🔄 Updated config for Branden@autoemployme.onmicrosoft.com

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

README.md

@@ -4,7 +4,7 @@
 
 ## Features
 
-- Scrapes job postings from **LinkedIn**, **Indeed**, **Glassdoor**, **Google**, **ZipRecruiter**, **Bayt** & **Naukri** concurrently
+- Scrapes job postings from **LinkedIn**, **Indeed**, **Glassdoor**, **Google**, **ZipRecruiter**, & **Bayt** concurrently
 - Aggregates the job postings in a dataframe
 - Proxies support to bypass blocking
 
@@ -25,7 +25,7 @@ import csv
 from jobspy import scrape_jobs
 
 jobs = scrape_jobs(
-    site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor", "google", "bayt", "naukri"],
+    site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor", "google", "bayt"],
     search_term="software engineer",
     google_search_term="software engineer jobs near San Francisco, CA since yesterday",
     location="San Francisco, CA",
@@ -51,7 +51,6 @@ linkedin       Software Engineer - Early Career  Lockheed Martin   Sunnyvale
 linkedin       Full-Stack Software Engineer      Rain              New York      NY     fulltime  yearly    None        None        https://www.linkedin.com/jobs/view/3696158877      Rain’s mission is to create the fastest and ea...
 zip_recruiter Software Engineer - New Grad       ZipRecruiter      Santa Monica  CA     fulltime  yearly    130000      150000      https://www.ziprecruiter.com/jobs/ziprecruiter...  We offer a hybrid work environment. Most US-ba...
 zip_recruiter Software Developer                 TEKsystems        Phoenix       AZ     fulltime  hourly    65          75          https://www.ziprecruiter.com/jobs/teksystems-0...  Top Skills' Details• 6 years of Java developme...
-
 ```
 
 ### Parameters for `scrape_jobs()`
@@ -221,7 +220,6 @@ JobPost
 │   ├── country
 │   ├── city
 │   ├── state
-├── is_remote
 ├── description
 ├── job_type: fulltime, parttime, internship, contract
 ├── job_function
@@ -231,7 +229,8 @@ JobPost
 │   ├── currency
 │   └── salary_source: direct_data, description (parsed from posting)
 ├── date_posted
-└── emails
+├── emails
+└── is_remote
 
 Linkedin specific
 └── job_level
@@ -246,12 +245,4 @@ Indeed specific
 ├── company_revenue_label
 ├── company_description
 └── company_logo
-
-Naukri specific
-├── skills
-├── experience_range
-├── company_rating
-├── company_reviews_count
-├── vacancy_count
-└── work_from_home_type
 ```

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 @@
+{"search_terms":["False tester"],"results_wanted":"100\n","max_days_old":"1\n","target_state":"NY","user_email":"Branden@autoemployme.onmicrosoft.com"}

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

@@ -10,7 +10,6 @@ from jobspy.glassdoor import Glassdoor
 from jobspy.google import Google
 from jobspy.indeed import Indeed
 from jobspy.linkedin import LinkedIn
-from jobspy.naukri import Naukri
 from jobspy.model import JobType, Location, JobResponse, Country
 from jobspy.model import SalarySource, ScraperInput, Site
 from jobspy.util import (
@@ -58,7 +57,6 @@ def scrape_jobs(
         Site.GLASSDOOR: Glassdoor,
         Site.GOOGLE: Google,
         Site.BAYT: BaytScraper,
-        Site.NAUKRI: Naukri,
     }
     set_logger_level(verbose)
     job_type = get_enum_from_value(job_type) if job_type else None
@@ -141,7 +139,6 @@ def scrape_jobs(
                     **job_data["location"]
                 ).display_location()
 
