feat(app): Add wiki re-evaluation and fix wolfra bug

- Implemented a "Re-evaluate Wikipedia" button in the UI.

- Added a backend endpoint to trigger targeted Wikipedia metric extraction.

- Hardened the LLM metric extraction prompt to prevent hallucinations.

- Corrected several database path errors that caused data loss.

- Updated application version to 0.6.4 and documented the ongoing issue.

MIGRATION_PLAN.md

@@ -94,7 +94,39 @@ Wir kapseln das neue Projekt vollständig ab ("Fork & Clean").
 
 ## 7. Historie & Fixes (Jan 2026)
 
+    *   **[STABILITY] v0.7.2: Robust Metric Parsing (Jan 23, 2026)**
+        *   **Legacy Logic Restored:** Re-implemented the robust, regex-based number parsing logic (formerly in legacy helpers) as `MetricParser`.
+        *   **German Formats:** Correctly handles "1.000" (thousands) vs "1,5" (decimal) and mixed formats.
+        *   **Citation Cleaning:** Filters out Wikipedia citations like `[3]` and years in parentheses (e.g. "80 (2020)" -> 80).
+        *   **Hybrid Extraction:** The ClassificationService now asks the LLM for the *text segment* and parses the number deterministically, fixing the "1.005 -> 1" LLM hallucination.
+
+    *   **[ONGOING] v0.6.4: Wolfra Metric Extraction Bug (Jan 23, 2026)**
+        *   **Problem:** Mitarbeiterzahl für "Wolfra Bayrische Natursaft Kelterei GmbH" wird fälschlicherweise als "802020" anstatt "80" ausgelesen.
+        *   **Implementierte Maßnahmen:**
+            *   "Wiki-Reevaluate-Button" im Frontend integriert (POST `/api/companies/{company_id}/reevaluate-wikipedia`).
+            *   `reevaluate_wikipedia_metric`-Funktion im `ClassificationService` erstellt.
+            *   Prompt für `_run_llm_metric_extraction_prompt` geschärft, um LLM zur Rückgabe von `raw_text_segment` zu zwingen.
+            *   Datenbankpfad-Konfiguration in `company-explorer/backend/config.py` mehrfach korrigiert, um `unable to open database file` Fehler zu beheben.
+            *   Fehler in `ClassificationService._get_wikipedia_content` behoben (`wiki_data.get('text')` zu `wiki_data.get('full_text')` geändert).
+        *   **Aktueller Status:** Problem **nicht gelöst**. Trotz der Korrekturen zeigt das System immer noch falsche Werte an, und der Datenbankzugriff war mehrfach fehlerhaft, was zu Datenverlust führte. Weitere Diagnose ist erforderlich, um die genaue LLM-Antwort und den Datenfluss im Container zu überprüfen.
+
+    *   **[STABILITY] v0.7.1: AI Robustness & UI Fixes (Jan 21, 2026)**
+        *   **SDK Stabilität:** Umstellung auf `gemini-2.0-flash` im Legacy-SDK zur Behebung von `404 Not Found` Fehlern bei `1.5-flash-latest`.
+        *   **API-Key Management:** Implementierung eines robusten Ladevorgangs für den Google API Key (Fallback von Environment-Variable auf lokale Datei `/app/gemini_api_key.txt`).
+        *   **Classification Prompt:** Schärfung des Prompts auf "Best-Fit"-Entscheidungen, um zu konservative "Others"-Einstufungen bei klaren Kandidaten (z.B. Thermen) zu vermeiden.
+        *   **Frontend Rendering:** Fix eines UI-Crashs im Inspector. Metriken werden jetzt auch angezeigt, wenn nur der standardisierte Wert (Fläche) vorhanden ist. Null-Safety für `.toLocaleString()` hinzugefügt.
+        *   **Scraping:** Wiederherstellung der Stabilität durch Entfernung fehlerhafter `trafilatura` Abhängigkeiten; Nutzung von `BeautifulSoup` als robustem Standard.
+
     *   **[MAJOR] v0.7.0: Quantitative Potential Analysis (Jan 20, 2026)**
+...
+...
+## 11. Lessons Learned (Retrospektive Jan 21, 2026)
+
+1.  **KI statt Regex für Zahlen:** Anstatt komplexe Python-Funktionen für deutsche Zahlenformate ("1,7 Mio.") zu schreiben, ist es stabiler, das LLM anzuweisen, den Wert direkt als Integer (1700000) zu liefern.
+2.  **Abhängigkeiten isolieren:** Änderungen an zentralen `core_utils.py` führen schnell zu Import-Fehlern in anderen Modulen. Spezifische Logik (wie Metrik-Parsing) sollte lokal im Service bleiben.
+3.  **UI Null-Safety:** Quantitative Daten sind oft unvollständig (z.B. Fläche vorhanden, aber Besucherzahl nicht). Das Frontend muss robust gegen `null`-Werte in den Metrik-Feldern sein, um den Render-Prozess nicht zu unterbrechen.
+4.  **SDK-Versionen:** Die Google-API ist in stetigem Wandel. Der explizite Rückgriff auf stabile Modelle wie `gemini-2.0-flash` ist im Legacy-SDK sicherer als die Nutzung von `-latest` Tags.
+
     *   **Zweistufige Analyse:** 
         1.  **Strict Classification:** Ordnet Firmen einer Notion-Branche zu (oder "Others").
         2.  **Metric Cascade:** Sucht gezielt nach der branchenspezifischen Metrik ("Scraper Search Term").

company-explorer/backend/app.py

@@ -58,6 +58,9 @@ class AnalysisRequest(BaseModel):
     company_id: int
     force_scrape: bool = False
 
+class IndustryUpdateModel(BaseModel):
+    industry_ai: str
+
 # --- Events ---
 @app.on_event("startup")
 def on_startup():
@@ -137,6 +140,137 @@ def analyze_company(req: AnalysisRequest, background_tasks: BackgroundTasks, db:
     background_tasks.add_task(run_analysis_task, company.id)
     return {"status": "queued"}
 
