update for artifact with run ID

git commit -m "Added requirements.txt"
git push origin main

.github/workflows/job_scraper_dynamic.yml

@@ -0,0 +1,49 @@
+name: JobSpy Scraper Dynamic Workflow
+
+on:
+  workflow_dispatch:
+    inputs:
+      user_email:
+        description: 'Email of user'
+        required: true
+        default: 'Branden@autoemployme.onmicrosoft.com'
+
+permissions:
+  contents: read
+  id-token: write
+
+jobs:
+  scrape_jobs:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout Repo
+        uses: actions/checkout@v3
+
+      - name: Set Up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+
+      - name: Install Dependencies
+        run: |
+          pip install --upgrade pip
+          pip install -r requirements.txt
+
+      - name: Sanitize Email + Create Run ID
+        id: vars
+        run: |
+          safe_email=$(echo "${{ github.event.inputs.user_email }}" | sed 's/@/_at_/g; s/\./_/g')
+          run_id=$(date +%s)
+          echo "safe_email=$safe_email" >> $GITHUB_OUTPUT
+          echo "run_id=$run_id" >> $GITHUB_OUTPUT
+
+      - name: Run Job Scraper
+        run: |
+          python job_scraper_dynamic.py "${{ github.event.inputs.user_email }}" "${{ steps.vars.outputs.run_id }}"
+
+      - name: Upload Output Artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: jobspy_output_${{ steps.vars.outputs.safe_email }}_${{ steps.vars.outputs.run_id }}
+          path: outputs/jobspy_output_${{ steps.vars.outputs.safe_email }}_${{ steps.vars.outputs.run_id }}.csv

.github/workflows/jobspy_scraper.yml

@@ -0,0 +1,48 @@
+name: JobSpy Scraper Workflow
+
+on:
+  workflow_dispatch:  # Allows manual trigger from GitHub or Power Automate
+  # Remove or comment out the schedule to prevent auto-runs
+  # schedule:
+  #   - cron: '0 */6 * * *'  # Runs every 6 hours (DISABLED)
+
+permissions:
+  actions: read
+  contents: read
+  id-token: write
+
+jobs:
+  scrape_jobs:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+
+      - name: Run JobSpy Scraper
+        run: python job_scraper_exact_match.py
+
+      - name: Debug - Check if jobspy_output.csv exists
+        run: |
+          if [ ! -f jobspy_output.csv ]; then
+            echo "❌ ERROR: jobspy_output.csv not found!"
+            exit 1
+          else
+            echo "✅ jobspy_output.csv found, proceeding to upload..."
+          fi
+
+      - name: Upload JobSpy Output as Artifact
+        uses: actions/upload-artifact@v4  # Explicitly using latest version
+        with:
+          name: jobspy-results
+          path: jobspy_output.csv

.github/workflows/publish-to-pypi.yml

@@ -1,50 +1,37 @@
-name: Publish Python 🐍 distributions 📦 to PyPI
+name: Publish JobSpy to PyPi
 on:
-  pull_request:
-    types:
-      - closed
-
-permissions:
-  contents: write
+  push:
+    branches:
+      - main
+  workflow_dispatch:
 
 jobs:
   build-n-publish:
-    name: Build and publish Python 🐍 distributions 📦 to PyPI
+    name: Build and publish JobSpy to PyPi
     runs-on: ubuntu-latest
 
-    if: github.event.pull_request.merged == true && github.event.pull_request.base.ref == 'main'
-
     steps:
       - uses: actions/checkout@v3
-
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
           python-version: "3.10"
 
-      - name: Install dependencies
-        run: pip install toml
-
-      - name: Increment version
-        run: python increment_version.py
-
-      - name: Commit version increment
-        run: |
-          git config --global user.name 'github-actions'
-          git config --global user.email 'github-actions@github.com'
-          git add pyproject.toml
-          git commit -m 'Increment version'
-
-      - name: Push changes
-        run: git push
-
       - name: Install poetry
-        run: pip install poetry --user
+        run: >-
+          python3 -m
+          pip install
+          poetry
+          --user
 
       - name: Build distribution 📦
-        run: poetry build
+        run: >-
+          python3 -m
+          poetry
+          build
 
       - name: Publish distribution 📦 to PyPI
+        if: startsWith(github.ref, 'refs/tags') || github.event_name == 'workflow_dispatch'
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
-          password: ${{ secrets.PYPI_API_TOKEN }}
+          password: ${{ secrets.PYPI_API_TOKEN }}

configs/config.json

@@ -0,0 +1,8 @@
+{
+    "search_terms": ["IT Support", "Help Desk"],
+    "results_wanted": 50,
+    "max_days_old": 7,
+    "target_state": "NY",
+    "user_email": "Branden@autoemployme.onmicrosoft.com"
+  }
+  

configs/config_Branden_at_autoemployme_onmicrosoft_com.json

@@ -0,0 +1,8 @@
+{
+    "search_terms": ["Testing", "Help Desk", "Support"],
+    "results_wanted": 50,
+    "max_days_old": 7,
+    "target_state": "NY",
+    "user_email": "Branden@autoemployme.onmicrosoft.com"
+  }
+  

increment_version.py

@@ -1,21 +0,0 @@
-import toml
-
-def increment_version(version):
-    major, minor, patch = map(int, version.split('.'))
-    patch += 1
-    return f"{major}.{minor}.{patch}"
-
-# Load pyproject.toml
-with open('pyproject.toml', 'r') as file:
-    pyproject = toml.load(file)
-
-# Increment the version
-current_version = pyproject['tool']['poetry']['version']
-new_version = increment_version(current_version)
-pyproject['tool']['poetry']['version'] = new_version
-
-# Save the updated pyproject.toml
-with open('pyproject.toml', 'w') as file:
-    toml.dump(pyproject, file)
-
-print(f"Version updated from {current_version} to {new_version}")

job_scraper.py

@@ -0,0 +1,116 @@
+import csv
+import datetime
+from jobspy.google import Google
+from jobspy.linkedin import LinkedIn
+from jobspy.indeed import Indeed
+from jobspy.ziprecruiter import ZipRecruiter
+from jobspy.model import ScraperInput
+
+# Define job sources
+sources = {
+    "google": Google,
+    "linkedin": LinkedIn,
+    "indeed": Indeed,
+    "zip_recruiter": ZipRecruiter,
+}
+
+# Define search preferences
+search_terms = ["Automation Engineer", "CRM Manager", "Implementation Specialist"]
+results_wanted = 200  # Fetch more jobs
+max_days_old = 2  # Fetch jobs posted in last 48 hours
+target_state = "NY"  # Only keep jobs from New York
+
+
+def scrape_jobs(search_terms, results_wanted, max_days_old, target_state):
+    """Scrape jobs from multiple sources and filter by state."""
+    all_jobs = []
+    today = datetime.date.today()
+    print("\n🔎 DEBUG: Fetching jobs for search terms:", search_terms)
+
+    for search_term in search_terms:
+        for source_name, source_class in sources.items():
+            print(f"\n🚀 Scraping {search_term} from {source_name}...")
+
+            scraper = source_class()
+            search_criteria = ScraperInput(
+                site_type=[source_name],
+                search_term=search_term,
+                results_wanted=results_wanted,
+            )
+
+            job_response = scraper.scrape(search_criteria)
+
+            for job in job_response.jobs:
+                # Normalize location fields
+                location_city = job.location.city.strip() if job.location.city else "Unknown"
+                location_state = job.location.state.strip().upper() if job.location.state else "Unknown"
+                location_country = str(job.location.country) if job.location.country else "Unknown"
+
+                # Debug: Show all jobs being fetched
+                print(f"📍 Fetched Job: {job.title} - {location_city}, {location_state}, {location_country}")
+
+                # Ensure the job is recent
+                if job.date_posted and (today - job.date_posted).days <= max_days_old:
+                    if location_state == target_state or job.is_remote:
+                        print(f"✅ MATCH (In NY or Remote): {job.title} - {location_city}, {location_state} (Posted {job.date_posted})")
+
+                        all_jobs.append({
+                            "Job ID": job.id,
+                            "Job Title (Primary)": job.title,
+                            "Company Name": job.company_name if job.company_name else "Unknown",
+                            "Industry": job.company_industry if job.company_industry else "Not Provided",
+                            "Experience Level": job.job_level if job.job_level else "Not Provided",
+                            "Job Type": job.job_type[0].name if job.job_type else "Not Provided",
+                            "Is Remote": job.is_remote,
+                            "Currency": job.compensation.currency if job.compensation else "",
+                            "Salary Min": job.compensation.min_amount if job.compensation else "",
+                            "Salary Max": job.compensation.max_amount if job.compensation else "",
+                            "Date Posted": job.date_posted.strftime("%Y-%m-%d") if job.date_posted else "Not Provided",
+                            "Location City": location_city,
+                            "Location State": location_state,
+                            "Location Country": location_country,
+                            "Job URL": job.job_url,
+                            "Job Description": job.description[:500] if job.description else "No description available",
+                            "Job Source": source_name
+                        })
+                    else:
+                        print(f"❌ Ignored (Wrong State): {job.title} - {location_city}, {location_state} (Posted {job.date_posted})")
+                else:
+                    print(f"⏳ Ignored (Too Old): {job.title} - {location_city}, {location_state} (Posted {job.date_posted})")
+
+    print(f"\n✅ {len(all_jobs)} jobs retrieved in NY")
+    return all_jobs
+
+
+def save_jobs_to_csv(jobs, filename="jobspy_output.csv"):
+    """Save job data to a CSV file."""
+    if not jobs:
+        print("⚠️ No jobs found matching criteria.")
+        return
+
+    fieldnames = [
+        "Job ID", "Job Title (Primary)", "Company Name", "Industry",
+        "Experience Level", "Job Type", "Is Remote", "Currency",
+        "Salary Min", "Salary Max", "Date Posted", "Location City",
+        "Location State", "Location Country", "Job URL", "Job Description",
+        "Job Source"
+    ]
+
+    with open(filename, mode="w", newline="", encoding="utf-8") as file:
+        writer = csv.DictWriter(file, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(jobs)
+
+    print(f"✅ Jobs saved to {filename} ({len(jobs)} entries)")
+
+
+# Run the scraper with multiple job searches
+job_data = scrape_jobs(
+    search_terms=search_terms,
+    results_wanted=results_wanted,
+    max_days_old=max_days_old,
+    target_state=target_state
+)
+
+# Save results to CSV
+save_jobs_to_csv(job_data)

