Initial commit
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mkrieger
parent
387bc056b9
commit
df8c2313a9
275 changed files with 12939 additions and 263 deletions
275
app/webcrawler.bck04032026
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275
app/webcrawler.bck04032026
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import os
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import re
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import pandas as pd
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import requests
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import time
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import random
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from io import StringIO
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from app.models import db, Job
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print("🆕 MODERN webcrawler LOADED! – BATCHED + PROXY")
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UPLOAD_FOLDER = '/app/uploads'
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RESULT_FOLDER = '/app/results'
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SCRAPER_URL = "http://gmaps-scraper:8080"
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OUTPUT_COLS = ['title', 'category', 'address', 'open_hours', 'website', 'phone', 'link']
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PROXY_URL = "http://bitlleuv-rotate:s5hzse6hz74b@p.webshare.io:80"
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API_PROXIES = {"http": PROXY_URL, "https": PROXY_URL}
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# ──────────────────────────────────────────────
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# Hilfsfunktionen
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# ──────────────────────────────────────────────
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def is_blocked(data):
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text = str(data).lower()
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blocked = any(kw in text for kw in ['captcha', 'blocked', 'rate limit', 'too many', '429'])
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if blocked:
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print(f"🚫 BLOCKED: {str(data)[:100]}")
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return blocked
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def fix_encoding(text):
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if not isinstance(text, str):
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return text
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try:
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return text.encode('latin-1').decode('utf-8')
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except (UnicodeEncodeError, UnicodeDecodeError):
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return text
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def build_input_addresses(df):
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addresses = set()
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for _, row in df.iterrows():
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plz = str(row.get('PLZ', '')).strip()
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stadt = str(row.get('Stadt', '')).strip()
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str_ = str(row.get('Straße', '')).strip()
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nr = str(row.get('Hausnummer', '')).strip()
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zusatz = str(row.get('Zusatz', '')).strip()
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full = f"{str_} {nr} {zusatz} {plz} {stadt}".lower().strip()
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full = ' '.join(full.split())
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addresses.add(full)
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return addresses
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def normalize_address(addr):
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if not isinstance(addr, str):
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return ''
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addr = fix_encoding(addr)
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return ' '.join(addr.lower().strip().split())
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def address_in_input(result_addr, input_addresses):
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norm = normalize_address(result_addr)
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for inp_addr in input_addresses:
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plz_match = re.search(r'\b\d{5}\b', inp_addr)
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if plz_match:
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plz = plz_match.group()
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if plz in norm:
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street = inp_addr.split()[0] if inp_addr else ''
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if len(street) > 3 and street[:4].lower() in norm:
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return True
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return False
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# ──────────────────────────────────────────────
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# CSV Nachbearbeitung
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# ──────────────────────────────────────────────
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def process_result_csv(raw_bytes, input_df, apply_filter=True):
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try:
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content = raw_bytes.decode('utf-8', errors='replace')
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df_out = pd.read_csv(StringIO(content))
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print(f"📄 Raw result: {df_out.shape}")
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available = [c for c in OUTPUT_COLS if c in df_out.columns]
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df_out = df_out[available]
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for col in df_out.columns:
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df_out[col] = df_out[col].apply(fix_encoding)
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if apply_filter:
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input_addresses = build_input_addresses(input_df)
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before = len(df_out)
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df_out = df_out[
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df_out['address'].apply(
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lambda a: address_in_input(a, input_addresses)
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)
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]
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print(f"📍 Filter: {before} → {len(df_out)}")
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df_out = df_out.drop_duplicates(subset=['title', 'address'], keep='first')
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df_out = df_out.dropna(subset=['title'], how='all')
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df_out = df_out[df_out['title'].str.strip().astype(bool)]
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print(f"✅ Final ({'gefiltert' if apply_filter else 'alle'}): {df_out.shape}")
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return df_out
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except Exception as e:
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print(f"💥 process_result_csv: {e}")
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return None
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# ──────────────────────────────────────────────
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# HAUPT-WORKER
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# ──────────────────────────────────────────────
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def process_file(filename, job_id, app):
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print(f"🎯 {filename} Job#{job_id} START!")