-            # Handle compensation
             compensation_obj = job_data.get("compensation")
             if compensation_obj and isinstance(compensation_obj, dict):
                 job_data["interval"] = (
@@ -160,6 +157,7 @@ def scrape_jobs(
                     and job_data["max_amount"]
                 ):
                     convert_to_annual(job_data)
+
             else:
                 if country_enum == Country.USA:
                     (
@@ -178,17 +176,6 @@ def scrape_jobs(
                 if "min_amount" in job_data and job_data["min_amount"]
                 else None
             )
-
-            #naukri-specific fields
-            job_data["skills"] = (
-                ", ".join(job_data["skills"]) if job_data["skills"] else None
-            )
-            job_data["experience_range"] = job_data.get("experience_range")
-            job_data["company_rating"] = job_data.get("company_rating")
-            job_data["company_reviews_count"] = job_data.get("company_reviews_count")
-            job_data["vacancy_count"] = job_data.get("vacancy_count")
-            job_data["work_from_home_type"] = job_data.get("work_from_home_type")
-
             job_df = pd.DataFrame([job_data])
             jobs_dfs.append(job_df)
 
@@ -212,4 +199,4 @@ def scrape_jobs(
             by=["site", "date_posted"], ascending=[True, False]
         ).reset_index(drop=True)
     else:
-        return pd.DataFrame()
+        return pd.DataFrame()

jobspy/exception.py

@@ -34,7 +34,3 @@ class GoogleJobsException(Exception):
 class BaytException(Exception):
     def __init__(self, message=None):
         super().__init__(message or "An error occurred with Bayt")
-
-class NaukriException(Exception):
-    def __init__(self,message=None):
-        super().__init__(message or "An error occurred with Naukri")

jobspy/indeed/__init__.py

@@ -205,6 +205,8 @@ class Indeed(Scraper):
         description = job["description"]["html"]
         if self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
             description = markdown_converter(description)
+            description = description.replace(",", "")
+
 
         job_type = get_job_type(job["attributes"])
         timestamp_seconds = job["datePublished"] / 1000

jobspy/indeed/util.py

@@ -20,7 +20,7 @@ def get_job_type(attributes: list) -> list[JobType]:
 def get_compensation(compensation: dict) -> Compensation | None:
     """
     Parses the job to get compensation
-    :param compensation:
+    :param sssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrompensation:
     :return: compensation object
     """
     if not compensation["baseSalary"] and not compensation["estimated"]:
@@ -58,14 +58,11 @@ def is_job_remote(job: dict, description: str) -> bool:
         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
+    return is_remote_in_attributes or is_remote_in_location
 
 
 def get_compensation_interval(interval: str) -> CompensationInterval:

jobspy/linkedin/__init__.py

@@ -14,11 +14,10 @@ from bs4.element import Tag
 from jobspy.exception import LinkedInException
 from jobspy.linkedin.constant import headers
 from jobspy.linkedin.util import (
-    is_job_remote,
     job_type_code,
     parse_job_type,
     parse_job_level,
-    parse_company_industry
+    parse_company_industry,
 )
 from jobspy.model import (
     JobPost,
@@ -174,7 +173,7 @@ class LinkedIn(Scraper):
     ) -> Optional[JobPost]:
         salary_tag = job_card.find("span", class_="job-search-card__salary-info")
 
-        compensation = description = None
+        compensation = None
         if salary_tag:
             salary_text = salary_tag.get_text(separator=" ").strip()
             salary_values = [currency_parser(value) for value in salary_text.split("-")]
@@ -218,8 +217,8 @@ class LinkedIn(Scraper):
         job_details = {}
         if full_descr:
             job_details = self._get_job_details(job_id)
-            description = job_details.get("description")
-        is_remote = is_job_remote(title, description, location)
+            description = description.replace(",", "")
+
 
         return JobPost(
             id=f"li-{job_id}",
@@ -227,7 +226,6 @@ class LinkedIn(Scraper):
             company_name=company,
             company_url=company_url,
             location=location,
-            is_remote=is_remote,
             date_posted=date_posted,
             job_url=f"{self.base_url}/jobs/view/{job_id}",
             compensation=compensation,
@@ -236,7 +234,7 @@ class LinkedIn(Scraper):
             company_industry=job_details.get("company_industry"),
             description=job_details.get("description"),
             job_url_direct=job_details.get("job_url_direct"),
-            emails=extract_emails_from_text(description),
+            emails=extract_emails_from_text(job_details.get("description")),
             company_logo=job_details.get("company_logo"),
             job_function=job_details.get("job_function"),
         )

jobspy/linkedin/util.py

@@ -1,6 +1,6 @@
 from bs4 import BeautifulSoup
 
-from jobspy.model import JobType, Location
+from jobspy.model import JobType
 from jobspy.util import get_enum_from_job_type
 
