
six==1.17.0
sniffio==1.3.1
soupsieve==2.8
tenacity==9.1.2
tls-client==1.0.1
typing-inspection==0.4.1
typing_extensions==4.15.0
tzdata==2025.2
urllib3==2.5.0
websockets==15.0.1
google-genai==1.38.0