job_scraper_dynamic.py

@@ -0,0 +1,94 @@
+import csv, datetime, os, sys, json
+from jobspy.google import Google
+from jobspy.linkedin import LinkedIn
+from jobspy.indeed import Indeed
+from jobspy.model import ScraperInput
+
+sources = {
+    "google": Google,
+    "linkedin": LinkedIn,
+    "indeed": Indeed,
+}
+
+def sanitize_email(email):
+    return email.replace("@", "_at_").replace(".", "_")
+
+def load_config(email):
+    safe_email = sanitize_email(email)
+    config_path = os.path.join("configs", f"config_{safe_email}.json")
+    if not os.path.exists(config_path):
+        raise FileNotFoundError(f"❌ Config for {email} not found at {config_path}")
+    with open(config_path, "r", encoding="utf-8") as f:
+        return json.load(f), safe_email
+
+def scrape_jobs(search_terms, results_wanted, max_days_old, target_state):
+    today = datetime.date.today()
+    all_jobs = []
+
+    for term in search_terms:
+        for source, Scraper in sources.items():
+            print(f"🔍 Scraping {term} from {source}")
+            scraper = Scraper()
+            try:
+                jobs = scraper.scrape(ScraperInput(
+                    site_type=[source],
+                    search_term=term,
+                    results_wanted=results_wanted
+                )).jobs
+            except Exception as e:
+                print(f"⚠️ {source} error: {e}")
+                continue
+
+            for job in jobs:
+                if job.date_posted and (today - job.date_posted).days <= max_days_old:
+                    if target_state == (job.location.state or "").upper() or job.is_remote:
+                        if any(term.lower() in job.title.lower() for term in search_terms):
+                            all_jobs.append({
+                                "Job ID": job.id,
+                                "Job Title (Primary)": job.title,
+                                "Company Name": job.company_name or "Unknown",
+                                "Industry": job.company_industry or "Not Provided",
+                                "Experience Level": job.job_level or "Not Provided",
+                                "Job Type": job.job_type[0].name if job.job_type else "Not Provided",
+                                "Is Remote": job.is_remote,
+                                "Currency": job.compensation.currency if job.compensation else "",
+                                "Salary Min": job.compensation.min_amount if job.compensation else "",
+                                "Salary Max": job.compensation.max_amount if job.compensation else "",
+                                "Date Posted": job.date_posted.strftime("%Y-%m-%d"),
+                                "Location City": job.location.city or "Unknown",
+                                "Location State": (job.location.state or "Unknown").upper(),
+                                "Location Country": job.location.country or "Unknown",
+                                "Job URL": job.job_url,
+                                "Job Description": job.description.replace(",", "") if job.description else "No description",
+                                "Job Source": source
+                            })
+    print(f"✅ Found {len(all_jobs)} jobs")
+    return all_jobs
+
+def save_to_csv(jobs, path):
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    fieldnames = [
+        "Job ID", "Job Title (Primary)", "Company Name", "Industry",
+        "Experience Level", "Job Type", "Is Remote", "Currency",
+        "Salary Min", "Salary Max", "Date Posted", "Location City",
+        "Location State", "Location Country", "Job URL", "Job Description", "Job Source"
+    ]
+    header = "|~|".join(fieldnames)
+    rows = [header] + ["|~|".join(str(job.get(col, "Not Provided")).replace(",", "").strip() for col in fieldnames) for job in jobs]
+    with open(path, "w", encoding="utf-8") as f:
+        f.write(",".join(rows))
+    print(f"💾 Saved output to: {path}")
+
+if __name__ == "__main__":
+    try:
+        if len(sys.argv) != 3:
+            raise ValueError("❌ Usage: python job_scraper_dynamic.py <user_email> <run_id>")
+
+        user_email, run_id = sys.argv[1], sys.argv[2]
+        config, safe_email = load_config(user_email)
+        jobs = scrape_jobs(config["search_terms"], config["results_wanted"], config["max_days_old"], config["target_state"])
+        save_to_csv(jobs, f"outputs/jobspy_output_{safe_email}_{run_id}.csv")
+
+    except Exception as e:
+        print(f"❌ Fatal error: {e}")
+        sys.exit(1)

job_scraper_exact_match.py

@@ -0,0 +1,146 @@
+import csv
+import datetime
+import os
+from jobspy.google import Google
+from jobspy.linkedin import LinkedIn
+from jobspy.indeed import Indeed
+from jobspy.model import ScraperInput
+
+# Define job sources
+sources = {
+    "google": Google,
+    "linkedin": LinkedIn,
+    "indeed": Indeed,
+}
+
+# Define search preferences
+search_terms = ["Automation Engineer", "CRM Manager", "Implementation Specialist", "CRM", "Project Manager", "POS", "Microsoft Power", "IT Support"]
+results_wanted = 100  # Fetch more jobs
+max_days_old = 2  # Fetch jobs posted in last 48 hours
+target_state = "NY"  # Only keep jobs from New York
+
+def scrape_jobs(search_terms, results_wanted, max_days_old, target_state):
+    """Scrape jobs from multiple sources and filter by state."""
+    all_jobs = []
+    today = datetime.date.today()
+    
+    print("\n🔎 DEBUG: Fetching jobs for search terms:", search_terms)
+
+    for search_term in search_terms:
+        for source_name, source_class in sources.items():
+            print(f"\n🚀 Scraping {search_term} from {source_name}...")
+
+            scraper = source_class()
+            search_criteria = ScraperInput(
+                site_type=[source_name],
+                search_term=search_term,
+                results_wanted=results_wanted,
+            )
+
+            job_response = scraper.scrape(search_criteria)
+
+            for job in job_response.jobs:
+                # Normalize location fields
+                location_city = job.location.city.strip() if job.location.city else "Unknown"
+                location_state = job.location.state.strip().upper() if job.location.state else "Unknown"
+                location_country = str(job.location.country) if job.location.country else "Unknown"
+
+                # Debug: Show all jobs being fetched
+                print(f"📍 Fetched Job: {job.title} - {location_city}, {location_state}, {location_country}")
+
+                # Exclude jobs that don’t explicitly match the search terms
+                if not any(term.lower() in job.title.lower() for term in search_terms):
+                    print(f"🚫 Excluding: {job.title} (Doesn't match {search_terms})")
+                    continue  # Skip this job
+
+                # Ensure the job is recent
+                if job.date_posted and (today - job.date_posted).days <= max_days_old:
+                    # Only accept jobs if they're in NY or Remote
+                    if location_state == target_state or job.is_remote:
+                        print(f"✅ MATCH: {job.title} - {location_city}, {location_state} (Posted {job.date_posted})")
+                        all_jobs.append({
+                            "Job ID": job.id,
+                            "Job Title (Primary)": job.title,
+                            "Company Name": job.company_name if job.company_name else "Unknown",
+                            "Industry": job.company_industry if job.company_industry else "Not Provided",
+                            "Experience Level": job.job_level if job.job_level else "Not Provided",
+                            "Job Type": job.job_type[0].name if job.job_type else "Not Provided",
+                            "Is Remote": job.is_remote,
+                            "Currency": job.compensation.currency if job.compensation else "",
+                            "Salary Min": job.compensation.min_amount if job.compensation else "",
+                            "Salary Max": job.compensation.max_amount if job.compensation else "",
+                            "Date Posted": job.date_posted.strftime("%Y-%m-%d") if job.date_posted else "Not Provided",
+                            "Location City": location_city,
+                            "Location State": location_state,
+                            "Location Country": location_country,
+                            "Job URL": job.job_url,
+                            "Job Description": job.description.replace(",", "") if job.description else "No description available",
+                            "Job Source": source_name
+                        })
+                    else:
+                        print(f"❌ Ignored (Wrong State): {job.title} - {location_city}, {location_state} (Posted {job.date_posted})")
+                else:
+                    print(f"⏳ Ignored (Too Old): {job.title} - {location_city}, {location_state} (Posted {job.date_posted})")
+
+    print(f"\n✅ {len(all_jobs)} jobs retrieved in NY")
+    return all_jobs
+
+
+def save_jobs_to_csv(jobs, filename="jobspy_output.csv"):
+    """Save job data to a CSV file with custom formatting:
+       - Fields within a record are separated by the custom delimiter |~|
+       - Records are separated by a comma
+       - All commas in field values are removed
+       - Blank fields are replaced with 'Not Provided'
+    """
+    if not jobs:
+        print("⚠️ No jobs found matching criteria.")
+        return
+
+    # Remove old CSV file before writing
+    if os.path.exists(filename):
+        os.remove(filename)
+
+    fieldnames = [
+        "Job ID", "Job Title (Primary)", "Company Name", "Industry",
+        "Experience Level", "Job Type", "Is Remote", "Currency",
+        "Salary Min", "Salary Max", "Date Posted", "Location City",
+        "Location State", "Location Country", "Job URL", "Job Description",
+        "Job Source"
+    ]
+
+    # Build header record using custom field delimiter
+    header_record = "|~|".join(fieldnames)
+    records = [header_record]
+
+    for job in jobs:
+        row = []
+        for field in fieldnames:
+            value = str(job.get(field, "")).strip()
+            if not value:
+                value = "Not Provided"
+            # Remove all commas from the value
+            value = value.replace(",", "")
+            row.append(value)
+        # Join fields with the custom delimiter
+        record = "|~|".join(row)
+        records.append(record)
+    
+    # Join records with a comma as the record separator
+    output = ",".join(records)
+    with open(filename, "w", encoding="utf-8") as file:
+        file.write(output)
+
+    print(f"✅ Jobs saved to {filename} ({len(jobs)} entries)")
+
+
+# Run the scraper with multiple job searches
+job_data = scrape_jobs(
+    search_terms=search_terms,
+    results_wanted=results_wanted,
+    max_days_old=max_days_old,
+    target_state=target_state
+)
+
+# Save results to CSV with custom formatting
+save_jobs_to_csv(job_data)

