import os
import re
import unicodedata
import json
import pandas as pd
import requests
import time
import random
from io import StringIO
from app.models import db, Job

print("🆕 MODERN webcrawler LOADED! – BATCHED + PROXY + RESUME + ETA + 2x SCRAPER")

UPLOAD_FOLDER = '/app/uploads'
RESULT_FOLDER = '/app/results'

# 2x Scraper – abwechselnd genutzt
SCRAPER_URLS = [
    "http://gmaps-scraper-1:8080",
    "http://gmaps-scraper-2:8080",
]

OUTPUT_COLS = ['title', 'category', 'address', 'open_hours', 'website', 'phone', 'link']

PROXY_URL = "http://bitlleuv-rotate:s5hzse6hz74b@p.webshare.io:80"
API_PROXIES = {"http": PROXY_URL, "https": PROXY_URL}

# ──────────────────────────────────────────────
# Tuning
# ──────────────────────────────────────────────
BATCH_SIZE        = 30    # Keywords pro Scraper-Job
BATCH_DELAY_MIN   = 3     # Sekunden Pause zwischen Batches (min)
BATCH_DELAY_MAX   = 6     # Sekunden Pause zwischen Batches (max)
MAX_TIME          = 60    # Sekunden die der Scraper pro Batch hat
POLL_MAX          = 90    # Max. Poll-Versuche pro Batch
POLL_DELAY_MIN    = 2     # Sekunden zwischen Polls (min)
POLL_DELAY_MAX    = 5     # Sekunden zwischen Polls (max)
STUCK_THRESHOLD   = 8     # Polls auf 'pending' bis Auto-Restart
MAX_RETRIES       = 2     # Wiederholversuche pro Batch bei Fehler

# ──────────────────────────────────────────────
# Hilfsfunktionen
# ──────────────────────────────────────────────

def is_blocked(data):
    text = str(data).lower()
    blocked = any(kw in text for kw in ['captcha', 'blocked', 'rate limit', 'too many', '429'])
    if blocked:
        print(f"🚫 BLOCKED: {str(data)[:100]}")
    return blocked

def fix_encoding(text):
    if not isinstance(text, str):
        return text
    try:
        return text.encode('latin-1').decode('utf-8')
    except (UnicodeEncodeError, UnicodeDecodeError):
        return text

# Fix 1: Sonderzeichen in Queries bereinigen
def clean_query(q):
    """Steuerzeichen + fehlerhafte Bytes entfernen für saubere Google Maps URLs"""
    q = ''.join(c for c in q if unicodedata.category(c) != 'Cc')
    q = ' '.join(q.split())
    return q.strip()

def build_input_addresses(df):
    addresses = set()
    for _, row in df.iterrows():
        plz    = str(row.get('PLZ', '')).strip()
        stadt  = str(row.get('Stadt', '')).strip()
        str_   = str(row.get('Straße', '')).strip()
        nr     = str(row.get('Hausnummer', '')).strip()
        zusatz = str(row.get('Zusatz', '')).strip()
        full = f"{str_} {nr} {zusatz} {plz} {stadt}".lower().strip()
        full = ' '.join(full.split())
        addresses.add(full)
    return addresses

def normalize_address(addr):
    if not isinstance(addr, str):
        return ''
    addr = fix_encoding(addr)
    return ' '.join(addr.lower().strip().split())

def address_in_input(result_addr, input_addresses):
    norm = normalize_address(result_addr)
    for inp_addr in input_addresses:
        plz_match = re.search(r'\b\d{5}\b', inp_addr)
        if plz_match:
            plz = plz_match.group()
            if plz in norm:
                street = inp_addr.split()[0] if inp_addr else ''
                if len(street) > 3 and street[:4].lower() in norm:
                    return True
    return False

def format_eta(seconds):
    """Sekunden → lesbares ETA-Format"""
    if seconds < 60:
        return f"{int(seconds)}s"
    h, rem = divmod(int(seconds), 3600)
    m = rem // 60
    return f"{h}h {m:02d}min" if h > 0 else f"{m}min"

