chore:version

.github/workflows/job_scraper_dynamic.yml

@@ -1,49 +0,0 @@
-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

@@ -1,48 +0,0 @@
-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** concurrently
+- Scrapes job postings from **LinkedIn**, **Indeed**, **Glassdoor**, **Google**, **ZipRecruiter**, **Bayt** & **Naukri** 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"],
+    site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor", "google", "bayt", "naukri"],
     search_term="software engineer",
     google_search_term="software engineer jobs near San Francisco, CA since yesterday",
     location="San Francisco, CA",
@@ -51,6 +51,7 @@ 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()`
@@ -220,6 +221,7 @@ JobPost
 │   ├── country
 │   ├── city
 │   ├── state
+├── is_remote
 ├── description
 ├── job_type: fulltime, parttime, internship, contract
 ├── job_function
@@ -229,8 +231,7 @@ JobPost
 │   ├── currency
 │   └── salary_source: direct_data, description (parsed from posting)
 ├── date_posted
-├── emails
-└── is_remote
+└── emails
 
 Linkedin specific
 └── job_level
@@ -245,4 +246,12 @@ 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

@@ -1,8 +0,0 @@
-{
-    "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

@@ -1 +0,0 @@
-{"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

@@ -1,116 +0,0 @@
-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

@@ -1,94 +0,0 @@
-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

@@ -1,146 +0,0 @@
-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

@@ -10,6 +10,7 @@ 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 (
@@ -57,6 +58,7 @@ 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
@@ -139,6 +141,7 @@ 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"] = (
@@ -157,7 +160,6 @@ def scrape_jobs(
                     and job_data["max_amount"]
                 ):
                     convert_to_annual(job_data)
-
             else:
                 if country_enum == Country.USA:
                     (
@@ -176,6 +178,17 @@ 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)
 
@@ -199,4 +212,4 @@ def scrape_jobs(
             by=["site", "date_posted"], ascending=[True, False]
         ).reset_index(drop=True)
     else:
-        return pd.DataFrame()
+        return pd.DataFrame()

jobspy/exception.py

@@ -34,3 +34,7 @@ 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

@@ -205,8 +205,6 @@ 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 sssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrompensation:
+    :param compensation:
     :return: compensation object
     """
     if not compensation["baseSalary"] and not compensation["estimated"]:
@@ -58,11 +58,14 @@ 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_location
+    return is_remote_in_attributes or is_remote_in_description or is_remote_in_location
 
 
 def get_compensation_interval(interval: str) -> CompensationInterval:

jobspy/linkedin/__init__.py

@@ -14,10 +14,11 @@ 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,
@@ -173,7 +174,7 @@ class LinkedIn(Scraper):
     ) -> Optional[JobPost]:
         salary_tag = job_card.find("span", class_="job-search-card__salary-info")
 
-        compensation = None
+        compensation = description = None
         if salary_tag:
             salary_text = salary_tag.get_text(separator=" ").strip()
             salary_values = [currency_parser(value) for value in salary_text.split("-")]
@@ -217,8 +218,8 @@ class LinkedIn(Scraper):
         job_details = {}
         if full_descr:
             job_details = self._get_job_details(job_id)
-            description = description.replace(",", "")
-
+            description = job_details.get("description")
+        is_remote = is_job_remote(title, description, location)
 
         return JobPost(
             id=f"li-{job_id}",
@@ -226,6 +227,7 @@ 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,
@@ -234,7 +236,7 @@ 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(job_details.get("description")),
+            emails=extract_emails_from_text(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
+from jobspy.model import JobType, Location
 from jobspy.util import get_enum_from_job_type
 
 
@@ -83,3 +83,14 @@ 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
@@ -268,9 +268,16 @@ 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] = []
@@ -283,6 +290,7 @@ class Site(Enum):
     GLASSDOOR = "glassdoor"
     GOOGLE = "google"
     BAYT = "bayt"
+    NAUKRI = "naukri"
 
 
 class SalarySource(Enum):

jobspy/naukri/__init__.py

@@ -0,0 +1,301 @@
+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

@@ -0,0 +1,11 @@
+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

@@ -0,0 +1,34 @@
+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

@@ -344,4 +344,11 @@ 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 = "1.1.78"
+version = "1.1.79"
 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"]
+keywords = [ "jobs-scraper", "linkedin", "indeed", "glassdoor", "ziprecruiter", "bayt", "naukri"]
 [[tool.poetry.packages]]
 include = "jobspy"
 
@@ -17,7 +17,7 @@ include = "jobspy"
 line-length = 88
 
 [tool.poetry.dependencies]
-python = "^3.10 || ^3.12"
+python = "^3.10"
 requests = "^2.31.0"
 beautifulsoup4 = "^4.12.2"
 pandas = "^2.1.0"

requirements.txt

@@ -1,118 +0,0 @@
-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