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with app.app_context():
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job = Job.query.get(job_id)
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if not job:
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print("❌ Job missing")
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return
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try:
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#Parse + ALLE Queries
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job.status = "📊 parsing CSV"
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db.session.commit()
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filepath = os.path.join(UPLOAD_FOLDER, filename)
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print(f"📁 {filepath} | {os.path.getsize(filepath)}b")
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df_input = pd.read_csv(filepath, sep=';', encoding='ISO-8859-1')
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print(f"📊 {df_input.shape}")
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queries = []
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for _, row in df_input.iterrows():
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parts = [
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str(row.get('PLZ', '')).strip(),
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str(row.get('Stadt', '')).strip(),
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str(row.get('Straße', '')).strip(),
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str(row.get('Hausnummer', '')).strip(),
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str(row.get('Zusatz', '')).strip(),
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]
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q = f"Firmen {' '.join(p for p in parts if p and p != 'nan')}".strip()
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if len(q) > 10:
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queries.append(q)
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total_queries = len(queries)
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print(f"🔍 {total_queries} Queries | Samples: {queries[:3]}")
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if total_queries == 0:
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raise ValueError("Keine gültigen Adressen")
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#BATCHED Processing
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BATCH_SIZE = 10 # Erhöht: 5 → 10 (paid proxy)
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BATCH_DELAY_MIN, BATCH_DELAY_MAX = 10, 20 # Reduziert: 30-60s → 10-20s (paid proxy)
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batches = (total_queries + BATCH_SIZE - 1) // BATCH_SIZE
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print(f"📦 {batches} Batches à {BATCH_SIZE} | ETA: ~{batches*15//60:.0f}h")
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all_results_filtered = []
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all_results_raw = []
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job.status = f"🔄 Batch 1/{batches}"
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db.session.commit()
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for batch_idx in range(batches):
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batch_start = batch_idx * BATCH_SIZE
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batch_end = min(batch_start + BATCH_SIZE, total_queries)
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batch_queries = queries[batch_start:batch_end]
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print(f"\n🔄 BATCH {batch_idx+1}/{batches} ({batch_start+1}-{batch_end}/{total_queries})")
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#Random Delay
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delay = random.uniform(BATCH_DELAY_MIN, BATCH_DELAY_MAX)
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print(f"😴 Delay: {delay:.0f}s | Proxy: {PROXY_URL}")
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time.sleep(delay)
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#API Call
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payload = {
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"name": f"{filename.replace('.csv','')}-{job_id}-B{batch_idx+1:03d}",
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"keywords": batch_queries,
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"lang": "de",
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"depth": 1,
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"zoom": 17,
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"radius": 50,
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"max_time": 60, # Reduziert: 120 → 60 (paid proxy schneller)
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"fast_mode": False,
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"proxies": [PROXY_URL]
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}
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try:
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resp = requests.post(
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f"{SCRAPER_URL}/api/v1/jobs",
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json=payload,
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timeout=45
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)
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print(f"📤 {resp.status_code}")
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if is_blocked(resp.text):
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print("🚫 Batch übersprungen (blocked)")
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continue
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if resp.status_code != 201:
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print(f"⚠️ Batch {batch_idx+1} fehlgeschlagen: {resp.text[:100]}")
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continue
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scraper_id = resp.json()['id']
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print(f"✅ Scraper: {scraper_id}")
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for poll_i in range(1, 61): # Reduziert: 121 → 61 (max_time 60s)
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r = requests.get(
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f"{SCRAPER_URL}/api/v1/jobs/{scraper_id}",
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timeout=15
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)
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data = r.json()
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status = data.get('Status', data.get('status', '?'))
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if status in ('ok', 'completed', 'scraped'):
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dl = requests.get(
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f"{SCRAPER_URL}/api/v1/jobs/{scraper_id}/download",
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timeout=90
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)
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if dl.status_code == 200:
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df_filtered = process_result_csv(dl.content, df_input, True)
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df_raw = process_result_csv(dl.content, df_input, False)
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if df_filtered is not None:
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all_results_filtered.append(df_filtered)
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all_results_raw.append(df_raw)
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print(f"📊 Batch {batch_idx+1}: {len(df_filtered)} filtered")
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break
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elif status in ('failed', 'error'):
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print(f"💥 Batch {batch_idx+1}: {status}")
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break
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time.sleep(random.uniform(5, 10)) # Reduziert: 10-20s → 5-10s (paid proxy)
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except Exception as e:
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print(f"💥 Batch {batch_idx+1}: {e}")
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job.status = f"🔄 Batch {batch_idx+2}/{batches}"
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db.session.commit()
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#MERGE & SAVE
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job.status = "🔧 merging results"
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db.session.commit()
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base = filename.replace('.csv', '')
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os.makedirs(RESULT_FOLDER, exist_ok=True)
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if all_results_filtered:
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df_final_filtered = pd.concat(all_results_filtered, ignore_index=True)
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df_final_filtered = df_final_filtered.drop_duplicates(subset=['title', 'address'])
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out_filtered = f"results_{base}_filtered.csv"
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df_final_filtered.to_csv(
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os.path.join(RESULT_FOLDER, out_filtered),
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index=False, encoding='utf-8-sig', sep=';'
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)
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if all_results_raw:
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df_final_raw = pd.concat(all_results_raw, ignore_index=True)
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out_raw = f"results_{base}_all.csv"
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df_final_raw.to_csv(
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os.path.join(RESULT_FOLDER, out_raw),
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index=False, encoding='utf-8-sig', sep=';'
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)
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job.result_filename = out_filtered
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job.result_filename_raw = out_raw
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job.status = f"✅ Fertig: {len(df_final_filtered)} Firmen"
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else:
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job.status = "❌ Keine Ergebnisse"
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db.session.commit()
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print(f"🎉 Job {job_id} komplett!")
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except Exception as e:
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job.status = f"Failed: {str(e)[:50]}"
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print(f"💥 FATAL: {e}")
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import traceback
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traceback.print_exc()
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db.session.commit()
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print(f"✅ DONE! Status: {job.status}")
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