 
@@ -83,14 +83,3 @@ def parse_company_industry(soup_industry: BeautifulSoup) -> str | None:
             industry = industry_span.get_text(strip=True)
 
     return industry
-
-
-def is_job_remote(title: dict, description: str, location: Location) -> bool:
-    """
-    Searches the title, location, and description to check if job is remote
-    """
-    remote_keywords = ["remote", "work from home", "wfh"]
-    location = location.display_location()
-    full_string = f'{title} {description} {location}'.lower()
-    is_remote = any(keyword in full_string for keyword in remote_keywords)
-    return is_remote

jobspy/model.py

@@ -254,13 +254,13 @@ class JobPost(BaseModel):
     is_remote: bool | None = None
     listing_type: str | None = None
 
-    # LinkedIn specific
+    # linkedin specific
     job_level: str | None = None
 
-    # LinkedIn and Indeed specific
+    # linkedin and indeed specific
     company_industry: str | None = None
 
-    # Indeed specific
+    # indeed specific
     company_addresses: str | None = None
     company_num_employees: str | None = None
     company_revenue: str | None = None
@@ -268,16 +268,9 @@ class JobPost(BaseModel):
     company_logo: str | None = None
     banner_photo_url: str | None = None
 
-    # LinkedIn only atm
+    # linkedin only atm
     job_function: str | None = None
 
-    # Naukri specific
-    skills: list[str] | None = None  #from tagsAndSkills
-    experience_range: str | None = None  #from experienceText
-    company_rating: float | None = None  #from ambitionBoxData.AggregateRating
-    company_reviews_count: int | None = None  #from ambitionBoxData.ReviewsCount
-    vacancy_count: int | None = None  #from vacancy
-    work_from_home_type: str | None = None  #from clusters.wfhType (e.g., "Hybrid", "Remote")
 
 class JobResponse(BaseModel):
     jobs: list[JobPost] = []
@@ -290,7 +283,6 @@ class Site(Enum):
     GLASSDOOR = "glassdoor"
     GOOGLE = "google"
     BAYT = "bayt"
-    NAUKRI = "naukri"
 
 
 class SalarySource(Enum):