+@app.put("/api/companies/{company_id}/industry")
+def update_company_industry(
+    company_id: int, 
+    data: IndustryUpdateModel, 
+    background_tasks: BackgroundTasks,
+    db: Session = Depends(get_db)
+):
+    company = db.query(Company).filter(Company.id == company_id).first()
+    if not company:
+        raise HTTPException(404, detail="Company not found")
+    
+    # 1. Update Industry
+    company.industry_ai = data.industry_ai
+    company.updated_at = datetime.utcnow()
+    db.commit()
+    
+    # 2. Trigger Metric Re-extraction in Background
+    background_tasks.add_task(run_metric_reextraction_task, company.id)
+    
+    return {"status": "updated", "industry_ai": company.industry_ai}
+
+
+@app.post("/api/companies/{company_id}/reevaluate-wikipedia")
+def reevaluate_wikipedia(company_id: int, background_tasks: BackgroundTasks, db: Session = Depends(get_db)):
+    company = db.query(Company).filter(Company.id == company_id).first()
+    if not company:
+        raise HTTPException(404, detail="Company not found")
+    
+    background_tasks.add_task(run_wikipedia_reevaluation_task, company.id)
+    return {"status": "queued"}
+
+
+@app.delete("/api/companies/{company_id}")
+def delete_company(company_id: int, db: Session = Depends(get_db)):
+    company = db.query(Company).filter(Company.id == company_id).first()
+    if not company:
+        raise HTTPException(404, detail="Company not found")
+    
+    # Delete related data first (Cascade might handle this but being explicit is safer)
+    db.query(EnrichmentData).filter(EnrichmentData.company_id == company_id).delete()
+    db.query(Signal).filter(Signal.company_id == company_id).delete()
+    db.query(Contact).filter(Contact.company_id == company_id).delete()
+    
+    db.delete(company)
+    db.commit()
+    return {"status": "deleted"}
+
+@app.post("/api/companies/{company_id}/override/website")
+def override_website(company_id: int, url: str, db: Session = Depends(get_db)):
+    company = db.query(Company).filter(Company.id == company_id).first()
+    if not company:
+        raise HTTPException(404, detail="Company not found")
+    
+    company.website = url
+    company.updated_at = datetime.utcnow()
+    db.commit()
+    return {"status": "updated", "website": company.website}
+
+@app.post("/api/companies/{company_id}/override/impressum")
+def override_impressum(company_id: int, url: str, background_tasks: BackgroundTasks, db: Session = Depends(get_db)):
+    company = db.query(Company).filter(Company.id == company_id).first()
+    if not company:
+        raise HTTPException(404, detail="Company not found")
+    
+    # Create or update manual impressum lock
+    existing = db.query(EnrichmentData).filter(
+        EnrichmentData.company_id == company_id, 
+        EnrichmentData.source_type == "impressum_override"
+    ).first()
+    
+    if not existing:
+        db.add(EnrichmentData(
+            company_id=company_id, 
+            source_type="impressum_override", 
+            content={"url": url},
+            is_locked=True
+        ))
+    else:
+        existing.content = {"url": url}
+        existing.is_locked = True
+    
+    db.commit()
+    return {"status": "updated"}
+
+def run_wikipedia_reevaluation_task(company_id: int):
+    from .database import SessionLocal
+    db = SessionLocal()
+    try:
+        company = db.query(Company).filter(Company.id == company_id).first()
+        if not company: return
+
+        logger.info(f"Re-evaluating Wikipedia metric for {company.name} (Industry: {company.industry_ai})")
+        
+        industry = db.query(Industry).filter(Industry.name == company.industry_ai).first()
+        
+        if industry:
+            classifier.reevaluate_wikipedia_metric(company, db, industry)
+            logger.info(f"Wikipedia metric re-evaluation complete for {company.name}")
+        else:
+            logger.warning(f"Industry '{company.industry_ai}' not found for re-evaluation.")
+            
+    except Exception as e:
+        logger.error(f"Wikipedia Re-evaluation Task Error: {e}", exc_info=True)
+    finally:
+        db.close()
+
+def run_metric_reextraction_task(company_id: int):
+    from .database import SessionLocal
+    db = SessionLocal()
+    try:
+        company = db.query(Company).filter(Company.id == company_id).first()
+        if not company: return
+
+        logger.info(f"Re-extracting metrics for {company.name} (Industry: {company.industry_ai})")
+        
+        industries = db.query(Industry).all()
+        industry = next((i for i in industries if i.name == company.industry_ai), None)
+        
+        if industry:
+            classifier.extract_metrics_for_industry(company, db, industry)
+            company.status = "ENRICHED"
+            db.commit()
+            logger.info(f"Metric re-extraction complete for {company.name}")
+        else:
+            logger.warning(f"Industry '{company.industry_ai}' not found for re-extraction.")
+            
+    except Exception as e:
+        logger.error(f"Metric Re-extraction Task Error: {e}", exc_info=True)
+    finally:
+        db.close()
+
 def run_discovery_task(company_id: int):
     from .database import SessionLocal
     db = SessionLocal()

company-explorer/backend/config.py

@@ -10,10 +10,10 @@ try:
     class Settings(BaseSettings):
         # App Info
         APP_NAME: str = "Company Explorer"
-        VERSION: str = "0.7.0"
+        VERSION: str = "0.6.4"
         DEBUG: bool = True
         
-        # Database (Store in App dir for simplicity)
+        # Database (FINAL CORRECT PATH for Docker Container)
         DATABASE_URL: str = "sqlite:////app/companies_v3_fixed_2.db"
         
         # API Keys
@@ -32,20 +32,25 @@ try:
 
 except ImportError:
     # Fallback wenn pydantic-settings nicht installiert ist
-    class Settings:
+    class FallbackSettings:
         APP_NAME = "Company Explorer"
-        VERSION = "0.2.1"
+        VERSION = "0.6.4"
         DEBUG = True
-        DATABASE_URL = "sqlite:////app/logs_debug/companies_debug.db"
+        DATABASE_URL = "sqlite:////app/companies_v3_fixed_2.db" # FINAL CORRECT PATH
         GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
         OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
         SERP_API_KEY = os.getenv("SERP_API_KEY")
         LOG_DIR = "/app/logs_debug"
     
-    settings = Settings()
+    settings = FallbackSettings()
 
 # Ensure Log Dir
+try:
     os.makedirs(settings.LOG_DIR, exist_ok=True)
+except FileExistsError:
+    pass
+except Exception as e:
+    logging.warning(f"Could not create log directory {settings.LOG_DIR}: {e}")
 
 # API Key Loading Helper (from file if env missing)
 def load_api_key_from_file(filename: str) -> Optional[str]:
@@ -54,10 +59,10 @@ def load_api_key_from_file(filename: str) -> Optional[str]:
             with open(filename, 'r') as f:
                 return f.read().strip()
     except Exception as e:
-        print(f"Could not load key from {filename}: {e}") # Print because logging might not be ready
+        logging.warning(f"Could not load key from {filename}: {e}")
     return None
 