jobspy/__init__.py

@@ -1,25 +1,27 @@
 from __future__ import annotations
 
-import pandas as pd
-from typing import Tuple
 from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Tuple
 
-from .jobs import JobType, Location
-from .scrapers.utils import set_logger_level, extract_salary, create_logger
-from .scrapers.indeed import IndeedScraper
-from .scrapers.ziprecruiter import ZipRecruiterScraper
-from .scrapers.glassdoor import GlassdoorScraper
-from .scrapers.google import GoogleJobsScraper
-from .scrapers.linkedin import LinkedInScraper
-from .scrapers.bayt import BaytScraper
-from .scrapers import SalarySource, ScraperInput, Site, JobResponse, Country
-from .scrapers.exceptions import (
-    LinkedInException,
-    IndeedException,
-    ZipRecruiterException,
-    GlassdoorException,
-    GoogleJobsException,
+import pandas as pd
+
+from jobspy.bayt import BaytScraper
+from jobspy.glassdoor import Glassdoor
+from jobspy.google import Google
+from jobspy.indeed import Indeed
+from jobspy.linkedin import LinkedIn
+from jobspy.model import JobType, Location, JobResponse, Country
+from jobspy.model import SalarySource, ScraperInput, Site
+from jobspy.util import (
+    set_logger_level,
+    extract_salary,
+    create_logger,
+    get_enum_from_value,
+    map_str_to_site,
+    convert_to_annual,
+    desired_order,
 )
+from jobspy.ziprecruiter import ZipRecruiter
 
 
 def scrape_jobs(
@@ -33,7 +35,6 @@ def scrape_jobs(
     easy_apply: bool | None = None,
     results_wanted: int = 15,
     country_indeed: str = "usa",
-    hyperlinks: bool = False,
     proxies: list[str] | str | None = None,
     ca_cert: str | None = None,
     description_format: str = "markdown",
@@ -46,28 +47,18 @@ def scrape_jobs(
     **kwargs,
 ) -> pd.DataFrame:
     """
-    Simultaneously scrapes job data from multiple job sites.
-    :return: pandas dataframe containing job data
+    Scrapes job data from job boards concurrently
+    :return: Pandas DataFrame containing job data
     """
     SCRAPER_MAPPING = {
-        Site.LINKEDIN: LinkedInScraper,
-        Site.INDEED: IndeedScraper,
-        Site.ZIP_RECRUITER: ZipRecruiterScraper,
-        Site.GLASSDOOR: GlassdoorScraper,
-        Site.GOOGLE: GoogleJobsScraper,
+        Site.LINKEDIN: LinkedIn,
+        Site.INDEED: Indeed,
+        Site.ZIP_RECRUITER: ZipRecruiter,
+        Site.GLASSDOOR: Glassdoor,
+        Site.GOOGLE: Google,
         Site.BAYT: BaytScraper,
     }
     set_logger_level(verbose)
-
-    def map_str_to_site(site_name: str) -> Site:
-        return Site[site_name.upper()]
-
-    def get_enum_from_value(value_str):
-        for job_type in JobType:
-            if value_str in job_type.value:
-                return job_type
-        raise Exception(f"Invalid job type: {value_str}")
-
     job_type = get_enum_from_value(job_type) if job_type else None
 
     def get_site_type():
@@ -127,28 +118,12 @@ def scrape_jobs(
             site_value, scraped_data = future.result()
             site_to_jobs_dict[site_value] = scraped_data
 
-    def convert_to_annual(job_data: dict):
-        if job_data["interval"] == "hourly":
-            job_data["min_amount"] *= 2080
-            job_data["max_amount"] *= 2080
-        if job_data["interval"] == "monthly":
-            job_data["min_amount"] *= 12
-            job_data["max_amount"] *= 12
-        if job_data["interval"] == "weekly":
-            job_data["min_amount"] *= 52
-            job_data["max_amount"] *= 52
-        if job_data["interval"] == "daily":
-            job_data["min_amount"] *= 260
-            job_data["max_amount"] *= 260
-        job_data["interval"] = "yearly"
-
     jobs_dfs: list[pd.DataFrame] = []
 
     for site, job_response in site_to_jobs_dict.items():
         for job in job_response.jobs:
             job_data = job.dict()
             job_url = job_data["job_url"]
-            job_data["job_url_hyper"] = f'<a href="{job_url}">{job_url}</a>'
             job_data["site"] = site
             job_data["company"] = job_data["company_name"]
             job_data["job_type"] = (
@@ -211,38 +186,6 @@ def scrape_jobs(
         # Step 2: Concatenate the filtered DataFrames
         jobs_df = pd.concat(filtered_dfs, ignore_index=True)
 
-        # Desired column order
-        desired_order = [
-            "id",
-            "site",
-            "job_url_hyper" if hyperlinks else "job_url",
-            "job_url_direct",
-            "title",
-            "company",
-            "location",
-            "date_posted",
-            "job_type",
-            "salary_source",
-            "interval",
-            "min_amount",
-            "max_amount",
-            "currency",
-            "is_remote",
-            "job_level",
-            "job_function",
-            "listing_type",
-            "emails",
-            "description",
-            "company_industry",
-            "company_url",
-            "company_logo",
-            "company_url_direct",
-            "company_addresses",
-            "company_num_employees",
-            "company_revenue",
-            "company_description",
-        ]
-
         # Step 3: Ensure all desired columns are present, adding missing ones as empty
         for column in desired_order:
             if column not in jobs_df.columns:

jobspy/bayt/__init__.py

@@ -1,10 +1,3 @@
-"""
-jobspy.scrapers.bayt
-~~~~~~~~~~~~~~~~~~~
-
-This module contains routines to scrape Bayt.
-"""
-
 from __future__ import annotations
 
 import random
@@ -12,9 +5,16 @@ import time
 
 from bs4 import BeautifulSoup
 
-from .. import Scraper, ScraperInput, Site
-from ..utils import create_logger, create_session
-from ...jobs import JobPost, JobResponse, Location, Country
+from jobspy.model import (
+    Scraper,
+    ScraperInput,
+    Site,
+    JobPost,
+    JobResponse,
+    Location,
+    Country,
+)
+from jobspy.util import create_logger, create_session
 
 log = create_logger("Bayt")
 