# ──────────────────────────────────────────────
# Fix 3: Scraper-Neustart bei Inactivity
# ──────────────────────────────────────────────

def restart_scraper(scraper_url):
    """Den betroffenen Scraper-Container neu starten"""
    try:
        import subprocess
        # Container-Name aus URL ableiten: http://gmaps-scraper-1:8080 → gmaps-scraper-1
        container = scraper_url.split("//")[1].split(":")[0]
        print(f"🔄 Starte {container} neu...")
        subprocess.run(["docker", "restart", container], timeout=30, capture_output=True)
        print(f"✅ {container} neu gestartet – warte 15s...")
        time.sleep(15)
        return True
    except Exception as e:
        print(f"⚠️ Scraper-Neustart fehlgeschlagen: {e}")
        return False

# ──────────────────────────────────────────────
# Resume: Progress-File Hilfsfunktionen
# ──────────────────────────────────────────────

def get_progress_path(job_id):
    return os.path.join(RESULT_FOLDER, f"progress_{job_id}.json")

def get_partial_path(job_id, suffix):
    return os.path.join(RESULT_FOLDER, f"partial_{job_id}_{suffix}.csv")

def load_progress(job_id):
    """Gespeicherten Fortschritt laden (falls vorhanden)"""
    path = get_progress_path(job_id)
    if os.path.exists(path):
        with open(path, 'r') as f:
            data = json.load(f)
        print(f"🔁 RESUME: ab Batch {data['last_completed_batch'] + 1}/{data['total_batches']}")
        return data
    return None

def save_progress(job_id, last_completed_batch, total_batches):
    """Fortschritt nach jedem Batch speichern"""
    path = get_progress_path(job_id)
    with open(path, 'w') as f:
        json.dump({"last_completed_batch": last_completed_batch, "total_batches": total_batches}, f)

def append_partial(job_id, df_filtered, df_raw):
    """Batch-Ergebnis an Partial-CSV anhängen"""
    for suffix, df in [('filtered', df_filtered), ('raw', df_raw)]:
        if df is None:
            continue
        path = get_partial_path(job_id, suffix)
        header = not os.path.exists(path)
        df.to_csv(path, mode='a', index=False, header=header, encoding='utf-8-sig', sep=';')

def load_partial(job_id):
    """Bestehende Partial-CSVs laden"""
    results_filtered, results_raw = [], []
    for suffix, lst in [('filtered', results_filtered), ('raw', results_raw)]:
        path = get_partial_path(job_id, suffix)
        if os.path.exists(path):
            try:
                df = pd.read_csv(path, sep=';', encoding='utf-8-sig')
                lst.append(df)
                print(f"📂 Partial {suffix}: {len(df)} Zeilen geladen")
            except Exception as e:
                print(f"⚠️ Partial {suffix} Ladefehler: {e}")
    return results_filtered, results_raw

def cleanup_progress(job_id):
    """Progress + Partial-Files nach Abschluss löschen"""
    for path in [
        get_progress_path(job_id),
        get_partial_path(job_id, 'filtered'),
        get_partial_path(job_id, 'raw'),
    ]:
        if os.path.exists(path):
            os.remove(path)

# ──────────────────────────────────────────────
# CSV Nachbearbeitung
# ──────────────────────────────────────────────

def process_result_csv(raw_bytes, input_df, apply_filter=True):
    try:
        content = raw_bytes.decode('utf-8', errors='replace')
        df_out = pd.read_csv(StringIO(content))
        print(f"📄 Raw result: {df_out.shape}")

        available = [c for c in OUTPUT_COLS if c in df_out.columns]
        df_out = df_out[available]

        for col in df_out.columns:
            df_out[col] = df_out[col].apply(fix_encoding)

        if apply_filter:
            input_addresses = build_input_addresses(input_df)
            before = len(df_out)
            df_out = df_out[
                df_out['address'].apply(lambda a: address_in_input(a, input_addresses))
            ]
            print(f"📍 Filter: {before} → {len(df_out)}")

        df_out = df_out.drop_duplicates(subset=['title', 'address'], keep='first')
        df_out = df_out.dropna(subset=['title'], how='all')
        df_out = df_out[df_out['title'].str.strip().astype(bool)]

        print(f"✅ Final ({'gefiltert' if apply_filter else 'alle'}): {df_out.shape}")
        return df_out
    except Exception as e:
        print(f"💥 process_result_csv: {e}")
        return None