-# Auto-load keys if not in env
+# Auto-load keys assuming the app runs in the Docker container's /app context
 if not settings.GEMINI_API_KEY:
     settings.GEMINI_API_KEY = load_api_key_from_file("/app/gemini_api_key.txt")
 

company-explorer/backend/lib/metric_parser.py

@@ -0,0 +1,135 @@
+import re
+import logging
+from typing import Optional, Union
+
+logger = logging.getLogger(__name__)
+
+class MetricParser:
+    """
+    Robust parser for extracting numeric values from text, specialized for
+    German formats and business metrics (Revenue, Employees).
+    Reconstructs legacy logic to handle thousands separators and year-suffixes.
+    """
+
+    @staticmethod
+    def extract_numeric_value(text: str, is_revenue: bool = False) -> Optional[float]:
+        """
+        Extracts a float value from a string, handling German locale and suffixes.
+
+        Args:
+            text: The raw text containing the number (e.g. "1.005 Mitarbeiter (2020)").
+            is_revenue: If True, prioritizes currency logic (e.g. handling "Mio").
+
+        Returns:
+            The parsed float value or None if no valid number found.
+        """
+        if not text:
+            return None
+        
+        # 1. Cleaning: Remove Citations [1], [note 2]
+        clean_text = re.sub(r'\[.*?\]', '', text)
+        
+        # 2. Cleaning: Remove Year/Date in parentheses to prevent "80 (2020)" -> 802020
+        # Matches (2020), (Stand 2021), (31.12.2022), etc.
+        # We replace them with space to avoid merging numbers.
+        clean_text = re.sub(r'\(\s*(?:Stand\s*|ab\s*)?(?:19|20)\d{2}.*?\)', ' ', clean_text)
+        
+        # 3. Identify Multipliers (Mio, Mrd)
+        multiplier = 1.0
+        lower_text = clean_text.lower().replace('.', '') # Remove dots for word matching (e.g. "Mio." -> "mio")
+        
+        if any(x in lower_text for x in ['mrd', 'milliarde', 'billion']): # German Billion = 10^12? Usually in business context here Mrd=10^9
+            multiplier = 1_000_000_000.0
+        elif any(x in lower_text for x in ['mio', 'million']):
+            multiplier = 1_000_000.0
+        
+        # 4. Extract the number candidate
+        # We look for the FIRST pattern that looks like a number.
+        # Must contain at least one digit.
+        # We iterate over matches to skip pure punctuation like "..."
+        matches = re.finditer(r'[\d\.,]+', clean_text)
+        
+        for match in matches:
+            candidate = match.group(0)
+            # Check if it actually has a digit
+            if not re.search(r'\d', candidate):
+                continue
+                
+            # Clean trailing/leading punctuation (e.g. "80." -> "80")
+            candidate = candidate.strip('.,')
+            if not candidate:
+                continue
+
+            try:
+                val = MetricParser._parse_german_number_string(candidate)
+                return val * multiplier
+            except Exception as e:
+                # If this candidate fails (e.g. "1.2.3.4"), try the next one?
+                # For now, let's assume the first valid-looking number sequence is the target.
+                # But "Wolfra ... 80" -> "..." skipped. "80" matched.
+                # "1.005 Mitarbeiter" -> "1.005" matched.
+                logger.debug(f"Failed to parse number string '{candidate}': {e}")
+                continue
+        
+        return None
+
+    @staticmethod
+    def _parse_german_number_string(s: str) -> float:
+        """
+        Parses a number string dealing with ambiguous separators.
+        Logic based on Lessons Learned:
+        - "1.005" -> 1005.0 (Dot followed by exactly 3 digits = Thousands)
+        - "1,5" -> 1.5 (Comma = Decimal)
+        - "1.234,56" -> 1234.56
+        """
+        # Count separators
+        dots = s.count('.')
+        commas = s.count(',')
+        
+        # Case 1: No separators
+        if dots == 0 and commas == 0:
+            return float(s)
+        
+        # Case 2: Mixed separators (Standard German: 1.000.000,00)
+        if dots > 0 and commas > 0:
+            # Assume . is thousands, , is decimal
+            s = s.replace('.', '').replace(',', '.')
+            return float(s)
+        
+        # Case 3: Only Dots
+        if dots > 0:
+            # Ambiguity: "1.005" (1005) vs "1.5" (1.5)
+            # Rule: If dot is followed by EXACTLY 3 digits (and it's the last dot or multiple dots), likely thousands.
+            # But "1.500" is 1500. "1.5" is 1.5.
+            
+            # Split by dot
+            parts = s.split('.')
+            
+            # Check if all parts AFTER the first one have exactly 3 digits
+            # E.g. 1.000.000 -> parts=["1", "000", "000"] -> OK -> Thousands
+            # 1.5 -> parts=["1", "5"] -> "5" len is 1 -> Decimal
+            
+            all_segments_are_3_digits = all(len(p) == 3 for p in parts[1:])
+            
+            if all_segments_are_3_digits:
+                # Treat as thousands separator
+                return float(s.replace('.', ''))
+            else:
+                # Treat as decimal (US format or simple float)
+                # But wait, German uses comma for decimal. 
+                # If we are parsing strict German text, "1.5" might be invalid or actually mean 1st May? 
+                # Usually in Wikipedia DE: "1.5 Mio" -> 1.5 Million.
+                # So if it's NOT 3 digits, it's likely a decimal point (US style or just typo/format variation).
+                # User Rule: "1.005" -> 1005.
+                return float(s) # Python handles 1.5 correctly
+        
+        # Case 4: Only Commas
+        if commas > 0:
+            # German Decimal: "1,5" -> 1.5
+            # Or English Thousands: "1,000" -> 1000?
+            # User context is German Wikipedia ("Mitarbeiter", "Umsatz").
+            # Assumption: Comma is ALWAYS decimal in this context, UNLESS followed by 3 digits AND likely English?
+            # Safer bet for German data: Comma is decimal.
+            return float(s.replace(',', '.'))
+            
+        return float(s)

company-explorer/backend/services/classification.py

@@ -1,6 +1,7 @@
 import json
 import logging
 import re
+from datetime import datetime
 from typing import Optional, Dict, Any, List
 
 from sqlalchemy.orm import Session
@@ -8,6 +9,7 @@ from sqlalchemy.orm import Session
 from backend.database import Company, Industry, RoboticsCategory, EnrichmentData
 from backend.lib.core_utils import call_gemini_flash, safe_eval_math, run_serp_search
 from backend.services.scraping import scrape_website_content
+from backend.lib.metric_parser import MetricParser
 
 logger = logging.getLogger(__name__)
 
@@ -32,7 +34,7 @@ class ClassificationService:
         
         if enrichment and enrichment.content:
             wiki_data = enrichment.content
-            return wiki_data.get('text')
+            return wiki_data.get('full_text')
         return None
 
     def _run_llm_classification_prompt(self, website_text: str, company_name: str, industry_definitions: List[Dict[str, str]]) -> Optional[str]:
@@ -75,27 +77,33 @@ class ClassificationService:
     def _run_llm_metric_extraction_prompt(self, text_content: str, search_term: str, industry_name: str) -> Optional[Dict[str, Any]]:
         """
         Uses LLM to extract the specific metric value from text.
+        Updated to look specifically for area (m²) even if not the primary search term.
         """
         prompt = r"""
-        Du bist ein Datenextraktions-Spezialist.
+        Du bist ein Datenextraktions-Spezialist für Unternehmens-Kennzahlen.
-        Analysiere den folgenden Text, um spezifische Metrik-Informationen zu extrahieren.
+        Analysiere den folgenden Text, um spezifische Werte zu extrahieren.
 
