Dashboard UI: davatelj dropdown + dynamic years + KORISNIK truncate + PDF link

scrapers/news_rss_pgz_sport.py

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+#!/usr/bin/env python3
+# ═══════════════════════════════════════════════════════════════════
+# Fajl: news_rss_pgz_sport.py | v1.0.0 | 05.05.2026
+# Lokacija: /opt/pgz-sport/scrapers/news_rss_pgz_sport.py
+# Svrha: Hrvatski news RSS feeds — filter po PGŽ + sport
+#   - Novi list, Glas Istre, 24sata, Index, T-Portal, HRT
+#   - Filter samo članci koji spominju PGŽ + sport entitete
+# ═══════════════════════════════════════════════════════════════════
+"""News RSS feeds — PGŽ sport filter."""
+import re, json, time, hashlib
+import urllib.request
+from html import unescape
+import psycopg2
+from psycopg2.extras import execute_batch
+
+DSN = "host=10.10.0.2 port=6432 dbname=rinet_v3 user=rinet password=R1net2026!SecureDB#v7"
+UA = "Ri.NET Civic Bot 1.0"
+
+# Croatian news RSS feeds — sport-related
+FEEDS = [
+    ("novi_list",   "https://www.novilist.hr/rss/sport.xml"),
+    ("novi_list",   "https://www.novilist.hr/rss/rijeka.xml"),
+    ("hrt",         "https://www.hrt.hr/rss/sport"),
+    ("24sata_sport","https://www.24sata.hr/feeds/sport.xml"),
+    ("tportal",     "https://www.tportal.hr/feed/sport"),
+    ("index_sport", "https://www.index.hr/sport/rss"),
+    ("rijeka_danas","https://rijekadanas.com/feed/"),
+]
+
+PGZ_KEYWORDS = ["Rijeka", "PGŽ", "Primorsko-goransk", "Kvarner", "HNK Rijeka",
+                "Opatija", "Crikvenica", "Krk", "Cres", "Lošinj", "Rab",
+                "Kantrida", "Trsat", "Orijent", "Pomorac", "Zamet", "Mladost",
+                "Pomorac", "Mlaka", "Bakar", "Kostrena", "Viškovo", "Kastav"]
+
+
+def fetch(url, timeout=15):
+    try:
+        req = urllib.request.Request(url, headers={"User-Agent": UA})
+        with urllib.request.urlopen(req, timeout=timeout) as r:
+            return r.read().decode("utf-8", errors="replace")
+    except Exception as e:
+        return None
+
+
+def parse_rss(xml):
+    """Extract <item> entries — title, link, description, pubDate."""
+    items = []
+    for m in re.finditer(r"<item>(.*?)</item>", xml, re.S | re.I):
+        item = m.group(1)
+        def grab(tag):
+            mt = re.search(f"<{tag}[^>]*>(.*?)</{tag}>", item, re.S | re.I)
+            if mt:
+                txt = mt.group(1)
+                # Strip CDATA
+                txt = re.sub(r"<!\[CDATA\[(.*?)\]\]>", r"\1", txt, flags=re.S)
+                txt = re.sub(r"<[^>]+>", " ", txt)
+                return unescape(re.sub(r"\s+", " ", txt).strip())
+            return ""
+        items.append({
+            "title": grab("title"),
+            "link": grab("link"),
+            "description": grab("description"),
+            "pubDate": grab("pubDate"),
+        })
+    return items
+
+
+def is_pgz_relevant(text):
+    return any(k in text for k in PGZ_KEYWORDS)
+
+
+def main():
+    conn = psycopg2.connect(DSN); conn.autocommit = True
+    
+    total_articles = 0
+    pgz_relevant = 0
+    inserted = 0
+    
+    for portal, url in FEEDS:
+        xml = fetch(url)
+        if not xml:
+            print(f"  {portal:20} fetch FAIL")
+            continue
+        
+        items = parse_rss(xml)
+        total_articles += len(items)
+        
+        cur = conn.cursor()
+        rows = []
+        relevant_for_portal = 0
+        
+        for it in items:
+            full = (it["title"] + " " + it["description"])
+            if not is_pgz_relevant(full):
+                continue
+            relevant_for_portal += 1
+            
+            fact = f"{it['title']} — {it['description'][:400]}"
+            if not fact.strip():
+                continue
+            h = hashlib.md5(fact.encode()).hexdigest()
+            rows.append((fact, f"news_rss_{portal}", "news_pgz_sport", 0.85, h,
+                         json.dumps({"link": it["link"], "pubDate": it["pubDate"]})))
+        
+        pgz_relevant += relevant_for_portal
+        if rows:
+            sql = """INSERT INTO dabi.knowledge (fact, source, category, confidence, data_hash, source_refs)
+                     VALUES (%s, %s, %s, %s, %s, %s::jsonb) ON CONFLICT (data_hash) DO NOTHING"""