jobspy/exception.py

@@ -1,5 +1,5 @@
 """
-jobspy.scrapers.exceptions
+jobspy.jobboard.exceptions
 ~~~~~~~~~~~~~~~~~~~
 
 This module contains the set of Scrapers' exceptions.

jobspy/glassdoor/__init__.py

@@ -1,41 +1,38 @@
-"""
-jobspy.scrapers.glassdoor
-~~~~~~~~~~~~~~~~~~~
-
-This module contains routines to scrape Glassdoor.
-"""
-
 from __future__ import annotations
 
 import re
 import json
 import requests
-from typing import Optional, Tuple
+from typing import Tuple
 from datetime import datetime, timedelta
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
-from .constants import fallback_token, query_template, headers
-from .. import Scraper, ScraperInput, Site
-from ..utils import extract_emails_from_text, create_logger
-from ..exceptions import GlassdoorException
-from ..utils import (
+from jobspy.glassdoor.constant import fallback_token, query_template, headers
+from jobspy.glassdoor.util import (
+    get_cursor_for_page,
+    parse_compensation,
+    parse_location,
+)
+from jobspy.util import (
+    extract_emails_from_text,
+    create_logger,
     create_session,
     markdown_converter,
 )
-from ...jobs import (
+from jobspy.exception import GlassdoorException
+from jobspy.model import (
     JobPost,
-    Compensation,
-    CompensationInterval,
-    Location,
     JobResponse,
-    JobType,
     DescriptionFormat,
+    Scraper,
+    ScraperInput,
+    Site,
 )
 
 log = create_logger("Glassdoor")
 
 
-class GlassdoorScraper(Scraper):
+class Glassdoor(Scraper):
     def __init__(
         self, proxies: list[str] | str | None = None, ca_cert: str | None = None
     ):
@@ -146,7 +143,7 @@ class GlassdoorScraper(Scraper):
                 except Exception as exc:
                     raise GlassdoorException(f"Glassdoor generated an exception: {exc}")
 
-        return jobs, self.get_cursor_for_page(
+        return jobs, get_cursor_for_page(
             res_json["data"]["jobListings"]["paginationCursors"], page_num + 1
         )
 
@@ -185,9 +182,9 @@ class GlassdoorScraper(Scraper):
         if location_type == "S":
             is_remote = True
         else:
-            location = self.parse_location(location_name)
+            location = parse_location(location_name)
 
-        compensation = self.parse_compensation(job["header"])
+        compensation = parse_compensation(job["header"])
         try:
             description = self._fetch_job_description(job_id)
         except:
@@ -321,44 +318,3 @@ class GlassdoorScraper(Scraper):
                 {"filterKey": "jobType", "values": self.scraper_input.job_type.value[0]}
             )
         return json.dumps([payload])
-
-    @staticmethod
-    def parse_compensation(data: dict) -> Optional[Compensation]:
-        pay_period = data.get("payPeriod")
-        adjusted_pay = data.get("payPeriodAdjustedPay")
-        currency = data.get("payCurrency", "USD")
-        if not pay_period or not adjusted_pay:
-            return None
-
-        interval = None
-        if pay_period == "ANNUAL":
-            interval = CompensationInterval.YEARLY
-        elif pay_period:
-            interval = CompensationInterval.get_interval(pay_period)
-        min_amount = int(adjusted_pay.get("p10") // 1)
-        max_amount = int(adjusted_pay.get("p90") // 1)
-        return Compensation(
-            interval=interval,
-            min_amount=min_amount,
-            max_amount=max_amount,
-            currency=currency,
-        )
-
-    @staticmethod
-    def get_job_type_enum(job_type_str: str) -> list[JobType] | None:
-        for job_type in JobType:
-            if job_type_str in job_type.value:
-                return [job_type]
-
-    @staticmethod
-    def parse_location(location_name: str) -> Location | None:
-        if not location_name or location_name == "Remote":
-            return
-        city, _, state = location_name.partition(", ")
-        return Location(city=city, state=state)
-
-    @staticmethod
-    def get_cursor_for_page(pagination_cursors, page_num):
-        for cursor_data in pagination_cursors:
-            if cursor_data["pageNumber"] == page_num:
-                return cursor_data["cursor"]

jobspy/glassdoor/util.py

@@ -0,0 +1,42 @@
+from jobspy.model import Compensation, CompensationInterval, Location, JobType
+
+
+def parse_compensation(data: dict) -> Compensation | None:
+    pay_period = data.get("payPeriod")
+    adjusted_pay = data.get("payPeriodAdjustedPay")
+    currency = data.get("payCurrency", "USD")
+    if not pay_period or not adjusted_pay:
+        return None
+
+    interval = None
+    if pay_period == "ANNUAL":
+        interval = CompensationInterval.YEARLY
+    elif pay_period:
+        interval = CompensationInterval.get_interval(pay_period)
+    min_amount = int(adjusted_pay.get("p10") // 1)
+    max_amount = int(adjusted_pay.get("p90") // 1)
+    return Compensation(
+        interval=interval,
+        min_amount=min_amount,
+        max_amount=max_amount,
+        currency=currency,
+    )
+
+
+def get_job_type_enum(job_type_str: str) -> list[JobType] | None:
+    for job_type in JobType:
+        if job_type_str in job_type.value:
+            return [job_type]
+
+
+def parse_location(location_name: str) -> Location | None:
+    if not location_name or location_name == "Remote":
+        return
+    city, _, state = location_name.partition(", ")
+    return Location(city=city, state=state)
+
+
+def get_cursor_for_page(pagination_cursors, page_num):
+    for cursor_data in pagination_cursors:
+        if cursor_data["pageNumber"] == page_num:
+            return cursor_data["cursor"]

jobspy/google/__init__.py

@@ -1,10 +1,3 @@
-"""
-jobspy.scrapers.google
-~~~~~~~~~~~~~~~~~~~
-
-This module contains routines to scrape Google.
-"""
-
 from __future__ import annotations
 
 import math
@@ -13,23 +6,21 @@ import json
 from typing import Tuple
 from datetime import datetime, timedelta
 
-from .constants import headers_jobs, headers_initial, async_param
-from .. import Scraper, ScraperInput, Site
-from ..utils import extract_emails_from_text, create_logger, extract_job_type
-from ..utils import (
-    create_session,
-)
-from ...jobs import (
+from jobspy.google.constant import headers_jobs, headers_initial, async_param
+from jobspy.model import (
+    Scraper,
+    ScraperInput,
+    Site,
     JobPost,
     JobResponse,
     Location,
     JobType,
 )
-
-log = create_logger("Google")
+from jobspy.util import extract_emails_from_text, extract_job_type, create_session
+from jobspy.google.util import log, find_job_info_initial_page, find_job_info
 
 
-class GoogleJobsScraper(Scraper):
+class Google(Scraper):
     def __init__(
         self, proxies: list[str] | str | None = None, ca_cert: str | None = None
     ):
@@ -135,7 +126,7 @@ class GoogleJobsScraper(Scraper):
         pattern_fc = r'<div jsname="Yust4d"[^>]+data-async-fc="([^"]+)"'
         match_fc = re.search(pattern_fc, response.text)
         data_async_fc = match_fc.group(1) if match_fc else None
-        jobs_raw = self._find_job_info_initial_page(response.text)
+        jobs_raw = find_job_info_initial_page(response.text)
         jobs = []
         for job_raw in jobs_raw:
             job_post = self._parse_job(job_raw)
@@ -167,7 +158,7 @@ class GoogleJobsScraper(Scraper):
                 continue
             job_d = json.loads(job_data)
 
-            job_info = self._find_job_info(job_d)
+            job_info = find_job_info(job_d)
             job_post = self._parse_job(job_info)
             if job_post:
                 jobs_on_page.append(job_post)
@@ -209,39 +200,3 @@ class GoogleJobsScraper(Scraper):
             job_type=extract_job_type(description),
         )
         return job_post
-
-    @staticmethod
-    def _find_job_info(jobs_data: list | dict) -> list | None:
-        """Iterates through the JSON data to find the job listings"""
-        if isinstance(jobs_data, dict):
-            for key, value in jobs_data.items():
-                if key == "520084652" and isinstance(value, list):
-                    return value
-                else:
-                    result = GoogleJobsScraper._find_job_info(value)
-                    if result:
-                        return result
-        elif isinstance(jobs_data, list):
-            for item in jobs_data:
-                result = GoogleJobsScraper._find_job_info(item)
-                if result:
-                    return result
-        return None
-
-    @staticmethod
-    def _find_job_info_initial_page(html_text: str):
-        pattern = f'520084652":(' + r"\[.*?\]\s*])\s*}\s*]\s*]\s*]\s*]\s*]"
-        results = []
-        matches = re.finditer(pattern, html_text)
-
-        import json
-
-        for match in matches:
-            try:
-                parsed_data = json.loads(match.group(1))
-                results.append(parsed_data)
-
-            except json.JSONDecodeError as e:
-                log.error(f"Failed to parse match: {str(e)}")
-                results.append({"raw_match": match.group(0), "error": str(e)})
-        return results

jobspy/google/util.py

@@ -0,0 +1,41 @@
+import re
+
+from jobspy.util import create_logger
+
+log = create_logger("Google")
+
+
+def find_job_info(jobs_data: list | dict) -> list | None:
+    """Iterates through the JSON data to find the job listings"""
+    if isinstance(jobs_data, dict):
+        for key, value in jobs_data.items():
+            if key == "520084652" and isinstance(value, list):
+                return value
+            else:
+                result = find_job_info(value)
+                if result:
+                    return result
+    elif isinstance(jobs_data, list):
+        for item in jobs_data:
+            result = find_job_info(item)
+            if result:
+                return result
+    return None
+
+
+def find_job_info_initial_page(html_text: str):
+    pattern = f'520084652":(' + r"\[.*?\]\s*])\s*}\s*]\s*]\s*]\s*]\s*]"
+    results = []
+    matches = re.finditer(pattern, html_text)
+
+    import json
+
+    for match in matches:
+        try:
+            parsed_data = json.loads(match.group(1))
+            results.append(parsed_data)
+
+        except json.JSONDecodeError as e:
+            log.error(f"Failed to parse match: {str(e)}")
+            results.append({"raw_match": match.group(0), "error": str(e)})
+    return results

jobspy/indeed/__init__.py

@@ -1,39 +1,32 @@
-"""
-jobspy.scrapers.indeed
-~~~~~~~~~~~~~~~~~~~
-
-This module contains routines to scrape Indeed.
-"""
-
 from __future__ import annotations
 
 import math
-from typing import Tuple
 from datetime import datetime
+from typing import Tuple
 
-from .constants import job_search_query, api_headers
-from .. import Scraper, ScraperInput, Site
-from ..utils import (
-    extract_emails_from_text,
-    get_enum_from_job_type,
-    markdown_converter,
-    create_session,
-    create_logger,
-)
-from ...jobs import (
+from jobspy.indeed.constant import job_search_query, api_headers
+from jobspy.indeed.util import is_job_remote, get_compensation, get_job_type
+from jobspy.model import (
+    Scraper,
+    ScraperInput,
+    Site,
     JobPost,
-    Compensation,
-    CompensationInterval,
     Location,
     JobResponse,
     JobType,
     DescriptionFormat,
 )
+from jobspy.util import (
+    extract_emails_from_text,
+    markdown_converter,
+    create_session,
+    create_logger,
+)
 
 log = create_logger("Indeed")
 
 
-class IndeedScraper(Scraper):
+class Indeed(Scraper):
     def __init__(
         self, proxies: list[str] | str | None = None, ca_cert: str | None = None
     ):
@@ -212,8 +205,10 @@ class IndeedScraper(Scraper):
         description = job["description"]["html"]
         if self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
             description = markdown_converter(description)
+            description = description.replace(",", "")
 
-        job_type = self._get_job_type(job["attributes"])
+
+        job_type = get_job_type(job["attributes"])
         timestamp_seconds = job["datePublished"] / 1000
         date_posted = datetime.fromtimestamp(timestamp_seconds).strftime("%Y-%m-%d")
         employer = job["employer"].get("dossier") if job["employer"] else None
@@ -234,14 +229,14 @@ class IndeedScraper(Scraper):
                 country=job.get("location", {}).get("countryCode"),
             ),
             job_type=job_type,
-            compensation=self._get_compensation(job["compensation"]),
+            compensation=get_compensation(job["compensation"]),
             date_posted=date_posted,
             job_url=job_url,
             job_url_direct=(
                 job["recruit"].get("viewJobUrl") if job.get("recruit") else None
             ),
             emails=extract_emails_from_text(description) if description else None,
-            is_remote=self._is_job_remote(job, description),
+            is_remote=is_job_remote(job, description),
             company_addresses=(
                 employer_details["addresses"][0]
                 if employer_details.get("addresses")
@@ -265,86 +260,3 @@ class IndeedScraper(Scraper):
                 else None
             ),
         )
-
-    @staticmethod
-    def _get_job_type(attributes: list) -> list[JobType]:
-        """
-        Parses the attributes to get list of job types
-        :param attributes:
-        :return: list of JobType
-        """
-        job_types: list[JobType] = []
-        for attribute in attributes:
-            job_type_str = attribute["label"].replace("-", "").replace(" ", "").lower()
-            job_type = get_enum_from_job_type(job_type_str)
-            if job_type:
-                job_types.append(job_type)
-        return job_types
-
-    @staticmethod
-    def _get_compensation(compensation: dict) -> Compensation | None:
-        """
-        Parses the job to get compensation
-        :param job:
-        :return: compensation object
-        """
-        if not compensation["baseSalary"] and not compensation["estimated"]:
-            return None
-        comp = (
-            compensation["baseSalary"]
-            if compensation["baseSalary"]
-            else compensation["estimated"]["baseSalary"]
-        )
-        if not comp:
-            return None
-        interval = IndeedScraper._get_compensation_interval(comp["unitOfWork"])
-        if not interval:
-            return None
-        min_range = comp["range"].get("min")
-        max_range = comp["range"].get("max")
-        return Compensation(
-            interval=interval,
-            min_amount=int(min_range) if min_range is not None else None,
-            max_amount=int(max_range) if max_range is not None else None,
-            currency=(
-                compensation["estimated"]["currencyCode"]
-                if compensation["estimated"]
-                else compensation["currencyCode"]
-            ),
-        )
-
-    @staticmethod
-    def _is_job_remote(job: dict, description: str) -> bool:
-        """
-        Searches the description, location, and attributes to check if job is remote
-        """
-        remote_keywords = ["remote", "work from home", "wfh"]
-        is_remote_in_attributes = any(
-            any(keyword in attr["label"].lower() for keyword in remote_keywords)
-            for attr in job["attributes"]
-        )
-        is_remote_in_description = any(
-            keyword in description.lower() for keyword in remote_keywords
-        )
-        is_remote_in_location = any(
-            keyword in job["location"]["formatted"]["long"].lower()
-            for keyword in remote_keywords
-        )
-        return (
-            is_remote_in_attributes or is_remote_in_description or is_remote_in_location
-        )
-
-    @staticmethod
-    def _get_compensation_interval(interval: str) -> CompensationInterval:
-        interval_mapping = {
-            "DAY": "DAILY",
-            "YEAR": "YEARLY",
-            "HOUR": "HOURLY",
-            "WEEK": "WEEKLY",
-            "MONTH": "MONTHLY",
-        }
-        mapped_interval = interval_mapping.get(interval.upper(), None)
-        if mapped_interval and mapped_interval in CompensationInterval.__members__:
-            return CompensationInterval[mapped_interval]
-        else:
-            raise ValueError(f"Unsupported interval: {interval}")

jobspy/indeed/util.py

@@ -0,0 +1,80 @@
+from jobspy.model import CompensationInterval, JobType, Compensation
+from jobspy.util import get_enum_from_job_type
+
+
+def get_job_type(attributes: list) -> list[JobType]:
+    """
+    Parses the attributes to get list of job types
+    :param attributes:
+    :return: list of JobType
+    """
+    job_types: list[JobType] = []
+    for attribute in attributes:
+        job_type_str = attribute["label"].replace("-", "").replace(" ", "").lower()
+        job_type = get_enum_from_job_type(job_type_str)
+        if job_type:
+            job_types.append(job_type)
+    return job_types
+
+
+def get_compensation(compensation: dict) -> Compensation | None:
+    """
+    Parses the job to get compensation
+    :param sssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrompensation:
+    :return: compensation object
+    """
+    if not compensation["baseSalary"] and not compensation["estimated"]:
+        return None
+    comp = (
+        compensation["baseSalary"]
+        if compensation["baseSalary"]
+        else compensation["estimated"]["baseSalary"]
+    )
+    if not comp:
+        return None
+    interval = get_compensation_interval(comp["unitOfWork"])
+    if not interval:
+        return None
+    min_range = comp["range"].get("min")
+    max_range = comp["range"].get("max")
+    return Compensation(
+        interval=interval,
+        min_amount=int(min_range) if min_range is not None else None,
+        max_amount=int(max_range) if max_range is not None else None,
+        currency=(
+            compensation["estimated"]["currencyCode"]
+            if compensation["estimated"]
+            else compensation["currencyCode"]
+        ),
+    )
+
+
+def is_job_remote(job: dict, description: str) -> bool:
+    """
+    Searches the description, location, and attributes to check if job is remote
+    """
+    remote_keywords = ["remote", "work from home", "wfh"]
+    is_remote_in_attributes = any(
+        any(keyword in attr["label"].lower() for keyword in remote_keywords)
+        for attr in job["attributes"]
+    )
+    is_remote_in_location = any(
+        keyword in job["location"]["formatted"]["long"].lower()