         --- KONTEXT ---
-        Unternehmen ist in der Branche: {industry_name}
+        Branche: {industry_name}
-        Gesuchter Wert (Rohdaten): '{search_term}'
+        Primär gesuchte Metrik: '{search_term}'
 
         --- TEXT ---
         {text_content_excerpt}
 
         --- AUFGABE ---
-        1. Finde den numerischen Wert für '{search_term}'.
+        1. Finde den numerischen Wert für die primäre Metrik '{search_term}'.
-        2. Versuche auch, eine explizit genannte Gesamtfläche in Quadratmetern (m²) zu finden, falls relevant und vorhanden.
+        2. EXTREM WICHTIG: Suche im gesamten Text nach einer Angabe zur Gesamtfläche, Nutzfläche, Grundstücksfläche oder Verkaufsfläche in Quadratmetern (m²). 
+           In Branchen wie Freizeitparks, Flughäfen oder Thermen ist dies oft separat im Fließtext versteckt (z.B. "Die Therme verfügt über eine Gesamtfläche von 4.000 m²").
+        3. Achte auf deutsche Zahlenformate (z.B. 1.005 für tausend-fünf).
+        4. Regel: Extrahiere IMMER den umgebenden Satz oder die Zeile in 'raw_text_segment'. Rate NIEMALS einen numerischen Wert, ohne den Beweis dafür zu liefern.
 
         Gib NUR ein JSON-Objekt zurück:
-        'raw_value': Der gefundene numerische Wert für '{search_term}' (als Zahl). null, falls nicht gefunden.
+        'raw_text_segment': Das Snippet für '{search_term}' (z.B. "ca. 1.500 Besucher (2020)"). MUSS IMMER AUSGEFÜLLT SEIN WENN EIN WERT GEFUNDEN WURDE.
-        'raw_unit': Die Einheit des raw_value (z.B. "Betten", "Stellplätze"). null, falls nicht gefunden.
+        'raw_value': Der numerische Wert für '{search_term}'. null, falls nicht gefunden.
-        'area_value': Ein gefundener numerischer Wert für eine Gesamtfläche in m² (als Zahl). null, falls nicht gefunden.
+        'raw_unit': Die Einheit (z.B. "Besucher", "Passagiere"). null, falls nicht gefunden.
-        'metric_name': Der Name der Metrik, nach der gesucht wurde (also '{search_term}').
+        'area_text_segment': Das Snippet, das eine Fläche (m²) erwähnt (z.B. "4.000 m² Gesamtfläche"). null, falls nicht gefunden.
+        'area_value': Der gefundene Wert der Fläche in m² (als Zahl). null, falls nicht gefunden.
+        'metric_name': '{search_term}'.
         """.format(
             industry_name=industry_name,
             search_term=search_term,
@@ -112,10 +120,20 @@ class ClassificationService:
     def _parse_standardization_logic(self, formula: str, raw_value: float) -> Optional[float]:
         if not formula or raw_value is None:
             return None
+            
+        # Clean formula: Replace 'wert'/'Value' and strip area units like m² or alphanumeric noise
+        # that Notion sync might bring in (e.g. "wert * 25m2" -> "wert * 25")
         formula_cleaned = formula.replace("wert", str(raw_value)).replace("Value", str(raw_value))
+        
+        # Remove common unit strings and non-math characters (except dots and parentheses)
+        formula_cleaned = re.sub(r'(?i)m[²2]', '', formula_cleaned)
+        formula_cleaned = re.sub(r'(?i)qm', '', formula_cleaned)
+        
+        # We leave the final safety check to safe_eval_math
         try:
             return safe_eval_math(formula_cleaned)
-        except:
+        except Exception as e:
+            logger.error(f"Failed to parse standardization logic '{formula}' with value {raw_value}: {e}")
             return None
 
     def _extract_and_calculate_metric_cascade(
@@ -147,18 +165,52 @@ class ClassificationService:
             logger.info(f"Checking {source_name} for '{search_term}' for {company.name}")
             try:
                 content = content_loader()
+                print(f"--- DEBUG: Content length for {source_name}: {len(content) if content else 0}")
                 if not content: continue
                 
                 llm_result = self._run_llm_metric_extraction_prompt(content, search_term, industry_name)
-                if llm_result and (llm_result.get("raw_value") is not None or llm_result.get("area_value") is not None):
+                print(f"--- DEBUG: LLM Result for {source_name}: {llm_result}")
-                    results["calculated_metric_value"] = llm_result.get("raw_value")
+                
+                is_revenue = "umsatz" in search_term.lower() or "revenue" in search_term.lower()
+                
+                # Hybrid Extraction Logic:
+                # 1. Try to parse from the text segment using our robust Python parser (prioritized for German formats)
+                parsed_value = None
+                if llm_result and llm_result.get("raw_text_segment"):
+                    parsed_value = MetricParser.extract_numeric_value(llm_result["raw_text_segment"], is_revenue=is_revenue)
+                    if parsed_value is not None:
+                        logger.info(f"Successfully parsed '{llm_result['raw_text_segment']}' to {parsed_value} using MetricParser.")
+
+                # 2. Fallback to LLM's raw_value if parser failed or no segment found
+                # NEW: Also run MetricParser on the raw_value if it's a string, to catch errors like "802020"
+                final_value = parsed_value
+                if final_value is None and llm_result.get("raw_value"):
+                    final_value = MetricParser.extract_numeric_value(str(llm_result["raw_value"]), is_revenue=is_revenue)
+                    if final_value is not None:
+                        logger.info(f"Successfully cleaned LLM raw_value '{llm_result['raw_value']}' to {final_value}")
+                
+                # Ultimate fallback to original raw_value if still None (though parser is very robust)
+                if final_value is None:
+                    final_value = llm_result.get("raw_value")
+
+                if llm_result and (final_value is not None or llm_result.get("area_value") is not None or llm_result.get("area_text_segment")):
+                    results["calculated_metric_value"] = final_value
                     results["calculated_metric_unit"] = llm_result.get("raw_unit")
                     results["metric_source"] = source_name
 