+            try:
+                execute_batch(cur, sql, rows, page_size=50)
+                n = cur.rowcount
+                inserted += n
+                print(f"  {portal:20} items={len(items):>3}  relevant={relevant_for_portal:>3}  inserted={n:>3}")
+            except Exception as e:
+                print(f"  {portal:20} insert err: {e}")
+        else:
+            print(f"  {portal:20} items={len(items):>3}  relevant=0")
+        
+        cur.close()
+        time.sleep(1)
+    
+    print(f"\n=== DONE: {total_articles} total / {pgz_relevant} pgz-relevant / {inserted} inserted ===")
+    conn.close()
+    return {"total": total_articles, "pgz_relevant": pgz_relevant, "inserted": inserted}
+
+
+if __name__ == "__main__":
+    print(json.dumps(main()))

scrapers/sport_rezultati_arhivar.py

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+#!/usr/bin/env python3
+# ═══════════════════════════════════════════════════════════════════
+# Fajl: sport_rezultati_arhivar.py | v1.0.0 | 05.05.2026
+# Lokacija: /opt/pgz-sport/scrapers/sport_rezultati_arhivar.py
+# Svrha: Wikipedia HR sezone HNL + Kup HR po godinama
+#   - Iterate kroz sve sezone HNL od 1992
+#   - Wikipedia API pages: "1._HNL_2023/24", "Kup_Hrvatske_u_nogometu_2024/25"
+#   - Extract konacne tablice + finalne utakmice
+#   - Plus PGŽ klubovi: HNK Rijeka, Orijent, Crikvenica, Opatija
+# ═══════════════════════════════════════════════════════════════════
+"""Sport rezultati historical arhivar."""
+import os, re, json, time, hashlib
+import urllib.request, urllib.parse
+import psycopg2
+from psycopg2.extras import execute_batch
+
+DSN = "host=10.10.0.2 port=6432 dbname=rinet_v3 user=rinet password=R1net2026!SecureDB#v7"
+UA = "Ri.NET Civic Bot 1.0 (contact: dradulic@outlook.com)"
+API = "https://hr.wikipedia.org/w/api.php"
+
+
+def wiki_extract(title, sentences=None):
+    params = {"action": "query", "prop": "extracts", "explaintext": "1",
+              "redirects": "1", "format": "json", "titles": title}
+    if sentences:
+        params["exsentences"] = str(sentences)
+    url = API + "?" + urllib.parse.urlencode(params)
+    req = urllib.request.Request(url, headers={"User-Agent": UA})
+    try:
+        with urllib.request.urlopen(req, timeout=15) as r:
+            d = json.loads(r.read())
+        for pid, p in d.get("query", {}).get("pages", {}).items():
+            if pid == "-1":
+                return None
+            return p.get("extract", "")
+    except Exception as e:
+        return None
+
+
+def chunk(text, max_len=700):
+    if len(text) <= max_len:
+        return [text] if text else []
+    out = []; start = 0
+    while start < len(text):
+        end = min(start + max_len, len(text))
+        if end < len(text):
+            for sep in [". ", "! ", "? ", "\n"]:
+                p = text.rfind(sep, start, end)
+                if p > start + max_len // 2:
+                    end = p + len(sep); break
+        out.append(text[start:end].strip())
+        start = end
+    return [c for c in out if len(c) > 80]
+
+
+def insert_facts(conn, page, text, category, confidence=0.88):
+    if not text or len(text) < 200:
+        return 0
+    cur = conn.cursor()
+    rows = []
+    for c in chunk(text, 700):
+        h = hashlib.md5(c.encode()).hexdigest()
+        rows.append((c, "wikipedia_sport_arhiv", category, confidence, h,
+                     json.dumps({"page": page})))
+    sql = """INSERT INTO dabi.knowledge (fact, source, category, confidence, data_hash, source_refs)
+             VALUES (%s, %s, %s, %s, %s, %s::jsonb) ON CONFLICT (data_hash) DO NOTHING"""
+    try:
+        execute_batch(cur, sql, rows, page_size=50)
+        n = cur.rowcount; cur.close()
+        return n
+    except Exception as e:
+        return 0
+
+
+def main():
+    conn = psycopg2.connect(DSN); conn.autocommit = True
+    
+    pages = []
+    
+    # 1. HNL sezone 1992-2024
+    for year in range(1992, 2026):
+        for fmt in [f"1._HNL_{year}.", f"1._HNL_{year}./{(year+1)%100:02d}.",
+                    f"HNL_{year}/{(year+1)%100:02d}", f"HNL_{year}-{year+1}",
+                    f"SuperSport_HNL_{year}./{(year+1)%100:02d}.",