# ──────────────────────────────────────────────
# HAUPT-WORKER
# ──────────────────────────────────────────────

def process_file(filename, job_id, app):
    print(f"🎯 {filename} Job#{job_id} START!")

    with app.app_context():
        job = Job.query.get(job_id)
        if not job:
            print("❌ Job missing")
            return

        try:
            #Parse + ALLE Queries
            job.status = "📊 parsing CSV"
            db.session.commit()

            filepath = os.path.join(UPLOAD_FOLDER, filename)
            print(f"📁 {filepath} | {os.path.getsize(filepath)}b")

            df_input = pd.read_csv(filepath, sep=';', encoding='ISO-8859-1')
            print(f"📊 {df_input.shape}")

            queries = []
            for _, row in df_input.iterrows():
                parts = [
                    str(row.get('PLZ', '')).strip(),
                    str(row.get('Stadt', '')).strip(),
                    str(row.get('Straße', '')).strip(),
                    str(row.get('Hausnummer', '')).strip(),
                    str(row.get('Zusatz', '')).strip(),
                ]
                q = f"Firmen {' '.join(p for p in parts if p and p != 'nan')}".strip()
                q = clean_query(q)  # Fix 1: Sonderzeichen bereinigen
                if len(q) > 10:
                    queries.append(q)

            total_queries = len(queries)
            print(f"🔍 {total_queries} Queries | Samples: {queries[:3]}")
            if total_queries == 0:
                raise ValueError("Keine gültigen Adressen")

            #BATCHED Processing
            batches = (total_queries + BATCH_SIZE - 1) // BATCH_SIZE

            # Resume: Fortschritt laden falls vorhanden
            os.makedirs(RESULT_FOLDER, exist_ok=True)
            progress = load_progress(job_id)
            start_batch = progress['last_completed_batch'] + 1 if progress else 0
            all_results_filtered, all_results_raw = load_partial(job_id) if progress else ([], [])

            eta_initial = format_eta((batches - start_batch) * ((BATCH_DELAY_MAX + MAX_TIME) / 2))
            print(f"📦 {batches} Batches à {BATCH_SIZE} | 2x Scraper | Start: {start_batch} | ETA: ~{eta_initial}")
            job_start_time = time.time()
            job.status = f"🔄 Batch {start_batch+1}/{batches} | ⏱️ ~{eta_initial}"
            db.session.commit()

            for batch_idx in range(start_batch, batches):
                batch_start = batch_idx * BATCH_SIZE
                batch_end = min(batch_start + BATCH_SIZE, total_queries)
                batch_queries = queries[batch_start:batch_end]

                # 2x Scraper: abwechselnd nutzen
                scraper_url = SCRAPER_URLS[batch_idx % len(SCRAPER_URLS)]
                print(f"\n🔄 BATCH {batch_idx+1}/{batches} ({batch_start+1}-{batch_end}/{total_queries}) → {scraper_url}")

                #Random Delay
                delay = random.uniform(BATCH_DELAY_MIN, BATCH_DELAY_MAX)
                print(f"😴 Delay: {delay:.0f}s")
                time.sleep(delay)

                #API Call
                payload = {
                    "name": f"{filename.replace('.csv','')}-{job_id}-B{batch_idx+1:03d}",
                    "keywords": batch_queries,
                    "lang": "de",
                    "depth": 1,
                    "zoom": 15,
                    "radius": 50,
                    "max_time": MAX_TIME,
                    "fast_mode": False,
                    "proxies": [PROXY_URL]
                }

                batch_success = False
                # Fix 2: Retry-Logik bei Scraper-Fehler
                for attempt in range(1, MAX_RETRIES + 1):
                    try:
                        resp = requests.post(f"{scraper_url}/api/v1/jobs", json=payload, timeout=45)
                        print(f"📤 {resp.status_code} (Versuch {attempt} | {scraper_url})")

                        if is_blocked(resp.text):
                            print("🚫 Batch übersprungen (blocked)")
                            break
                        if resp.status_code != 201:
                            print(f"⚠️ Batch {batch_idx+1} fehlgeschlagen: {resp.text[:100]}")
                            if attempt < MAX_RETRIES:
                                time.sleep(10)
                            continue

                        scraper_id = resp.json()['id']
                        print(f"✅ Scraper: {scraper_id}")