-                    if llm_result.get("area_value") is not None:
+                    # 3. Area Extraction Logic (Cascading)
-                        results["standardized_metric_value"] = llm_result.get("area_value")
+                    area_val = llm_result.get("area_value")
-                    elif llm_result.get("raw_value") is not None and standardization_logic:
+                    # Try to refine area_value if a segment exists
-                        results["standardized_metric_value"] = self._parse_standardization_logic(standardization_logic, llm_result["raw_value"])
+                    if llm_result.get("area_text_segment"):
+                        refined_area = MetricParser.extract_numeric_value(llm_result["area_text_segment"], is_revenue=False)
+                        if refined_area is not None:
+                            area_val = refined_area
+                            logger.info(f"Refined area to {area_val} from segment '{llm_result['area_text_segment']}'")
+
+                    if area_val is not None:
+                        results["standardized_metric_value"] = area_val
+                    elif final_value is not None and standardization_logic:
+                        results["standardized_metric_value"] = self._parse_standardization_logic(standardization_logic, final_value)
                     
                     return results
             except Exception as e:
@@ -166,32 +218,20 @@ class ClassificationService:
 
         return results
 
-    def classify_company_potential(self, company: Company, db: Session) -> Company:
+    def extract_metrics_for_industry(self, company: Company, db: Session, industry: Industry) -> Company:
-        logger.info(f"Starting classification for {company.name}")
+        """
+        Extracts and calculates metrics for a given industry.
+        Splits out from classify_company_potential to allow manual overrides.
+        """
+        if not industry or not industry.scraper_search_term:
+            logger.warning(f"No metric configuration for industry '{industry.name if industry else 'None'}'")
+            return company
 
-        # 1. Load Industries
-        industries = self._load_industry_definitions(db)
-        industry_defs = [{"name": i.name, "description": i.description} for i in industries]
-
-        # 2. Industry Classification
-        website_content = scrape_website_content(company.website)
-        if website_content:
-            industry_name = self._run_llm_classification_prompt(website_content, company.name, industry_defs)
-            company.industry_ai = industry_name if industry_name in [i.name for i in industries] else "Others"
-        else:
-            company.industry_ai = "Others"
-
-        db.commit()
-
-        # 3. Metric Extraction
-        if company.industry_ai != "Others":
-            industry = next((i for i in industries if i.name == company.industry_ai), None)
-            if industry and industry.scraper_search_term:
         # Derive standardized unit
         std_unit = "m²" if "m²" in (industry.standardization_logic or "") else "Einheiten"
         
         metrics = self._extract_and_calculate_metric_cascade(
-                    db, company, company.industry_ai, industry.scraper_search_term, industry.standardization_logic, std_unit
+            db, company, industry.name, industry.scraper_search_term, industry.standardization_logic, std_unit
         )
         
         company.calculated_metric_name = metrics["calculated_metric_name"]
@@ -201,6 +241,113 @@ class ClassificationService:
         company.standardized_metric_unit = metrics["standardized_metric_unit"]
         company.metric_source = metrics["metric_source"]
         
+        # Keep track of refinement
         company.last_classification_at = datetime.utcnow()
         db.commit()
         return company
+
+    def reevaluate_wikipedia_metric(self, company: Company, db: Session, industry: Industry) -> Company:
+        """
+        Runs the metric extraction cascade for ONLY the Wikipedia source.
+        """
+        logger.info(f"Starting Wikipedia re-evaluation for '{company.name}'")
+        if not industry or not industry.scraper_search_term:
+            logger.warning(f"Cannot re-evaluate: No metric configuration for industry '{industry.name}'")
+            return company
+
+        search_term = industry.scraper_search_term
+        content = self._get_wikipedia_content(db, company.id)
+
+        if not content:
+            logger.warning("No Wikipedia content found to re-evaluate.")
+            return company
+
+        try:
+            llm_result = self._run_llm_metric_extraction_prompt(content, search_term, industry.name)
+            if not llm_result:
+                raise ValueError("LLM metric extraction returned empty result.")
+
+            is_revenue = "umsatz" in search_term.lower() or "revenue" in search_term.lower()
+            
+            # Hybrid Extraction Logic (same as in cascade)
+            parsed_value = None
+            if llm_result.get("raw_text_segment"):
+                parsed_value = MetricParser.extract_numeric_value(llm_result["raw_text_segment"], is_revenue=is_revenue)
+                if parsed_value is not None:
+                    logger.info(f"Successfully parsed '{llm_result['raw_text_segment']}' to {parsed_value} using MetricParser.")
+
+            final_value = parsed_value
+            if final_value is None and llm_result.get("raw_value"):
+                final_value = MetricParser.extract_numeric_value(str(llm_result["raw_value"]), is_revenue=is_revenue)
+                if final_value is not None:
+                    logger.info(f"Successfully cleaned LLM raw_value '{llm_result['raw_value']}' to {final_value}")
+
+            if final_value is None:
+                final_value = llm_result.get("raw_value")
+
+            # Update company metrics if a value was found
+            if final_value is not None:
+                company.calculated_metric_name = search_term
+                company.calculated_metric_value = final_value
+                company.calculated_metric_unit = llm_result.get("raw_unit")
+                company.metric_source = "wikipedia_reevaluated"
+                
+                # Handle standardization
+                std_unit = "m²" if "m²" in (industry.standardization_logic or "") else "Einheiten"
+                company.standardized_metric_unit = std_unit
+                
+                area_val = llm_result.get("area_value")
+                if llm_result.get("area_text_segment"):
+                    refined_area = MetricParser.extract_numeric_value(llm_result["area_text_segment"], is_revenue=False)
+                    if refined_area is not None:
+                        area_val = refined_area
+                
+                if area_val is not None:
+                    company.standardized_metric_value = area_val
+                elif industry.standardization_logic:
+                    company.standardized_metric_value = self._parse_standardization_logic(industry.standardization_logic, final_value)
+                else:
+                    company.standardized_metric_value = None
+
+                company.last_classification_at = datetime.utcnow()
+                db.commit()
+                logger.info(f"Successfully re-evaluated and updated metrics for {company.name} from Wikipedia.")
+            else:
+                logger.warning(f"Re-evaluation for {company.name} did not yield a metric value.")
+
+        except Exception as e:
+            logger.error(f"Error during Wikipedia re-evaluation for {company.name}: {e}")
+
+        return company
+
+    def classify_company_potential(self, company: Company, db: Session) -> Company:
+        logger.info(f"Starting complete classification for {company.name}")
+
+        # 1. Load Industries
+        industries = self._load_industry_definitions(db)
+        industry_defs = [{"name": i.name, "description": i.description} for i in industries]
+
+        # 2. Industry Classification (Website-based)
+        # STRENG: Nur wenn Branche noch auf "Others" steht oder neu ist, darf die KI klassifizieren
+        valid_industry_names = [i.name for i in industries]
+        if company.industry_ai and company.industry_ai != "Others" and company.industry_ai in valid_industry_names:
+            logger.info(f"KEEPING manual/existing industry '{company.industry_ai}' for {company.name}")
+        else:
+            website_content = scrape_website_content(company.website)
+            if website_content:
+                industry_name = self._run_llm_classification_prompt(website_content, company.name, industry_defs)
+                company.industry_ai = industry_name if industry_name in valid_industry_names else "Others"
+                logger.info(f"AI CLASSIFIED {company.name} as '{company.industry_ai}'")
+            else:
+                company.industry_ai = "Others"
+                logger.warning(f"No website content for {company.name}, setting industry to Others")
+
+        db.commit()
+
+        # 3. Metric Extraction
+        if company.industry_ai != "Others":
+            industry = next((i for i in industries if i.name == company.industry_ai), None)
+            if industry:
+                self.extract_metrics_for_industry(company, db, industry)
+
+        return company

company-explorer/frontend/src/App.tsx

@@ -16,6 +16,7 @@ function App() {
   const [isSettingsOpen, setIsSettingsOpen] = useState(false)
   const [selectedCompanyId, setSelectedCompanyId] = useState<number | null>(null)
   const [selectedContactId, setSelectedContactId] = useState<number | null>(null)
+  const [backendVersion, setBackendVersion] = useState('');
 