+        for keyword in remote_keywords
+    )
+    return is_remote_in_attributes or is_remote_in_location
+
+
+def get_compensation_interval(interval: str) -> CompensationInterval:
+    interval_mapping = {
+        "DAY": "DAILY",
+        "YEAR": "YEARLY",
+        "HOUR": "HOURLY",
+        "WEEK": "WEEKLY",
+        "MONTH": "MONTHLY",
+    }
+    mapped_interval = interval_mapping.get(interval.upper(), None)
+    if mapped_interval and mapped_interval in CompensationInterval.__members__:
+        return CompensationInterval[mapped_interval]
+    else:
+        raise ValueError(f"Unsupported interval: {interval}")

jobspy/linkedin/__init__.py

@@ -1,47 +1,48 @@
-"""
-jobspy.scrapers.linkedin
-~~~~~~~~~~~~~~~~~~~
-
-This module contains routines to scrape LinkedIn.
-"""
-
 from __future__ import annotations
 
 import math
-import time
 import random
-import regex as re
-from typing import Optional
+import time
 from datetime import datetime
-
-from bs4.element import Tag
-from bs4 import BeautifulSoup
+from typing import Optional
 from urllib.parse import urlparse, urlunparse, unquote
 
-from .constants import headers
-from .. import Scraper, ScraperInput, Site
-from ..exceptions import LinkedInException
-from ..utils import create_session, remove_attributes, create_logger
-from ...jobs import (
+import regex as re
+from bs4 import BeautifulSoup
+from bs4.element import Tag
+
+from jobspy.exception import LinkedInException
+from jobspy.linkedin.constant import headers
+from jobspy.linkedin.util import (
+    job_type_code,
+    parse_job_type,
+    parse_job_level,
+    parse_company_industry,
+)
+from jobspy.model import (
     JobPost,
     Location,
     JobResponse,
-    JobType,
     Country,
     Compensation,
     DescriptionFormat,
+    Scraper,
+    ScraperInput,
+    Site,
 )
-from ..utils import (
+from jobspy.util import (
     extract_emails_from_text,
-    get_enum_from_job_type,
     currency_parser,
     markdown_converter,
+    create_session,
+    remove_attributes,
+    create_logger,
 )
 
 log = create_logger("LinkedIn")
 
 
-class LinkedInScraper(Scraper):
+class LinkedIn(Scraper):
     base_url = "https://www.linkedin.com"
     delay = 3
     band_delay = 4
@@ -95,7 +96,7 @@ class LinkedInScraper(Scraper):
                 "distance": scraper_input.distance,
                 "f_WT": 2 if scraper_input.is_remote else None,
                 "f_JT": (
-                    self.job_type_code(scraper_input.job_type)
+                    job_type_code(scraper_input.job_type)
                     if scraper_input.job_type
                     else None
                 ),
@@ -216,6 +217,8 @@ class LinkedInScraper(Scraper):
         job_details = {}
         if full_descr:
             job_details = self._get_job_details(job_id)
+            description = description.replace(",", "")
+
 
         return JobPost(
             id=f"li-{job_id}",
@@ -282,9 +285,9 @@ class LinkedInScraper(Scraper):
         )
         return {
             "description": description,
-            "job_level": self._parse_job_level(soup),
-            "company_industry": self._parse_company_industry(soup),
-            "job_type": self._parse_job_type(soup),
+            "job_level": parse_job_level(soup),
+            "company_industry": parse_company_industry(soup),
+            "job_type": parse_job_type(soup),
             "job_url_direct": self._parse_job_url_direct(soup),
             "company_logo": company_logo,
             "job_function": job_function,
@@ -316,77 +319,6 @@ class LinkedInScraper(Scraper):
                 location = Location(city=city, state=state, country=country)
         return location
 
-    @staticmethod
-    def _parse_job_type(soup_job_type: BeautifulSoup) -> list[JobType] | None:
-        """
-        Gets the job type from job page
-        :param soup_job_type:
-        :return: JobType
-        """
-        h3_tag = soup_job_type.find(
-            "h3",
-            class_="description__job-criteria-subheader",
-            string=lambda text: "Employment type" in text,
-        )
-        employment_type = None
-        if h3_tag:
-            employment_type_span = h3_tag.find_next_sibling(
-                "span",
-                class_="description__job-criteria-text description__job-criteria-text--criteria",
-            )
-            if employment_type_span:
-                employment_type = employment_type_span.get_text(strip=True)
-                employment_type = employment_type.lower()
-                employment_type = employment_type.replace("-", "")
-
-        return [get_enum_from_job_type(employment_type)] if employment_type else []
-
-    @staticmethod
-    def _parse_job_level(soup_job_level: BeautifulSoup) -> str | None:
-        """
-        Gets the job level from job page
-        :param soup_job_level:
-        :return: str
-        """
-        h3_tag = soup_job_level.find(
-            "h3",
-            class_="description__job-criteria-subheader",
-            string=lambda text: "Seniority level" in text,
-        )
-        job_level = None
-        if h3_tag:
-            job_level_span = h3_tag.find_next_sibling(
-                "span",
-                class_="description__job-criteria-text description__job-criteria-text--criteria",
-            )
-            if job_level_span:
-                job_level = job_level_span.get_text(strip=True)
-
-        return job_level
-
-    @staticmethod
-    def _parse_company_industry(soup_industry: BeautifulSoup) -> str | None:
-        """
-        Gets the company industry from job page
-        :param soup_industry:
-        :return: str
-        """
-        h3_tag = soup_industry.find(
-            "h3",
-            class_="description__job-criteria-subheader",
-            string=lambda text: "Industries" in text,
-        )
-        industry = None
-        if h3_tag:
-            industry_span = h3_tag.find_next_sibling(
-                "span",
-                class_="description__job-criteria-text description__job-criteria-text--criteria",
-            )
-            if industry_span:
-                industry = industry_span.get_text(strip=True)
-
-        return industry
-
     def _parse_job_url_direct(self, soup: BeautifulSoup) -> str | None:
         """
         Gets the job url direct from job page
@@ -403,13 +335,3 @@ class LinkedInScraper(Scraper):
                 job_url_direct = unquote(job_url_direct_match.group())
 
         return job_url_direct
-
-    @staticmethod
-    def job_type_code(job_type_enum: JobType) -> str:
-        return {
-            JobType.FULL_TIME: "F",
-            JobType.PART_TIME: "P",
-            JobType.INTERNSHIP: "I",
-            JobType.CONTRACT: "C",
-            JobType.TEMPORARY: "T",
-        }.get(job_type_enum, "")

jobspy/linkedin/util.py

@@ -0,0 +1,85 @@
+from bs4 import BeautifulSoup
+
+from jobspy.model import JobType
+from jobspy.util import get_enum_from_job_type
+
+
+def job_type_code(job_type_enum: JobType) -> str:
+    return {
+        JobType.FULL_TIME: "F",
+        JobType.PART_TIME: "P",
+        JobType.INTERNSHIP: "I",
+        JobType.CONTRACT: "C",
+        JobType.TEMPORARY: "T",
+    }.get(job_type_enum, "")
+
+
+def parse_job_type(soup_job_type: BeautifulSoup) -> list[JobType] | None:
+    """
+    Gets the job type from job page
+    :param soup_job_type:
+    :return: JobType
+    """
+    h3_tag = soup_job_type.find(
+        "h3",
+        class_="description__job-criteria-subheader",
+        string=lambda text: "Employment type" in text,
+    )
+    employment_type = None
+    if h3_tag:
+        employment_type_span = h3_tag.find_next_sibling(
+            "span",
+            class_="description__job-criteria-text description__job-criteria-text--criteria",
+        )
+        if employment_type_span:
+            employment_type = employment_type_span.get_text(strip=True)
+            employment_type = employment_type.lower()
+            employment_type = employment_type.replace("-", "")
+
+    return [get_enum_from_job_type(employment_type)] if employment_type else []
+
+
+def parse_job_level(soup_job_level: BeautifulSoup) -> str | None:
+    """
+    Gets the job level from job page
+    :param soup_job_level:
+    :return: str
+    """
+    h3_tag = soup_job_level.find(
+        "h3",
+        class_="description__job-criteria-subheader",
+        string=lambda text: "Seniority level" in text,
+    )
+    job_level = None
+    if h3_tag:
+        job_level_span = h3_tag.find_next_sibling(
+            "span",
+            class_="description__job-criteria-text description__job-criteria-text--criteria",
+        )
+        if job_level_span:
+            job_level = job_level_span.get_text(strip=True)
+
+    return job_level
+
+
+def parse_company_industry(soup_industry: BeautifulSoup) -> str | None:
+    """
+    Gets the company industry from job page
+    :param soup_industry:
+    :return: str
+    """
+    h3_tag = soup_industry.find(
+        "h3",
+        class_="description__job-criteria-subheader",
+        string=lambda text: "Industries" in text,
+    )
+    industry = None
+    if h3_tag:
+        industry_span = h3_tag.find_next_sibling(
+            "span",
+            class_="description__job-criteria-text description__job-criteria-text--criteria",