jobspy/naukri/__init__.py

@@ -1,301 +0,0 @@
-from __future__ import annotations
-
-import math
-import random
-import time
-from datetime import datetime, date
-from typing import Optional
-
-import regex as re
-import requests
-
-from jobspy.exception import NaukriException
-from jobspy.naukri.constant import headers as naukri_headers
-from jobspy.naukri.util import (
-    is_job_remote,
-    parse_job_type,
-    parse_company_industry,
-)
-from jobspy.model import (
-    JobPost,
-    Location,
-    JobResponse,
-    Country,
-    Compensation,
-    DescriptionFormat,
-    Scraper,
-    ScraperInput,
-    Site,
-)
-from jobspy.util import (
-    extract_emails_from_text,
-    currency_parser,
-    markdown_converter,
-    create_session,
-    create_logger,
-)
-
-log = create_logger("Naukri")
-
-class Naukri(Scraper):
-    base_url = "https://www.naukri.com/jobapi/v3/search"
-    delay = 3
-    band_delay = 4
-    jobs_per_page = 20  
-
-    def __init__(
-        self, proxies: list[str] | str | None = None, ca_cert: str | None = None
-    ):
-        """
-        Initializes NaukriScraper with the Naukri API URL
-        """
-        super().__init__(Site.NAUKRI, proxies=proxies, ca_cert=ca_cert)
-        self.session = create_session(
-            proxies=self.proxies,
-            ca_cert=ca_cert,
-            is_tls=False,
-            has_retry=True,
-            delay=5,
-            clear_cookies=True,
-        )
-        self.session.headers.update(naukri_headers)
-        self.scraper_input = None
-        self.country = "India"  #naukri is india-focused by default
-        log.info("Naukri scraper initialized")
-
-    def scrape(self, scraper_input: ScraperInput) -> JobResponse:
-        """
-        Scrapes Naukri API for jobs with scraper_input criteria
-        :param scraper_input:
-        :return: job_response
-        """
-        self.scraper_input = scraper_input
-        job_list: list[JobPost] = []
-        seen_ids = set()
-        start = scraper_input.offset or 0
-        page = (start // self.jobs_per_page) + 1
-        request_count = 0
-        seconds_old = (
-            scraper_input.hours_old * 3600 if scraper_input.hours_old else None
-        )
-        continue_search = (
-            lambda: len(job_list) < scraper_input.results_wanted and page <= 50  # Arbitrary limit
-        )
-
-        while continue_search():
-            request_count += 1
-            log.info(
-                f"Scraping page {request_count} / {math.ceil(scraper_input.results_wanted / self.jobs_per_page)} "
-                f"for search term: {scraper_input.search_term}"
-            )
-            params = {
-                "noOfResults": self.jobs_per_page,
-                "urlType": "search_by_keyword",
-                "searchType": "adv",
-                "keyword": scraper_input.search_term,
-                "pageNo": page,
-                "k": scraper_input.search_term,
-                "seoKey": f"{scraper_input.search_term.lower().replace(' ', '-')}-jobs",
-                "src": "jobsearchDesk",
-                "latLong": "",
-                "location": scraper_input.location,
-                "remote": "true" if scraper_input.is_remote else None,
-            }
-            if seconds_old:
-                params["days"] = seconds_old // 86400  # Convert to days
-
-            params = {k: v for k, v in params.items() if v is not None}
-            try:
-                log.debug(f"Sending request to {self.base_url} with params: {params}")
-                response = self.session.get(self.base_url, params=params, timeout=10)
-                if response.status_code not in range(200, 400):
-                    err = f"Naukri API response status code {response.status_code} - {response.text}"
-                    log.error(err)
-                    return JobResponse(jobs=job_list)
-                data = response.json()
-                job_details = data.get("jobDetails", [])
-                log.info(f"Received {len(job_details)} job entries from API")
-                if not job_details:
-                    log.warning("No job details found in API response")
-                    break
-            except Exception as e:
-                log.error(f"Naukri API request failed: {str(e)}")
-                return JobResponse(jobs=job_list)
-
-            for job in job_details:
-                job_id = job.get("jobId")
-                if not job_id or job_id in seen_ids:
-                    continue
-                seen_ids.add(job_id)
-                log.debug(f"Processing job ID: {job_id}")
-
-                try:
-                    fetch_desc = scraper_input.linkedin_fetch_description
-                    job_post = self._process_job(job, job_id, fetch_desc)
-                    if job_post:
-                        job_list.append(job_post)
-                        log.info(f"Added job: {job_post.title} (ID: {job_id})")
-                    if not continue_search():
-                        break
-                except Exception as e:
-                    log.error(f"Error processing job ID {job_id}: {str(e)}")
-                    raise NaukriException(str(e))
-
-            if continue_search():
-                time.sleep(random.uniform(self.delay, self.delay + self.band_delay))
-                page += 1
-
-        job_list = job_list[:scraper_input.results_wanted]
-        log.info(f"Scraping completed. Total jobs collected: {len(job_list)}")
-        return JobResponse(jobs=job_list)
-
-    def _process_job(
-        self, job: dict, job_id: str, full_descr: bool
-    ) -> Optional[JobPost]:
-        """
-        Processes a single job from API response into a JobPost object
-        """
-        title = job.get("title", "N/A")
-        company = job.get("companyName", "N/A")
-        company_url = f"https://www.naukri.com/{job.get('staticUrl', '')}" if job.get("staticUrl") else None
-
-        location = self._get_location(job.get("placeholders", []))
-        compensation = self._get_compensation(job.get("placeholders", []))
-        date_posted = self._parse_date(job.get("footerPlaceholderLabel"), job.get("createdDate"))
-
-        job_url = f"https://www.naukri.com{job.get('jdURL', f'/job/{job_id}')}"
-        description = job.get("jobDescription") if full_descr else None
-        if description and self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