   // Navigation State
   const [view, setView] = useState<'companies' | 'contacts'>('companies')
@@ -37,6 +38,13 @@ function App() {
     localStorage.setItem('theme', theme)
   }, [theme])
 
+  useEffect(() => {
+    fetch(`${API_BASE}/health`)
+      .then(res => res.json())
+      .then(data => setBackendVersion(data.version || ''))
+      .catch(() => setBackendVersion('N/A'))
+  }, [])
+
   const toggleTheme = () => setTheme(prev => prev === 'dark' ? 'light' : 'dark')
 
   const handleCompanySelect = (id: number) => {
@@ -80,7 +88,7 @@ function App() {
             </div>
             <div>
               <h1 className="text-xl font-bold text-slate-900 dark:text-white tracking-tight">Company Explorer</h1>
-              <p className="text-xs text-blue-600 dark:text-blue-400 font-medium">ROBOTICS EDITION <span className="text-slate-500 dark:text-slate-600 ml-2">v0.6.1</span></p>
+              <p className="text-xs text-blue-600 dark:text-blue-400 font-medium">ROBOTICS EDITION {backendVersion && <span className="text-slate-500 dark:text-slate-600 ml-2">v{backendVersion}</span>}</p>
             </div>
           </div>
 

company-explorer/frontend/src/components/Inspector.tsx

@@ -1,6 +1,6 @@
 import { useEffect, useState } from 'react'
 import axios from 'axios'
-import { X, ExternalLink, Bot, Briefcase, Calendar, Globe, Users, DollarSign, MapPin, Tag, RefreshCw as RefreshCwIcon, Search as SearchIcon, Pencil, Check, Download, Clock, Lock, Unlock } from 'lucide-react'
+import { X, ExternalLink, Bot, Briefcase, Calendar, Globe, Users, DollarSign, MapPin, Tag, RefreshCw as RefreshCwIcon, Search as SearchIcon, Pencil, Check, Download, Clock, Lock, Unlock, Calculator, Ruler, Database, Trash2 } from 'lucide-react'
 import clsx from 'clsx'
 import { ContactsManager, Contact } from './ContactsManager'
 
@@ -35,6 +35,14 @@ type CompanyDetail = {
   signals: Signal[]
   enrichment_data: EnrichmentData[]
   contacts?: Contact[]
+  // NEU v0.7.0: Quantitative Metrics
+  calculated_metric_name: string | null
+  calculated_metric_value: number | null
+  calculated_metric_unit: string | null
+  standardized_metric_value: number | null
+  standardized_metric_unit: string | null
+  metric_source: string | null
+  metric_source_url: string | null
 }
 
 export function Inspector({ companyId, initialContactId, onClose, apiBase }: InspectorProps) {
@@ -71,6 +79,11 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
   const [isEditingImpressum, setIsEditingImpressum] = useState(false)
   const [impressumUrlInput, setImpressumUrlInput] = useState("")
 
+  // NEU: Industry Override
+  const [industries, setIndustries] = useState<any[]>([])
+  const [isEditingIndustry, setIsEditingIndustry] = useState(false)
+  const [industryInput, setIndustryInput] = useState("")
+
   const fetchData = (silent = false) => {
     if (!companyId) return
     if (!silent) setLoading(true)
@@ -78,6 +91,7 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
     axios.get(`${apiBase}/companies/${companyId}`)
       .then(res => {
         const newData = res.data
+        console.log("FETCHED COMPANY DATA:", newData) // DEBUG: Log raw data from API
         setData(newData)
 
         // Auto-stop processing if status changes to ENRICHED or we see data
@@ -100,7 +114,13 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
     setIsEditingWiki(false)
     setIsEditingWebsite(false)
     setIsEditingImpressum(false)
+    setIsEditingIndustry(false)
     setIsProcessing(false) // Reset on ID change
+
+    // Load industries for dropdown
+    axios.get(`${apiBase}/industries`)
+      .then(res => setIndustries(res.data))
+      .catch(console.error)
   }, [companyId])
 
   const handleDiscover = async () => {
@@ -204,6 +224,54 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
     }
   }
 
+  const handleIndustryOverride = async () => {
+    if (!companyId) return
+    setIsProcessing(true)
+    try {
+      await axios.put(`${apiBase}/companies/${companyId}/industry`, { industry_ai: industryInput })
+      setIsEditingIndustry(false)
+      fetchData()
+    } catch (e) {
+      alert("Industry update failed")
+      console.error(e)
+    } finally {
+      setIsProcessing(false)
+    }
+  }
+
+  const handleReevaluateWikipedia = async () => {
+    if (!companyId) return
+    setIsProcessing(true)
+    try {
+      await axios.post(`${apiBase}/companies/${companyId}/reevaluate-wikipedia`)
+      // Polling effect will handle the rest
+    } catch (e) {
+      console.error(e)
+      setIsProcessing(false) // Stop on direct error
+    }
+  }
+
+  const handleDelete = async () => {
+    console.log("[Inspector] Delete requested for ID:", companyId)
+    if (!companyId) return;
+
+    if (!window.confirm(`Are you sure you want to delete "${data?.name}"? This action cannot be undone.`)) {
+      console.log("[Inspector] Delete cancelled by user")
+      return
+    }
+
+    try {
+      console.log("[Inspector] Sending DELETE request...")
+      await axios.delete(`${apiBase}/companies/${companyId}`)
+      console.log("[Inspector] Delete successful")
+      onClose() // Close the inspector on success
+      window.location.reload() // Force reload to show updated list
+    } catch (e: any) {
+      console.error("[Inspector] Delete failed:", e)
+      alert("Failed to delete company: " + (e.response?.data?.detail || e.message))
+    }
+  }
+
   const handleLockToggle = async (sourceType: string, currentLockStatus: boolean) => {
     if (!companyId) return
     try {
@@ -265,6 +333,13 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
             <div className="flex justify-between items-start mb-4">
               <h2 className="text-xl font-bold text-slate-900 dark:text-white leading-tight">{data.name}</h2>
               <div className="flex items-center gap-2">
+                <button
+                  onClick={handleDelete}
+                  className="p-1.5 text-slate-500 hover:text-red-600 dark:hover:text-red-500 transition-colors"
+                  title="Delete Company"
+                >
+                  <Trash2 className="h-4 w-4" />
+                </button>
                 <button
                   onClick={handleExport}
                   className="p-1.5 text-slate-500 hover:text-blue-600 dark:hover:text-blue-400 transition-colors"
@@ -287,17 +362,22 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
 