+        )
+        if industry_span:
+            industry = industry_span.get_text(strip=True)
+
+    return industry

jobspy/model.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from abc import ABC, abstractmethod
 from typing import Optional
 from datetime import date
 from enum import Enum
@@ -68,16 +69,20 @@ class Country(Enum):
     AUSTRIA = ("austria", "at", "at")
     BAHRAIN = ("bahrain", "bh")
     BELGIUM = ("belgium", "be", "fr:be")
+    BULGARIA = ("bulgaria", "bg")
     BRAZIL = ("brazil", "br", "com.br")
     CANADA = ("canada", "ca", "ca")
     CHILE = ("chile", "cl")
     CHINA = ("china", "cn")
     COLOMBIA = ("colombia", "co")
     COSTARICA = ("costa rica", "cr")
+    CROATIA = ("croatia", "hr")
+    CYPRUS = ("cyprus", "cy")
     CZECHREPUBLIC = ("czech republic,czechia", "cz")
     DENMARK = ("denmark", "dk")
     ECUADOR = ("ecuador", "ec")
     EGYPT = ("egypt", "eg")
+    ESTONIA = ("estonia", "ee")
     FINLAND = ("finland", "fi")
     FRANCE = ("france", "fr", "fr")
     GERMANY = ("germany", "de", "de")
@@ -91,6 +96,8 @@ class Country(Enum):
     ITALY = ("italy", "it", "it")
     JAPAN = ("japan", "jp")
     KUWAIT = ("kuwait", "kw")
+    LATVIA = ("latvia", "lv")
+    LITHUANIA = ("lithuania", "lt")
     LUXEMBOURG = ("luxembourg", "lu")
     MALAYSIA = ("malaysia", "malaysia:my", "com")
     MALTA = ("malta", "malta:mt", "mt")
@@ -111,6 +118,8 @@ class Country(Enum):
     ROMANIA = ("romania", "ro")
     SAUDIARABIA = ("saudi arabia", "sa")
     SINGAPORE = ("singapore", "sg", "sg")
+    SLOVAKIA = ("slovakia", "sk")
+    SLOVENIA = ("slovenia", "sl")
     SOUTHAFRICA = ("south africa", "za")
     SOUTHKOREA = ("south korea", "kr")
     SPAIN = ("spain", "es", "es")
@@ -265,3 +274,49 @@ class JobPost(BaseModel):
 
 class JobResponse(BaseModel):
     jobs: list[JobPost] = []
+
+
+class Site(Enum):
+    LINKEDIN = "linkedin"
+    INDEED = "indeed"
+    ZIP_RECRUITER = "zip_recruiter"
+    GLASSDOOR = "glassdoor"
+    GOOGLE = "google"
+    BAYT = "bayt"
+
+
+class SalarySource(Enum):
+    DIRECT_DATA = "direct_data"
+    DESCRIPTION = "description"
+
+
+class ScraperInput(BaseModel):
+    site_type: list[Site]
+    search_term: str | None = None
+    google_search_term: str | None = None
+
+    location: str | None = None
+    country: Country | None = Country.USA
+    distance: int | None = None
+    is_remote: bool = False
+    job_type: JobType | None = None
+    easy_apply: bool | None = None
+    offset: int = 0
+    linkedin_fetch_description: bool = False
+    linkedin_company_ids: list[int] | None = None
+    description_format: DescriptionFormat | None = DescriptionFormat.MARKDOWN
+
+    results_wanted: int = 15
+    hours_old: int | None = None
+
+
+class Scraper(ABC):
+    def __init__(
+        self, site: Site, proxies: list[str] | None = None, ca_cert: str | None = None
+    ):
+        self.site = site
+        self.proxies = proxies
+        self.ca_cert = ca_cert
+
+    @abstractmethod
+    def scrape(self, scraper_input: ScraperInput) -> JobResponse: ...

jobspy/util.py

@@ -11,7 +11,7 @@ import urllib3
 from markdownify import markdownify as md
 from requests.adapters import HTTPAdapter, Retry
 
-from ..jobs import CompensationInterval, JobType
+from jobspy.model import CompensationInterval, JobType, Site
 
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
@@ -286,3 +286,62 @@ def extract_job_type(description: str):
             listing_types.append(key)
 
     return listing_types if listing_types else None
+
+
+def map_str_to_site(site_name: str) -> Site:
+    return Site[site_name.upper()]
+
+
+def get_enum_from_value(value_str):
+    for job_type in JobType:
+        if value_str in job_type.value:
+            return job_type
+    raise Exception(f"Invalid job type: {value_str}")
+
+
+def convert_to_annual(job_data: dict):
+    if job_data["interval"] == "hourly":
+        job_data["min_amount"] *= 2080
+        job_data["max_amount"] *= 2080
+    if job_data["interval"] == "monthly":
+        job_data["min_amount"] *= 12
+        job_data["max_amount"] *= 12
+    if job_data["interval"] == "weekly":
+        job_data["min_amount"] *= 52
+        job_data["max_amount"] *= 52
+    if job_data["interval"] == "daily":
+        job_data["min_amount"] *= 260
+        job_data["max_amount"] *= 260
+    job_data["interval"] = "yearly"
+
+
+desired_order = [
+    "id",
+    "site",
+    "job_url",
+    "job_url_direct",
+    "title",
+    "company",
+    "location",
+    "date_posted",
+    "job_type",
+    "salary_source",
+    "interval",
+    "min_amount",
+    "max_amount",
+    "currency",
+    "is_remote",
+    "job_level",
+    "job_function",
+    "listing_type",
+    "emails",
+    "description",
+    "company_industry",
+    "company_url",
+    "company_logo",
+    "company_url_direct",
+    "company_addresses",
+    "company_num_employees",
+    "company_revenue",
+    "company_description",
+]

jobspy/ziprecruiter/__init__.py

@@ -1,10 +1,3 @@
-"""
-jobspy.scrapers.ziprecruiter
-~~~~~~~~~~~~~~~~~~~
-
-This module contains routines to scrape ZipRecruiter.
-"""
-
 from __future__ import annotations
 
 import json
@@ -13,33 +6,34 @@ import re
 import time
 from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime
-from typing import Optional, Tuple, Any
 
 from bs4 import BeautifulSoup
 
-from .constants import headers
-from .. import Scraper, ScraperInput, Site
-from ..utils import (
+from jobspy.ziprecruiter.constant import headers, get_cookie_data
+from jobspy.util import (
     extract_emails_from_text,
     create_session,
     markdown_converter,
     remove_attributes,
     create_logger,
 )
-from ...jobs import (
+from jobspy.model import (
     JobPost,
     Compensation,
     Location,
     JobResponse,
-    JobType,
     Country,
     DescriptionFormat,
+    Scraper,
+    ScraperInput,
+    Site,
 )
+from jobspy.ziprecruiter.util import get_job_type_enum, add_params
 
 log = create_logger("ZipRecruiter")
 
 
-class ZipRecruiterScraper(Scraper):
+class ZipRecruiter(Scraper):
     base_url = "https://www.ziprecruiter.com"
     api_url = "https://api.ziprecruiter.com"
 
@@ -90,7 +84,7 @@ class ZipRecruiterScraper(Scraper):
 
     def _find_jobs_in_page(
         self, scraper_input: ScraperInput, continue_token: str | None = None
-    ) -> Tuple[list[JobPost], Optional[str]]:
+    ) -> tuple[list[JobPost], str | None]:
         """
         Scrapes a page of ZipRecruiter for jobs with scraper_input criteria
         :param scraper_input:
@@ -98,7 +92,7 @@ class ZipRecruiterScraper(Scraper):
         :return: jobs found on page
         """
         jobs_list = []
-        params = self._add_params(scraper_input)
+        params = add_params(scraper_input)
         if continue_token:
             params["continue_from"] = continue_token
         try:
@@ -151,7 +145,7 @@ class ZipRecruiterScraper(Scraper):
         location = Location(
             city=job.get("job_city"), state=job.get("job_state"), country=country_enum
         )
-        job_type = self._get_job_type_enum(
+        job_type = get_job_type_enum(
             job.get("employment_type", "").replace("_", "").lower()
         )
         date_posted = datetime.fromisoformat(job["posted_time"].rstrip("Z")).date()
@@ -200,13 +194,17 @@ class ZipRecruiterScraper(Scraper):
                 else ""
             )
             description_full = job_description_clean + company_description_clean
-            script_tag = soup.find("script", type="application/json")
-            if script_tag:
-                job_json = json.loads(script_tag.string)
-                job_url_val = job_json["model"].get("saveJobURL", "")
-                m = re.search(r"job_url=(.+)", job_url_val)
-                if m:
-                    job_url_direct = m.group(1)
+
+            try:
+                script_tag = soup.find("script", type="application/json")
+                if script_tag:
+                    job_json = json.loads(script_tag.string)
+                    job_url_val = job_json["model"].get("saveJobURL", "")
+                    m = re.search(r"job_url=(.+)", job_url_val)
+                    if m:
+                        job_url_direct = m.group(1)
+            except:
+                job_url_direct = None
 
             if self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
                 description_full = markdown_converter(description_full)
@@ -217,51 +215,5 @@ class ZipRecruiterScraper(Scraper):
         """
         Sends a session event to the API with device properties.
         """
-        data = [
-            ("event_type", "session"),
-            ("logged_in", "false"),
-            ("number_of_retry", "1"),
-            ("property", "model:iPhone"),
-            ("property", "os:iOS"),
-            ("property", "locale:en_us"),
-            ("property", "app_build_number:4734"),
-            ("property", "app_version:91.0"),
-            ("property", "manufacturer:Apple"),
-            ("property", "timestamp:2025-01-12T12:04:42-06:00"),
-            ("property", "screen_height:852"),
-            ("property", "os_version:16.6.1"),
-            ("property", "source:install"),
-            ("property", "screen_width:393"),