-            description = markdown_converter(description)
-
-        job_type = parse_job_type(description) if description else None
-        company_industry = parse_company_industry(description) if description else None
-        is_remote = is_job_remote(title, description or "", location)
-        company_logo = job.get("logoPathV3") or job.get("logoPath")
-
-        # Naukri-specific fields
-        skills = job.get("tagsAndSkills", "").split(",") if job.get("tagsAndSkills") else None
-        experience_range = job.get("experienceText")
-        ambition_box = job.get("ambitionBoxData", {})
-        company_rating = float(ambition_box.get("AggregateRating")) if ambition_box.get("AggregateRating") else None
-        company_reviews_count = ambition_box.get("ReviewsCount")
-        vacancy_count = job.get("vacancy")
-        work_from_home_type = self._infer_work_from_home_type(job.get("placeholders", []), title, description or "")
-
-        job_post = JobPost(
-            id=f"nk-{job_id}",
-            title=title,
-            company_name=company,
-            company_url=company_url,
-            location=location,
-            is_remote=is_remote,
-            date_posted=date_posted,
-            job_url=job_url,
-            compensation=compensation,
-            job_type=job_type,
-            company_industry=company_industry,
-            description=description,
-            emails=extract_emails_from_text(description or ""),
-            company_logo=company_logo,
-            skills=skills,
-            experience_range=experience_range,
-            company_rating=company_rating,
-            company_reviews_count=company_reviews_count,
-            vacancy_count=vacancy_count,
-            work_from_home_type=work_from_home_type,
-        )
-        log.debug(f"Processed job: {title} at {company}")
-        return job_post
-
-    def _get_location(self, placeholders: list[dict]) -> Location:
-        """
-        Extracts location data from placeholders
-        """
-        location = Location(country=Country.INDIA)
-        for placeholder in placeholders:
-            if placeholder.get("type") == "location":
-                location_str = placeholder.get("label", "")
-                parts = location_str.split(", ")
-                city = parts[0] if parts else None
-                state = parts[1] if len(parts) > 1 else None
-                location = Location(city=city, state=state, country=Country.INDIA)
-                log.debug(f"Parsed location: {location.display_location()}")
-                break
-        return location
-
-    def _get_compensation(self, placeholders: list[dict]) -> Optional[Compensation]:
-        """
-        Extracts compensation data from placeholders, handling Indian salary formats (Lakhs, Crores)
-        """
-        for placeholder in placeholders:
-            if placeholder.get("type") == "salary":
-                salary_text = placeholder.get("label", "").strip()
-                if salary_text == "Not disclosed":
-                    log.debug("Salary not disclosed")
-                    return None
-
-                # Handle Indian salary formats (e.g., "12-16 Lacs P.A.", "1-5 Cr")
-                salary_match = re.match(r"(\d+(?:\.\d+)?)\s*-\s*(\d+(?:\.\d+)?)\s*(Lacs|Lakh|Cr)\s*(P\.A\.)?", salary_text, re.IGNORECASE)
-                if salary_match:
-                    min_salary, max_salary, unit = salary_match.groups()[:3]
-                    min_salary, max_salary = float(min_salary), float(max_salary)
-                    currency = "INR"
-
-                    # Convert to base units (INR)
-                    if unit.lower() in ("lacs", "lakh"):
-                        min_salary *= 100000  # 1 Lakh = 100,000 INR
-                        max_salary *= 100000
-                    elif unit.lower() == "cr":
-                        min_salary *= 10000000  # 1 Crore = 10,000,000 INR
-                        max_salary *= 10000000
-
-                    log.debug(f"Parsed salary: {min_salary} - {max_salary} INR")
-                    return Compensation(
-                        min_amount=int(min_salary),
-                        max_amount=int(max_salary),
-                        currency=currency,
-                    )
-                else:
-                    log.debug(f"Could not parse salary: {salary_text}")
-                    return None
-        return None
-
-    def _parse_date(self, label: str, created_date: int) -> Optional[date]:
-        """
-        Parses date from footerPlaceholderLabel or createdDate, returning a date object
-        """
-        today = datetime.now()
-        if not label:
-            if created_date:
-                return datetime.fromtimestamp(created_date / 1000).date()  # Convert to date
-            return None
-        label = label.lower()
-        if "today" in label or "just now" in label or "few hours" in label:
-            log.debug("Date parsed as today")
-            return today.date()
-        elif "ago" in label:
-            match = re.search(r"(\d+)\s*day", label)
-            if match:
-                days = int(match.group(1))
-                parsed_date = today.replace(day=today.day - days).date()
-                log.debug(f"Date parsed: {days} days ago -> {parsed_date}")
-                return parsed_date
-        elif created_date:
-            parsed_date = datetime.fromtimestamp(created_date / 1000).date()
-            log.debug(f"Date parsed from timestamp: {parsed_date}")
-            return parsed_date
-        log.debug("No date parsed")
-        return None
-
-    def _infer_work_from_home_type(self, placeholders: list[dict], title: str, description: str) -> Optional[str]:
-        """
-        Infers work-from-home type from job data (e.g., 'Hybrid', 'Remote', 'Work from office')
-        """
-        location_str = next((p["label"] for p in placeholders if p["type"] == "location"), "").lower()
-        if "hybrid" in location_str or "hybrid" in title.lower() or "hybrid" in description.lower():
-            return "Hybrid"
-        elif "remote" in location_str or "remote" in title.lower() or "remote" in description.lower():
-            return "Remote"
-        elif "work from office" in description.lower() or not ("remote" in description.lower() or "hybrid" in description.lower()):
-            return "Work from office"
-        return None

