config.py

import os
from dotenv import load_dotenv

load_dotenv()

NVIDIA_BASE_URL = "https://integrate.api.nvidia.com/v1"

# ── Candidate contact ────────────────────────────────────────────────────────
# Resume-header address line. ATS checkers (Jobalytics / Resume Worded) flag a
# missing address; a "City, State, Country" line satisfies that check. Used as
# the fallback when the uploaded resume has no parseable location.
CONTACT_LOCATION = os.getenv(
    "CONTACT_LOCATION", "Hyderabad, Telangana, India · Open to relocate"
)

# Primary key — GLM 5.1 (most reliable, used for resume parsing + customization)
NVIDIA_API_KEY = os.getenv("NVIDIA_API_KEY")
GLM_BASE_URL   = NVIDIA_BASE_URL
GLM_MODEL      = "z-ai/glm-5.1"

# ── 10 assessment models ────────────────────────────────────────────────────
# phase1 = used for quick keyword scoring (instant, no LLM needed now)
# phase2 = used for detailed LLM assessment of top jobs
ASSESSMENT_MODELS = [
    # ─────────────────────────────────────────────────────────────────────
    # Phase 2 (detailed LLM assessment) speed results from test run:
    #   Kimi-K2.6        ~5s/batch   FAST ✅  phase2=True
    #   Step-3.7-Flash   ~8-35s      FAST ✅  phase2=True
    #   Qwen3.5-397b     ~9s         FAST ✅  phase2=True
    #   Qwen3.5-122b-v2  ~12s        FAST ✅  phase2=True
    #   GPT-OSS-120b     ~11s        FAST ✅  phase2=True
    #   Qwen3.5-122b     ~40s        OK   ✅  phase2=True
    #   DeepSeek-v4-Pro  ~42s        OK   ✅  phase2=True
    #   DeepSeek-v4-Flash ~229s      SLOW ⚠️  phase2=False (too slow)
    #   GLM-5.1          ~234s       SLOW ⚠️  phase2=False (too slow)
    #   MiniMax-M2.7     ~908s       BLOCKED❌ phase2=False
    # ─────────────────────────────────────────────────────────────────────
    {
        "name":       "Kimi-K2.6",
        "model":      "moonshotai/kimi-k2.6",
        "api_key":    os.getenv("NVIDIA_API_KEY_3"),
        "base_url":   NVIDIA_BASE_URL,
        "extra_body": {},
        "phase1":     True,
        "phase2":     True,   # ~5s per batch FAST
        "tailor":     True,   # capable tier — strong at structured JSON rewrites
    },
    {
        "name":       "Step-3.7-Flash",
        "model":      "stepfun-ai/step-3.7-flash",
        "api_key":    os.getenv("NVIDIA_API_KEY_8"),
        "base_url":   NVIDIA_BASE_URL,
        "extra_body": {},
        "phase1":     True,
        "phase2":     True,   # ~8-35s FAST
        "tailor":     False,  # weaker — drops roles / sparse bullets; not for tailoring
    },
    {
        "name":       "Qwen3.5-397b",
        "model":      "qwen/qwen3.5-397b-a17b",
        "api_key":    os.getenv("NVIDIA_API_KEY_7"),
        "base_url":   NVIDIA_BASE_URL,
        "extra_body": {},
        "phase1":     True,
        "phase2":     True,   # ~9s FAST
        "tailor":     True,   # capable tier — large model, good instruction following
    },
    {
        "name":       "Qwen3.5-122b-v2",
        "model":      "qwen/qwen3.5-122b-a10b",
        "api_key":    os.getenv("NVIDIA_API_KEY_7"),
        "base_url":   NVIDIA_BASE_URL,
        "extra_body": {},
        "phase1":     True,
        "phase2":     True,   # ~12s FAST
        "tailor":     False,  # smaller — keep for assessment, not tailoring
    },
    {
        "name":       "GPT-OSS-120b",
        "model":      "openai/gpt-oss-120b",
        "api_key":    os.getenv("NVIDIA_API_KEY_5"),
        "base_url":   NVIDIA_BASE_URL,
        "extra_body": {},
        "phase1":     True,
        "phase2":     True,   # ~11s FAST
        "tailor":     True,   # capable tier
    },
    {
        "name":       "Qwen3.5-122b",
        "model":      "qwen/qwen3.5-122b-a10b",
        "api_key":    os.getenv("NVIDIA_API_KEY_4"),
        "base_url":   NVIDIA_BASE_URL,
        "extra_body": {},
        "phase1":     True,
        "phase2":     True,   # ~40s OK
        "tailor":     False,  # smaller
    },
    {
        "name":       "DeepSeek-v4-Pro",
        "model":      "deepseek-ai/deepseek-v4-pro",
        "api_key":    os.getenv("NVIDIA_API_KEY_2"),
        "base_url":   NVIDIA_BASE_URL,
        "extra_body": {"chat_template_kwargs": {"thinking": False}},
        "phase1":     True,
        "phase2":     True,   # ~42s OK
        "tailor":     True,   # capable tier — strong reasoning
    },
    {
        "name":       "DeepSeek-v4-Flash",
        "model":      "deepseek-ai/deepseek-v4-flash",
        "api_key":    os.getenv("NVIDIA_API_KEY_6"),
        "base_url":   NVIDIA_BASE_URL,
        "extra_body": {"chat_template_kwargs": {"thinking": True, "reasoning_effort": "high"}},
        "phase1":     True,
        "phase2":     False,  # ~229s too slow for phase2
    },
    {
        "name":       "GLM-5.1",
        "model":      "z-ai/glm-5.1",
        "api_key":    os.getenv("NVIDIA_API_KEY"),
        "base_url":   NVIDIA_BASE_URL,
        "extra_body": {},
        "phase1":     True,
        "phase2":     False,  # ~234s too slow for phase2; used for resume customization
    },
    {
        "name":       "MiniMax-M2.7",
        "model":      "minimaxai/minimax-m2.7",
        "api_key":    os.getenv("NVIDIA_API_KEY_2"),
        "base_url":   NVIDIA_BASE_URL,
        "extra_body": {},
        "phase1":     True,
        "phase2":     False,  # ~908s BLOCKED
    },
]