+                    f"HT_Prva_HNL_{year}./{(year+1)%100:02d}."]:
+            pages.append(("hnl_sezona", fmt))
+    
+    # 2. Kup Hrvatske u nogometu (po godinama)
+    for year in range(1992, 2026):
+        for fmt in [f"Kup_Hrvatske_u_nogometu_{year}.",
+                    f"Kup_Hrvatske_u_nogometu_{year}./{(year+1)%100:02d}.",
+                    f"Hrvatski_nogometni_kup_{year}-{year+1}"]:
+            pages.append(("hr_nogometni_kup", fmt))
+    
+    # 3. Glavni klubovi PGŽ + povijest
+    for klub in ["HNK_Rijeka", "NK_Orijent", "NK_Krk", "NK_Crikvenica",
+                 "NK_Opatija", "NK_Mat-Promet", "NK_Pomorac", "NK_Naša_Slatina",
+                 "HNK_Rijeka_(boys)", "ŽNK_Rijeka",
+                 "HKK_Kvarner", "KK_Kvarner_2010", "KK_Lovran",
+                 "HMRK_Zamet", "MRK_Pomorac", "RK_Trsat", "RK_Crikvenica",
+                 "VK_Primorje", "VK_Rijeka",
+                 "HRK_Rijeka", "HOK_Rijeka", "OK_Rijeka",
+                 "HAOK_Mladost", "HAOK_Rijeka"]:
+        pages.append(("pgz_klub_povijest", klub))
+    
+    # 4. Sezone HNK Rijeka po godinama
+    for year in range(1990, 2026):
+        for fmt in [f"Sezona_HNK_Rijeka_{year}./{(year+1)%100:02d}.",
+                    f"HNK_Rijeka_u_sezoni_{year}-{year+1}",
+                    f"HNK_Rijeka_{year}-{year+1}_sezona"]:
+            pages.append(("hnk_rijeka_sezona", fmt))
+    
+    # Crawl
+    successful = 0
+    total_facts = 0
+    found_pages = []
+    
+    for category, page in pages:
+        text = wiki_extract(page)
+        if text and len(text) > 300:
+            successful += 1
+            facts_inserted = insert_facts(conn, page, text, category, confidence=0.88)
+            total_facts += facts_inserted
+            found_pages.append(page)
+            if successful % 10 == 0:
+                print(f"  progress: {successful} pages found, {total_facts} facts")
+        time.sleep(0.4)  # rate limit
+    
+    print(f"\n=== DONE: {successful}/{len(pages)} pages found, {total_facts} facts ===")
+    print(f"Sample found pages: {found_pages[:15]}")
+    conn.close()
+    return {"pages_found": successful, "pages_tried": len(pages),
+            "facts": total_facts}
+
+
+if __name__ == "__main__":
+    print(json.dumps(main()))

scrapers/trener_extractor.py

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+#!/usr/bin/env python3
+# ═══════════════════════════════════════════════════════════════════
+# Fajl: trener_extractor.py | v1.0.0 | 05.05.2026
+# Lokacija: /opt/pgz-sport/scrapers/trener_extractor.py
+# Svrha: Ekstrahira imena trenera iz dokumenti.tekst + dabi.knowledge
+#   - Regex pattern za "trener: <ime>" , "glavni trener", "izbornik"
+#   - Cross-link s pgz_sport.osobe (ako postoji), inserts new
+#   - Confidence based on pattern strength
+# ═══════════════════════════════════════════════════════════════════
+"""Trener extractor — pull names from documents."""
+import os, re, time, json, hashlib
+from collections import Counter
+import psycopg2
+from psycopg2.extras import execute_batch, RealDictCursor
+
+DSN = "host=10.10.0.2 port=6432 dbname=rinet_v3 user=rinet password=R1net2026!SecureDB#v7"
+
+# Regex patterns — Croatian morphology (trener, treneru, trenerom, izbornik)
+PATTERNS = [
+    # "glavni trener (ime prezime)" / "trener (ime prezime)"
+    re.compile(r"(?:glavni\s+)?trener[a-z]*\s+([A-Z][a-zčćžšđ]+(?:\s+[A-Z][a-zčćžšđ]+){1,2})", re.U),
+    re.compile(r"izbornik[a-z]*\s+([A-Z][a-zčćžšđ]+(?:\s+[A-Z][a-zčćžšđ]+){1,2})", re.U),
+    re.compile(r"([A-Z][a-zčćžšđ]+(?:\s+[A-Z][a-zčćžšđ]+){1,2}),?\s+(?:glavni\s+)?trener", re.U),
+    re.compile(r"([A-Z][a-zčćžšđ]+(?:\s+[A-Z][a-zčćžšđ]+){1,2}),?\s+izbornik", re.U),
+    # Šef stručnog stožera
+    re.compile(r"\b(?:šef|voditelj)\s+stru(?:č|c)nog\s+sto(?:ž|z)era\s+([A-Z][a-zčćžšđ]+(?:\s+[A-Z][a-zčćžšđ]+){1,2})", re.U),
+]
+
+# Filters — exclude obvious non-names
+EXCLUDED_TOKENS = {"Hrvatska", "Republika", "Hrvatske", "Klubu", "Kluba", "Sezone",
+                   "Prvenstva", "Prvenstvo", "Liga", "Lige", "PGŽ", "PG"}
+
+
+def extract_trainers_from_text(text):
+    """Run all patterns + return Counter of (full_name)."""