                        stuck_counter = 0
                        for poll_i in range(1, POLL_MAX + 1):
                            r = requests.get(f"{scraper_url}/api/v1/jobs/{scraper_id}", timeout=15)
                            data = r.json()
                            status = data.get('Status', data.get('status', '?'))
                            print(f"⏳ Poll {poll_i}: {status}")

                            # Fix 4: Auto-Recovery bei Pending-Stuck
                            if status == 'pending':
                                stuck_counter += 1
                                if stuck_counter >= STUCK_THRESHOLD:
                                    print(f"⚠️ Job {scraper_id} hängt – abbrechen + Neustart")
                                    requests.delete(f"{scraper_url}/api/v1/jobs/{scraper_id}", timeout=10)
                                    restart_scraper(scraper_url)  # Fix 3: Nur betroffenen Scraper neu starten
                                    break
                            else:
                                stuck_counter = 0

                            if status in ('ok', 'completed', 'scraped'):
                                dl = requests.get(f"{scraper_url}/api/v1/jobs/{scraper_id}/download", timeout=90)
                                if dl.status_code == 200:
                                    df_filtered = process_result_csv(dl.content, df_input, True)
                                    df_raw = process_result_csv(dl.content, df_input, False)
                                    if df_filtered is not None:
                                        all_results_filtered.append(df_filtered)
                                        all_results_raw.append(df_raw)
                                        append_partial(job_id, df_filtered, df_raw)  # Resume: sofort speichern
                                        print(f"📊 Batch {batch_idx+1}: {len(df_filtered)} filtered")
                                batch_success = True
                                break

                            # Fix 2: Scraper-Fehler → Retry
                            elif status in ('failed', 'error'):
                                print(f"💥 Batch {batch_idx+1}: {status} (Versuch {attempt})")
                                if attempt < MAX_RETRIES:
                                    time.sleep(10)
                                break

                            time.sleep(random.uniform(POLL_DELAY_MIN, POLL_DELAY_MAX))

                        if batch_success:
                            break

                    except Exception as e:
                        print(f"💥 Batch {batch_idx+1} Versuch {attempt}: {e}")
                        if attempt < MAX_RETRIES:
                            time.sleep(10)

                # Resume: Fortschritt nach jedem Batch speichern
                save_progress(job_id, batch_idx, batches)

                # ETA berechnen
                elapsed = time.time() - job_start_time
                done_so_far = batch_idx - start_batch + 1
                if done_so_far > 0:
                    avg_per_batch = elapsed / done_so_far
                    remaining = (batches - batch_idx - 1) * avg_per_batch
                    eta_str = format_eta(remaining)
                else:
                    eta_str = "?"

                job.status = f"🔄 Batch {batch_idx+2}/{batches} | ⏱️ ~{eta_str}"
                db.session.commit()

            #MERGE & SAVE
            job.status = "🔧 merging results"
            db.session.commit()

            base = filename.replace('.csv', '')

            if all_results_filtered:
                df_final_filtered = pd.concat(all_results_filtered, ignore_index=True)
                df_final_filtered = df_final_filtered.drop_duplicates(subset=['title', 'address'])

                out_filtered = f"results_{base}_filtered.csv"
                df_final_filtered.to_csv(
                    os.path.join(RESULT_FOLDER, out_filtered),
                    index=False, encoding='utf-8-sig', sep=';'
                )

                if all_results_raw:
                    df_final_raw = pd.concat(all_results_raw, ignore_index=True)
                    out_raw = f"results_{base}_all.csv"
                    df_final_raw.to_csv(
                        os.path.join(RESULT_FOLDER, out_raw),
                        index=False, encoding='utf-8-sig', sep=';'
                    )

                job.result_filename = out_filtered
                job.result_filename_raw = out_raw
                job.status = f"✅ Fertig: {len(df_final_filtered)} Firmen"

                # Resume: Cleanup nach Abschluss
                cleanup_progress(job_id)
            else:
                job.status = "❌ Keine Ergebnisse"

            db.session.commit()
            print(f"🎉 Job {job_id} komplett!")

        except Exception as e:
            job.status = f"Failed: {str(e)[:50]}"
            print(f"💥 FATAL: {e}")
            import traceback
            traceback.print_exc()
            db.session.commit()

        print(f"✅ DONE! Status: {job.status}")