import requests
import os

# Relative import (same folder)
from .normalizer import normalize_job_data

# --- CONFIGURATION FOR SCRAPINGDOG API ---
try:
    import streamlit as st
    SCRAPINGDOG_API_KEY = st.secrets.get("SCRAPINGDOG_API_KEY")
except (ImportError, KeyError, FileNotFoundError):
    SCRAPINGDOG_API_KEY = os.getenv("SCRAPINGDOG_API_KEY")

def fetch_linkedin_jobs_stub(query="python developer", location="remote", limit=5):
    print("LinkedIn agent is a stub. Returning mock data.")
    mock_linkedin_jobs = [
        {
            'title': 'Senior Python Engineer (Stub)',
            'company_name': 'Innovate Inc.',
            'location': 'Remote',
            'description': 'Looking for a senior python developer with experience in Django and cloud services. This is a mock job from a stub function.',
            'posted_at': '2025-09-27',
            'job_url': 'https://www.linkedin.com/jobs'
        }
    ]
    return [normalize_job_data(job, "LinkedIn (Stub)") for job in mock_linkedin_jobs]

def fetch_linkedin_jobs_real(query="python developer", location="remote", limit=10):
    if not SCRAPINGDOG_API_KEY:
        print("SCRAPINGDOG_API_KEY not set. Cannot fetch real data from LinkedIn.")
        return []

    url = "https://api.scrapingdog.com/linkedinjobs/"
    params = {
        "api_key": SCRAPINGDOG_API_KEY,
        "query": query,
        "location": location,
        "num": limit
    }

    try:
        response = requests.get(url, params=params)
        response.raise_for_status()
        jobs = response.json()

        if not isinstance(jobs, list):
            print("Unexpected API response format.")
            return []

        adapted_jobs = []
        for job in jobs:
            adapted_job = {
                'title': job.get('job_title', 'N/A'),
                'company_name': job.get('company_name', 'N/A'),
                'location': job.get('job_location', 'Remote'),
                'description': job.get('job_description', ''),
                'posted_at': job.get('posted_at', 'N/A'),
                'job_url': job.get('job_url', '')
            }
            adapted_jobs.append(adapted_job)

        return [normalize_job_data(job, "LinkedIn") for job in adapted_jobs]

    except requests.exceptions.RequestException as e:
        print(f"Error fetching jobs from ScrapingDog API: {e}")
        return []
    except ValueError as e:
        print(f"Error parsing JSON: {e}")
        return []