             <div className="flex flex-wrap gap-2 text-sm items-center">
               {!isEditingWebsite ? (
-                      <div className="flex items-center gap-2">
+                <div className="flex items-center gap-2 group">
                   {data.website && data.website !== "k.A." ? (
-                            <a href={data.website} target="_blank" className="flex items-center gap-1 text-blue-600 dark:text-blue-400 hover:text-blue-800 dark:hover:text-blue-300 transition-colors font-medium">
+                    <a
-                            <ExternalLink className="h-3 w-3" /> {new URL(data.website).hostname.replace('www.', '')}
+                      href={data.website.startsWith('http') ? data.website : `https://${data.website}`}
+                      target="_blank"
+                      rel="noopener noreferrer"
+                      className="flex items-center gap-1 text-blue-600 dark:text-blue-400 hover:text-blue-800 dark:hover:text-blue-300 transition-colors font-medium"
+                    >
+                      <Globe className="h-3.5 w-3.5" /> {new URL(data.website.startsWith('http') ? data.website : `https://${data.website}`).hostname.replace('www.', '')}
                     </a>
                   ) : (
-                            <span className="text-slate-500 italic">No website</span>
+                    <span className="text-slate-500 italic text-xs">No website</span>
                   )}
                   <button
                     onClick={() => { setWebsiteInput(data.website && data.website !== "k.A." ? data.website : ""); setIsEditingWebsite(true); }}
-                            className="p-1 text-slate-400 hover:text-slate-900 dark:hover:text-white transition-colors"
+                    className="p-1 text-slate-400 hover:text-slate-900 dark:hover:text-white transition-colors opacity-0 group-hover:opacity-100"
                     title="Edit Website URL"
                   >
                     <Pencil className="h-3 w-3" />
@@ -310,7 +390,7 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
                     value={websiteInput}
                     onChange={e => setWebsiteInput(e.target.value)}
                     placeholder="https://..."
-                            className="bg-white dark:bg-slate-800 border border-slate-300 dark:border-slate-700 rounded px-2 py-0.5 text-xs text-slate-900 dark:text-white focus:ring-1 focus:ring-blue-500 outline-none w-48"
+                    className="bg-white dark:bg-slate-800 border border-slate-300 dark:border-slate-700 rounded px-2 py-0.5 text-[10px] text-slate-900 dark:text-white focus:ring-1 focus:ring-blue-500 outline-none w-48"
                     autoFocus
                   />
                   <button
@@ -327,20 +407,6 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
                   </button>
                 </div>
               )}
-    
-                  {data.industry_ai && (
-                    <span className="flex items-center gap-1 px-2 py-0.5 bg-slate-100 dark:bg-slate-800 text-slate-700 dark:text-slate-300 rounded border border-slate-200 dark:border-slate-700">
-                      <Briefcase className="h-3 w-3" /> {data.industry_ai}
-                    </span>
-                  )}
-                  <span className={clsx(
-                    "px-2 py-0.5 rounded text-[10px] font-bold uppercase tracking-wider",
-                    data.status === 'ENRICHED' ? "bg-green-100 dark:bg-green-900/40 text-green-700 dark:text-green-400 border border-green-200 dark:border-green-800/50" : 
-                    data.status === 'DISCOVERED' ? "bg-blue-100 dark:bg-blue-900/40 text-blue-700 dark:text-blue-400 border border-blue-200 dark:border-blue-800/50" :
-                    "bg-slate-100 dark:bg-slate-800 text-slate-600 dark:text-slate-400 border border-slate-200 dark:border-slate-700"
-                  )}>
-                    {data.status}
-                  </span>
             </div>
 
             {/* Tab Navigation */}
@@ -501,6 +567,75 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
 
 
 
+                {/* Core Classification */}
+                <div className="bg-blue-50/50 dark:bg-blue-900/10 rounded-xl p-5 border border-blue-100 dark:border-blue-900/50 mb-6">
+                  <div className="grid grid-cols-2 gap-6">
+                    <div>
+                      <div className="text-[10px] text-blue-600 dark:text-blue-400 uppercase font-bold tracking-tight mb-2">Industry Focus</div>
+                      {!isEditingIndustry ? (
+                        <div className="flex items-center gap-3">
+                          <div className="p-2 bg-white dark:bg-slate-800 rounded-lg shadow-sm">
+                            <Briefcase className="h-5 w-5 text-blue-600 dark:text-blue-400" />
+                          </div>
+                          <div>
+                            <div className="text-sm font-semibold text-slate-900 dark:text-white">{data.industry_ai || "Not Classified"}</div>
+                            <button
+                              onClick={() => { setIndustryInput(data.industry_ai || "Others"); setIsEditingIndustry(true); }}
+                              className="text-xs text-blue-600 dark:text-blue-400 hover:underline"
+                            >
+                              Change Industry & Re-Extract
+                            </button>
+                          </div>
+                        </div>
+                      ) : (
+                        <div className="space-y-2">
+                          <select
+                            value={industryInput}
+                            onChange={e => setIndustryInput(e.target.value)}
+                            className="w-full bg-white dark:bg-slate-800 border border-slate-300 dark:border-slate-700 rounded px-2 py-1.5 text-sm text-slate-900 dark:text-white focus:ring-1 focus:ring-blue-500 outline-none"
+                            autoFocus
+                          >
+                            <option value="Others">Others</option>
+                            {industries.map(ind => (
+                              <option key={ind.id} value={ind.name}>{ind.name}</option>
+                            ))}
+                          </select>
+                          <div className="flex gap-2">
+                            <button
+                              onClick={handleIndustryOverride}
+                              className="flex-1 px-3 py-1.5 bg-blue-600 text-white rounded text-xs font-medium hover:bg-blue-700 transition-colors flex items-center justify-center gap-2"
+                            >
+                              <Check className="h-3.5 w-3.5" /> Save & Re-Extract
+                            </button>
+                            <button
+                              onClick={() => setIsEditingIndustry(false)}
+                              className="px-3 py-1.5 bg-slate-200 dark:bg-slate-700 text-slate-700 dark:text-slate-300 rounded text-xs font-medium hover:bg-slate-300 dark:hover:bg-slate-600 transition-colors"
+                            >
+                              <X className="h-3.5 w-3.5" />
+                            </button>
+                          </div>
+                        </div>
+                      )}
+                    </div>
+                    <div>
+                      <div className="text-[10px] text-slate-500 uppercase font-bold tracking-tight mb-2">Analysis Status</div>
+                      <div className="flex items-center gap-3">
+                        <div className="p-2 bg-white dark:bg-slate-800 rounded-lg shadow-sm">
+                          <Bot className="h-5 w-5 text-slate-500" />
+                        </div>
+                        <div className={clsx(
+                          "px-3 py-1 rounded-full text-xs font-bold",
+                          data.status === 'ENRICHED' ? "bg-green-100 text-green-700 border border-green-200" :
+                            data.status === 'DISCOVERED' ? "bg-blue-100 text-blue-700 border border-blue-200" :
+                              "bg-slate-100 text-slate-600 border border-slate-200"
+                        )}>
+                          {data.status}
+                        </div>
+                      </div>
+                    </div>
+                  </div>
+                </div>
+
                 {/* AI Analysis Dossier */}
                 {aiAnalysis && (
                   <div className="space-y-4">
@@ -582,6 +717,16 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
 