jobspy/naukri/constant.py

@@ -1,11 +0,0 @@
-headers = {
-    "authority": "www.naukri.com",
-    "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
-    "accept-language": "en-US,en;q=0.9",
-    "cache-control": "max-age=0",
-    "upgrade-insecure-requests": "1",
-    "appid": "109",
-    "systemid": "Naukri",
-    "Nkparam": "Ppy0YK9uSHqPtG3bEejYc04RTpUN2CjJOrqA68tzQt0SKJHXZKzz9M8cZtKLVkoOuQmfe4cTb1r2CwfHaxW5Tg==",
-    "user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
-}

jobspy/naukri/util.py

@@ -1,34 +0,0 @@
-from __future__ import annotations
-
-from bs4 import BeautifulSoup
-from jobspy.model import JobType, Location
-from jobspy.util import get_enum_from_job_type
-
-
-def parse_job_type(soup: BeautifulSoup) -> list[JobType] | None:
-    """
-    Gets the job type from the job page
-    """
-    job_type_tag = soup.find("span", class_="job-type")
-    if job_type_tag:
-        job_type_str = job_type_tag.get_text(strip=True).lower().replace("-", "")
-        return [get_enum_from_job_type(job_type_str)] if job_type_str else None
-    return None
-
-
-def parse_company_industry(soup: BeautifulSoup) -> str | None:
-    """
-    Gets the company industry from the job page
-    """
-    industry_tag = soup.find("span", class_="industry")
-    return industry_tag.get_text(strip=True) if industry_tag else None
-
-
-def is_job_remote(title: str, description: str, location: Location) -> bool:
-    """
-    Searches the title, description, and location to check if the job is remote
-    """
-    remote_keywords = ["remote", "work from home", "wfh"]
-    location_str = location.display_location()
-    full_string = f"{title} {description} {location_str}".lower()
-    return any(keyword in full_string for keyword in remote_keywords)

jobspy/util.py

@@ -344,11 +344,4 @@ desired_order = [
     "company_num_employees",
     "company_revenue",
     "company_description",
-    #naukri-specific fields
-    "skills",
-    "experience_range",
-    "company_rating",
-    "company_reviews_count",
-    "vacancy_count",
-    "work_from_home_type",
 ]

jobspy/ziprecruiter/__init__.py

@@ -216,4 +216,4 @@ class ZipRecruiter(Scraper):
         Sends a session event to the API with device properties.
         """
         url = f"{self.api_url}/jobs-app/event"
-        self.session.post(url, data=get_cookie_data)
+        self.session.post(url, data=get_cookie_data)

jobspy/ziprecruiter/util.py

@@ -28,4 +28,4 @@ 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
+    return None

pyproject.toml

@@ -4,12 +4,12 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "python-jobspy"
-version = "1.1.79"
+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", "bayt", "naukri"]
+keywords = [ "jobs-scraper", "linkedin", "indeed", "glassdoor", "ziprecruiter", "bayt"]
 [[tool.poetry.packages]]
 include = "jobspy"
 
@@ -17,7 +17,7 @@ include = "jobspy"
 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"

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