-            ("property", "device_model:iPhone 14 Pro"),
-            ("property", "brand:Apple"),
-        ]
-
         url = f"{self.api_url}/jobs-app/event"
-        self.session.post(url, data=data)
-
-    @staticmethod
-    def _get_job_type_enum(job_type_str: str) -> list[JobType] | None:
-        for job_type in JobType:
-            if job_type_str in job_type.value:
-                return [job_type]
-        return None
-
-    @staticmethod
-    def _add_params(scraper_input) -> dict[str, str | Any]:
-        params = {
-            "search": scraper_input.search_term,
-            "location": scraper_input.location,
-        }
-        if scraper_input.hours_old:
-            params["days"] = max(scraper_input.hours_old // 24, 1)
-        job_type_map = {JobType.FULL_TIME: "full_time", JobType.PART_TIME: "part_time"}
-        if scraper_input.job_type:
-            job_type = scraper_input.job_type
-            params["employment_type"] = job_type_map.get(job_type, job_type.value[0])
-        if scraper_input.easy_apply:
-            params["zipapply"] = 1
-        if scraper_input.is_remote:
-            params["remote"] = 1
-        if scraper_input.distance:
-            params["radius"] = scraper_input.distance
-        return {k: v for k, v in params.items() if v is not None}
+        self.session.post(url, data=get_cookie_data)

jobspy/ziprecruiter/constant.py

@@ -0,0 +1,29 @@
+headers = {
+    "Host": "api.ziprecruiter.com",
+    "accept": "*/*",
+    "x-zr-zva-override": "100000000;vid:ZT1huzm_EQlDTVEc",
+    "x-pushnotificationid": "0ff4983d38d7fc5b3370297f2bcffcf4b3321c418f5c22dd152a0264707602a0",
+    "x-deviceid": "D77B3A92-E589-46A4-8A39-6EF6F1D86006",
+    "user-agent": "Job Search/87.0 (iPhone; CPU iOS 16_6_1 like Mac OS X)",
+    "authorization": "Basic YTBlZjMyZDYtN2I0Yy00MWVkLWEyODMtYTI1NDAzMzI0YTcyOg==",
+    "accept-language": "en-US,en;q=0.9",
+}
+
+get_cookie_data = [
+    ("event_type", "session"),
+    ("logged_in", "false"),
+    ("number_of_retry", "1"),
+    ("property", "model:iPhone"),
+    ("property", "os:iOS"),
+    ("property", "locale:en_us"),
+    ("property", "app_build_number:4734"),
+    ("property", "app_version:91.0"),
+    ("property", "manufacturer:Apple"),
+    ("property", "timestamp:2025-01-12T12:04:42-06:00"),
+    ("property", "screen_height:852"),
+    ("property", "os_version:16.6.1"),
+    ("property", "source:install"),
+    ("property", "screen_width:393"),
+    ("property", "device_model:iPhone 14 Pro"),
+    ("property", "brand:Apple"),
+]

jobspy/ziprecruiter/util.py

@@ -0,0 +1,31 @@
+from jobspy.model import JobType
+
+
+def add_params(scraper_input) -> dict[str, str | int]:
+    params: dict[str, str | int] = {
+        "search": scraper_input.search_term,
+        "location": scraper_input.location,
+    }
+    if scraper_input.hours_old:
+        params["days"] = max(scraper_input.hours_old // 24, 1)
+
+    job_type_map = {JobType.FULL_TIME: "full_time", JobType.PART_TIME: "part_time"}
+    if scraper_input.job_type:
+        job_type = scraper_input.job_type
+        params["employment_type"] = job_type_map.get(job_type, job_type.value[0])
+
+    if scraper_input.easy_apply:
+        params["zipapply"] = 1
+    if scraper_input.is_remote:
+        params["remote"] = 1
+    if scraper_input.distance:
+        params["radius"] = scraper_input.distance
+
+    return {k: v for k, v in params.items() if v is not None}
+
+
+def get_job_type_enum(job_type_str: str) -> list[JobType] | None:
+    for job_type in JobType:
+        if job_type_str in job_type.value:
+            return [job_type]
+    return None

pyproject.toml

@@ -4,21 +4,20 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "python-jobspy"
-version = "1.1.76"
-description = "Job scraper for LinkedIn, Indeed, Glassdoor & ZipRecruiter"
-authors = [ "Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>",]
-homepage = "https://github.com/Bunsly/JobSpy"
+version = "1.1.78"
+description = "Job scraper for LinkedIn, Indeed, Glassdoor, ZipRecruiter & Bayt"
+authors = ["Cullen Watson <cullen@cullenwatson.com>", "Zachary Hampton <zachary@zacharysproducts.com>"]
+homepage = "https://github.com/cullenwatson/JobSpy"
 readme = "README.md"
-keywords = [ "jobs-scraper", "linkedin", "indeed", "glassdoor", "ziprecruiter",]
+keywords = [ "jobs-scraper", "linkedin", "indeed", "glassdoor", "ziprecruiter", "bayt"]
 [[tool.poetry.packages]]
 include = "jobspy"
-from = "src"
 
 [tool.black]
 line-length = 88
 
 [tool.poetry.dependencies]
-python = "^3.10"
+python = "^3.10 || ^3.12"
 requests = "^2.31.0"
 beautifulsoup4 = "^4.12.2"
 pandas = "^2.1.0"
@@ -29,7 +28,6 @@ markdownify = "^0.13.1"
 regex = "^2024.4.28"
 
 [tool.poetry.group.dev.dependencies]
-pytest = "^7.4.1"
 jupyter = "^1.0.0"
 black = "*"
 pre-commit = "*"

requirements.txt

@@ -0,0 +1,118 @@
+annotated-types==0.7.0
+anyio==4.6.2.post1
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asttokens==2.4.1
+async-lru==2.0.4
+attrs==24.2.0
+babel==2.16.0
+beautifulsoup4==4.12.3
+black==24.10.0
+bleach==6.1.0
+certifi==2024.8.30
+cffi==1.17.1
+cfgv==3.4.0
+charset-normalizer==3.4.0
+click==8.1.7
+comm==0.2.2
+debugpy==1.8.7
+decorator==5.1.1
+defusedxml==0.7.1
+distlib==0.3.9
+executing==2.1.0
+fastjsonschema==2.20.0
+filelock==3.16.1
+fqdn==1.5.1
+h11==0.14.0
+httpcore==1.0.6
+httpx==0.27.2
+identify==2.6.1
+idna==3.10
+ipykernel==6.29.5
+ipython==8.28.0
+ipywidgets==8.1.5
+isoduration==20.11.0
+jedi==0.19.1
+Jinja2==3.1.4
+json5==0.9.25
+jsonpointer==3.0.0
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+jupyter==1.1.1
+jupyter-console==6.6.3
+jupyter-events==0.10.0
+jupyter-lsp==2.2.5
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+jupyter_server==2.14.2
+jupyter_server_terminals==0.5.3
+jupyterlab==4.2.5
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.27.3
+jupyterlab_widgets==3.0.13
+markdownify==0.13.1
+MarkupSafe==3.0.2
+matplotlib-inline==0.1.7
+mistune==3.0.2
+mypy-extensions==1.0.0
+nbclient==0.10.0
+nbconvert==7.16.4
+nbformat==5.10.4
+nest-asyncio==1.6.0
+nodeenv==1.9.1
+notebook==7.2.2
+notebook_shim==0.2.4
+numpy==1.26.3
+overrides==7.7.0
+packaging==24.1
+pandas==2.2.3
+pandocfilters==1.5.1
+parso==0.8.4
+pathspec==0.12.1
+pexpect==4.9.0
+platformdirs==4.3.6
+pre_commit==4.0.1
+prometheus_client==0.21.0
+prompt_toolkit==3.0.48
+psutil==6.1.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pycparser==2.22
+pydantic==2.9.2
+pydantic_core==2.23.4
+Pygments==2.18.0
+python-dateutil==2.9.0.post0
+-e git+https://github.com/fakebranden/JobSpy@60819a8fcabbd3eaba7741b673023612dc3d3692#egg=python_jobspy
+python-json-logger==2.0.7
+pytz==2024.2
+PyYAML==6.0.2
+pyzmq==26.2.0
+referencing==0.35.1
+regex==2024.9.11
+requests==2.32.3
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rpds-py==0.20.0
+Send2Trash==1.8.3
+setuptools==75.2.0
+six==1.16.0
+sniffio==1.3.1
+soupsieve==2.6
+stack-data==0.6.3
+terminado==0.18.1
+tinycss2==1.3.0
+tls-client==1.0.1
+tornado==6.4.1
+traitlets==5.14.3
+types-python-dateutil==2.9.0.20241003
+typing_extensions==4.12.2
+tzdata==2024.2
+uri-template==1.3.0
+urllib3==2.2.3
+virtualenv==20.27.0
+wcwidth==0.2.13
+webcolors==24.8.0
+webencodings==0.5.1
+websocket-client==1.8.0
+widgetsnbextension==4.0.13

src/jobspy/scrapers/__init__.py

@@ -1,58 +0,0 @@
-from __future__ import annotations
-
-from abc import ABC, abstractmethod
-
-from ..jobs import (
-    Enum,
-    BaseModel,
-    JobType,
-    JobResponse,
-    Country,
-    DescriptionFormat,
-)
-
-
-class Site(Enum):
-    LINKEDIN = "linkedin"
-    INDEED = "indeed"
-    ZIP_RECRUITER = "zip_recruiter"
-    GLASSDOOR = "glassdoor"
-    GOOGLE = "google"
-    BAYT = "bayt"
-
-
-class SalarySource(Enum):
-    DIRECT_DATA = "direct_data"
-    DESCRIPTION = "description"
-
-
-class ScraperInput(BaseModel):
-    site_type: list[Site]
-    search_term: str | None = None
-    google_search_term: str | None = None
-
-    location: str | None = None
-    country: Country | None = Country.USA
-    distance: int | None = None
-    is_remote: bool = False
-    job_type: JobType | None = None
-    easy_apply: bool | None = None
-    offset: int = 0
-    linkedin_fetch_description: bool = False
-    linkedin_company_ids: list[int] | None = None
-    description_format: DescriptionFormat | None = DescriptionFormat.MARKDOWN
-
-    results_wanted: int = 15
-    hours_old: int | None = None
-
-
-class Scraper(ABC):
-    def __init__(
-        self, site: Site, proxies: list[str] | None = None, ca_cert: str | None = None
-    ):
-        self.site = site
-        self.proxies = proxies
-        self.ca_cert = ca_cert
-
-    @abstractmethod
-    def scrape(self, scraper_input: ScraperInput) -> JobResponse: ...

src/jobspy/scrapers/ziprecruiter/constants.py

@@ -1,10 +0,0 @@
-headers = {
-    "Host": "api.ziprecruiter.com",
-    "accept": "*/*",
-    "x-zr-zva-override": "100000000;vid:ZT1huzm_EQlDTVEc",
-    "x-pushnotificationid": "0ff4983d38d7fc5b3370297f2bcffcf4b3321c418f5c22dd152a0264707602a0",
-    "x-deviceid": "D77B3A92-E589-46A4-8A39-6EF6F1D86006",
-    "user-agent": "Job Search/87.0 (iPhone; CPU iOS 16_6_1 like Mac OS X)",
-    "authorization": "Basic YTBlZjMyZDYtN2I0Yy00MWVkLWEyODMtYTI1NDAzMzI0YTcyOg==",
-    "accept-language": "en-US,en;q=0.9",
-}