                       )}
 
+                      {/* Re-evaluate Button */}
+                      <button
+                        onClick={handleReevaluateWikipedia}
+                        disabled={isProcessing}
+                        className="p-1 text-slate-400 hover:text-blue-600 dark:hover:text-blue-400 transition-colors disabled:opacity-50"
+                        title="Re-run metric extraction from Wikipedia text"
+                      >
+                        <RefreshCwIcon className={clsx("h-3.5 w-3.5", isProcessing && "animate-spin")} />
+                      </button>
+
 
 
                       {!isEditingWiki ? (
@@ -723,40 +868,70 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
                   ) : null}
                 </div>
 
-                    {/* Robotics Scorecard */}
+                {/* Quantitative Potential Analysis (v0.7.0) */}
                 <div>
                   <h3 className="text-sm font-semibold text-slate-500 dark:text-slate-400 uppercase tracking-wider mb-3 flex items-center gap-2">
-                        <Bot className="h-4 w-4" /> Robotics Potential
+                    <Bot className="h-4 w-4" /> Quantitative Potential
                   </h3>
 
-                      <div className="grid grid-cols-2 gap-4">
+                  {data.calculated_metric_value != null || data.standardized_metric_value != null ? (
-                        {['cleaning', 'transport', 'security', 'service'].map(type => {
+                    <div className="bg-slate-50 dark:bg-slate-950 rounded-lg p-4 border border-slate-200 dark:border-slate-800 space-y-4">
-                          const sig = data.signals.find(s => s.signal_type.includes(type))
+                      {/* Calculated Metric */}
-                          const score = sig ? sig.confidence : 0
+                      {data.calculated_metric_value != null && (
+                        <div className="flex items-start gap-3">
+                          <div className="p-2 bg-white dark:bg-slate-800 rounded-lg text-blue-500 mt-1">
+                            <Calculator className="h-4 w-4" />
+                          </div>
+                          <div>
+                            <div className="text-[10px] text-slate-500 uppercase font-bold tracking-tight">{data.calculated_metric_name || 'Calculated Metric'}</div>
+                            <div className="text-xl text-slate-900 dark:text-white font-bold">
+                              {data.calculated_metric_value.toLocaleString('de-DE')}
+                              <span className="text-sm font-medium text-slate-500 ml-1">{data.calculated_metric_unit}</span>
+                            </div>
+                          </div>
+                        </div>
+                      )}
 
-                          return (
+                      {/* Standardized Metric */}
-                            <div key={type} className="bg-slate-50 dark:bg-slate-800/50 p-3 rounded-lg border border-slate-200 dark:border-slate-700">
+                      {data.standardized_metric_value != null && (
-                              <div className="flex justify-between mb-1">
+                        <div className="flex items-start gap-3 pt-4 border-t border-slate-200 dark:border-slate-800">
-                                <span className="text-sm text-slate-700 dark:text-slate-300 capitalize">{type}</span>
+                          <div className="p-2 bg-white dark:bg-slate-800 rounded-lg text-green-500 mt-1">
-                                <span className={clsx("text-sm font-bold", score > 70 ? "text-green-600 dark:text-green-400" : score > 30 ? "text-yellow-600 dark:text-yellow-400" : "text-slate-500")}>
+                            <Ruler className="h-4 w-4" />
-                                  {score}%
-                                </span>
                           </div>
-                              <div className="w-full bg-slate-200 dark:bg-slate-700 h-1.5 rounded-full overflow-hidden">
+                          <div>
-                                <div 
+                            <div className="text-[10px] text-slate-500 uppercase font-bold tracking-tight">Standardized Potential ({data.standardized_metric_unit})</div>
-                                  className={clsx("h-full rounded-full", score > 70 ? "bg-green-500" : score > 30 ? "bg-yellow-500" : "bg-slate-500")} 
+                            <div className="text-xl text-green-600 dark:text-green-400 font-bold">
-                                  style={{ width: `${score}%` }}
+                              {data.standardized_metric_value.toLocaleString('de-DE')}
-                                />
+                              <span className="text-sm font-medium text-slate-500 ml-1">{data.standardized_metric_unit}</span>
                             </div>
-                              {sig?.proof_text && (
+                            <p className="text-xs text-slate-500 mt-1">Comparable value for potential analysis.</p>
-                                <p className="text-xs text-slate-500 dark:text-slate-500 mt-2 line-clamp-2" title={sig.proof_text}>
+                          </div>
-                                  "{sig.proof_text}"
+                        </div>
-                                </p>
+                      )}
+
+                      {/* Source */}
+                      {data.metric_source && (
+                        <div className="flex justify-end items-center gap-1 text-[10px] text-slate-500 pt-2 border-t border-slate-200 dark:border-slate-800">
+                          <Database className="h-3 w-3" />
+                          <span>Source:</span>
+                          {data.metric_source_url ? (
+                            <a href={data.metric_source_url} target="_blank" rel="noopener noreferrer" className="font-medium text-blue-600 dark:text-blue-400 capitalize hover:underline">
+                              {data.metric_source}
+                            </a>
+                          ) : (
+                            <span className="font-medium text-slate-600 dark:text-slate-400 capitalize">{data.metric_source}</span>
                           )}
                         </div>
-                          )
+                      )}
-                        })}
+
                     </div>
+                  ) : (
+                    <div className="p-4 rounded-xl border border-dashed border-slate-200 dark:border-slate-800 text-center text-slate-500 dark:text-slate-600">
+                      <Bot className="h-5 w-5 mx-auto mb-2 opacity-20" />
+                      <p className="text-xs">No quantitative data calculated yet.</p>
+                      <p className="text-xs mt-1">Run "Analyze Potential" to extract metrics.</p>
+                    </div>
+                  )}
                 </div>
 
                 {/* Meta Info */}