# ── Google Sheets / Drive ──────────────────────────────────────────────────
GOOGLE = {
    "sheet_id":         os.getenv("GOOGLE_SHEET_ID", "1Ehxt3eortehbtySdtgcSrMhCqmxIMUAmvRqSkII0HJk"),
    "sheet_tab":        "Job Applications",
    "drive_folder":     "Job Automation Agent — Resumes",
    "credentials_file": "google_credentials.json",   # Service account JSON
    "token_file":       "google_token.json",          # OAuth token (auto-created)
}

# ── Job Search — Product Manager roles ONLY ───────────────────────────────
JOB_SEARCH = {
    "roles": [
        "Product Manager",
        "Senior Product Manager",
        "AI Product Manager",
        "Technical Product Manager",
        "Product Owner",
    ],
    # Required words in job title — scraper DROPS any job without these
    "required_title_words": ["product"],
    # Blocked words in title — scraper DROPS any job with these
    "blocked_title_words": [
        "teacher", "faculty", "engineer", "developer", "designer",
        "accountant", "analyst", "consultant", "manager data",
        "project manager", "program manager", "marketing manager",
        "sales", "hr ", "recruiter", "finance", "legal",
        "civil", "mechanical", "electrical", "hardware", "network",
        "software engineer", "data engineer", "ml engineer",
    ],
    "locations": ["India", "Bangalore", "Mumbai", "Delhi NCR", "Hyderabad", "Pune", "Remote"],
    "experience_min": 2,
    "experience_max": 15,
    "max_jobs_per_platform": 30,
    "days_posted": 7,
    "primary_role": "Product Manager",
}

# Platforms (Naukri blocked by Akamai)
PLATFORMS = {
    "linkedin":  True,
    "naukri":    False,
    "indeed":    True,
    "glassdoor": True,
}

# Assessment — process ALL PM jobs
ASSESSMENT = {
    "min_score_for_llm_resume": 6,    # LLM-tailored resume for score >= this
    "generate_all_resumes":     True,  # Template resume for ALL PM-relevant jobs
    "max_llm_resumes":          30,    # Max LLM-tailored resumes per run
    "process_all_jobs":         True,  # Assess ALL scraped jobs (not just top N)
    "dedup_days":               30,    # Skip jobs seen in last N days (0 = no dedup)
    "test_mode":                False, # Set True for quick test run
    "test_jobs_limit":          10,    # Max jobs in test mode
}

# ── Production LLM generation (provider abstraction + fallback chain) ──────────
# The pipeline is model-independent: any provider may generate content, but
# deterministic validation (schema + independent score + parse) decides if it
# passes. Providers are tried in order until one yields a READY (>=90) resume.
LLM_GENERATION = {
    "provider_order": ["kimi", "nvidia_primary", "deterministic"],
    "temperature": 0.1,
    "max_retries": 2,
    "require_json_schema": True,
    "fallback_on_schema_error": True,
    "fallback_on_low_independent_score": True,
    "fallback_on_parse_failure": True,
}

# ── Resume automation policy (AUTO_AGGRESSIVE) ─────────────────────────────────
# Controls how aggressively plausible JD terms are auto-included and which
# resumes are downloadable, so a 100-job batch runs mostly hands-off.
AUTOMATION = {
    "automation_mode": "auto_aggressive",
    # What to do per risk severity (see candidate_fit.severity):
    "review_policy": {
        "LOW_RISK_AUTO_INCLUDED":          "auto_include",
        "MEDIUM_RISK_REVIEW_RECOMMENDED":  "auto_include_with_review_flag",
        "HIGH_RISK_NEEDS_CONFIRMATION":    "ask_user",
        "BLOCKED_DO_NOT_INCLUDE":          "exclude",
    },
    # Which statuses are downloadable:
    "download_policy": {
        "READY_90_PLUS":                    "allow",
        "READY_90_PLUS_REVIEW_RECOMMENDED": "allow_with_warning",
        "WEAK_90_INTERNAL_ONLY":            "block",
        "NEEDS_REPAIR":                     "block",
        "NEEDS_USER_INPUT":                 "block",
        "NOT_ELIGIBLE_LOW_FIT":             "block",
        "PARSE_FAILED":                     "block",
    },
}

# ── ever-jobs integration ─────────────────────────────────────────────────────
from src.ever_jobs_bridge.platforms import INDIA_DEFAULT_PLATFORMS

EVER_JOBS = {
    # Point this at a HOSTED ever-jobs sidecar to enable the 150+ extra
    # platforms on HF Spaces (where a local Node process can't run). Defaults
    # to the local dev sidecar.
    "api_url":            os.getenv("EVER_JOBS_API_URL", "http://localhost:3001"),
    "default_platforms":  INDIA_DEFAULT_PLATFORMS,
    "max_results":        25,
    "request_timeout":    30,
    "http_timeout":       120,
    "max_platforms_per_call": 20,
}

# Output
OUTPUT = {
    "excel_path":  "data/output/reports/job_report.xlsx",
    "resumes_dir": "data/output/resumes/",
    "report_dir":  "data/output/reports/",
}

# Resume
RESUME = {
    "pdf_path": os.getenv("RESUME_PATH", "data/resume/resume.pdf"),
}