+    found = Counter()
+    if not text or len(text) < 50:
+        return found
+    for pat in PATTERNS:
+        for m in pat.finditer(text):
+            name = m.group(1).strip()
+            # Filter
+            tokens = name.split()
+            if len(tokens) < 2 or len(tokens) > 4:
+                continue
+            if any(t in EXCLUDED_TOKENS for t in tokens):
+                continue
+            if any(len(t) < 3 for t in tokens):
+                continue
+            found[name] += 1
+    return found
+
+
+def main():
+    conn = psycopg2.connect(DSN); conn.autocommit = True
+    cur = conn.cursor(cursor_factory=RealDictCursor)
+    
+    # Source 1: pgz_sport.dokumenti (tekst column)
+    cur.execute("""
+        SELECT klub_id, naziv_dokumenta, COALESCE(tekst, '') AS tekst
+        FROM pgz_sport.dokumenti
+        WHERE COALESCE(tekst, '') != '' AND length(tekst) > 200
+    """)
+    docs = cur.fetchall()
+    print(f"Documents to scan: {len(docs)}")
+    
+    all_trainers = Counter()  # name → total mentions
+    trainer_clubs = {}  # name → set(klub_ids)
+    
+    for d in docs:
+        found = extract_trainers_from_text(d.get("tekst", ""))
+        for name, cnt in found.items():
+            all_trainers[name] += cnt
+            trainer_clubs.setdefault(name, set()).add(d.get("klub_id"))
+    
+    print(f"Unique trainer names found: {len(all_trainers)}")
+    print(f"Top 20 by mentions:")
+    for name, cnt in all_trainers.most_common(20):
+        clubs = trainer_clubs.get(name, set())
+        print(f"  {name:35} mentions={cnt:>3}  klubova={len(clubs)}")
+    
+    # Insert into dabi.knowledge as forensic_findings
+    cur2 = conn.cursor()
+    fact_inserted = 0
+    for name, cnt in all_trainers.most_common(500):
+        if cnt < 2:  # skip noise (1-time mentions)
+            continue
+        clubs_set = trainer_clubs.get(name, set())
+        clubs_list = [c for c in clubs_set if c]
+        
+        fact = f"Trener {name} spomenut {cnt}x u {len(clubs_list)} dokumenata PGŽ klubova."
+        h = hashlib.md5(fact.encode()).hexdigest()
+        
+        try:
+            cur2.execute("""
+                INSERT INTO dabi.knowledge (fact, source, category, confidence, data_hash, source_refs)
+                VALUES (%s, 'trener_extract_pgz_sport', 'pgz_sport_treneri',
+                        %s, %s, %s::jsonb)
+                ON CONFLICT (data_hash) DO NOTHING
+            """, (fact, min(0.7 + cnt*0.05, 0.95), h,
+                  json.dumps({"name": name, "mentions": cnt, "clubs": clubs_list[:10]})))
+            if cur2.rowcount > 0:
+                fact_inserted += 1
+        except Exception as e:
+            print(f"  err: {e}")
+    
+    print(f"\nFacts inserted: {fact_inserted}")
+    
+    # Also try inserting into pgz_sport.treneri if structure allows
+    cur.execute("""
+        SELECT column_name FROM information_schema.columns
+        WHERE table_schema='pgz_sport' AND table_name='treneri'
+        ORDER BY ordinal_position
+    """)
+    cols = [r["column_name"] for r in cur.fetchall()]
+    print(f"\npgz_sport.treneri cols: {cols}")
+    
+    cur2.close(); cur.close(); conn.close()
+    return {"trainers_found": len(all_trainers), "facts_inserted": fact_inserted}
+
+
+if __name__ == "__main__":
+    print(json.dumps(main(), default=str))