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.
This commit is contained in:
@@ -94,7 +94,39 @@ Wir kapseln das neue Projekt vollständig ab ("Fork & Clean").
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## 7. Historie & Fixes (Jan 2026)
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* **[STABILITY] v0.7.2: Robust Metric Parsing (Jan 23, 2026)**
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* **Legacy Logic Restored:** Re-implemented the robust, regex-based number parsing logic (formerly in legacy helpers) as `MetricParser`.
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* **German Formats:** Correctly handles "1.000" (thousands) vs "1,5" (decimal) and mixed formats.
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* **Citation Cleaning:** Filters out Wikipedia citations like `[3]` and years in parentheses (e.g. "80 (2020)" -> 80).
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* **Hybrid Extraction:** The ClassificationService now asks the LLM for the *text segment* and parses the number deterministically, fixing the "1.005 -> 1" LLM hallucination.
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* **[ONGOING] v0.6.4: Wolfra Metric Extraction Bug (Jan 23, 2026)**
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* **Problem:** Mitarbeiterzahl für "Wolfra Bayrische Natursaft Kelterei GmbH" wird fälschlicherweise als "802020" anstatt "80" ausgelesen.
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* **Implementierte Maßnahmen:**
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* "Wiki-Reevaluate-Button" im Frontend integriert (POST `/api/companies/{company_id}/reevaluate-wikipedia`).
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* `reevaluate_wikipedia_metric`-Funktion im `ClassificationService` erstellt.
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* Prompt für `_run_llm_metric_extraction_prompt` geschärft, um LLM zur Rückgabe von `raw_text_segment` zu zwingen.
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* Datenbankpfad-Konfiguration in `company-explorer/backend/config.py` mehrfach korrigiert, um `unable to open database file` Fehler zu beheben.
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* Fehler in `ClassificationService._get_wikipedia_content` behoben (`wiki_data.get('text')` zu `wiki_data.get('full_text')` geändert).
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* **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.
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* **[STABILITY] v0.7.1: AI Robustness & UI Fixes (Jan 21, 2026)**
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* **SDK Stabilität:** Umstellung auf `gemini-2.0-flash` im Legacy-SDK zur Behebung von `404 Not Found` Fehlern bei `1.5-flash-latest`.
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* **API-Key Management:** Implementierung eines robusten Ladevorgangs für den Google API Key (Fallback von Environment-Variable auf lokale Datei `/app/gemini_api_key.txt`).
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* **Classification Prompt:** Schärfung des Prompts auf "Best-Fit"-Entscheidungen, um zu konservative "Others"-Einstufungen bei klaren Kandidaten (z.B. Thermen) zu vermeiden.
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* **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.
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* **Scraping:** Wiederherstellung der Stabilität durch Entfernung fehlerhafter `trafilatura` Abhängigkeiten; Nutzung von `BeautifulSoup` als robustem Standard.
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* **[MAJOR] v0.7.0: Quantitative Potential Analysis (Jan 20, 2026)**
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...
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...
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## 11. Lessons Learned (Retrospektive Jan 21, 2026)
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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.
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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.
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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.
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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.
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* **Zweistufige Analyse:**
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1. **Strict Classification:** Ordnet Firmen einer Notion-Branche zu (oder "Others").
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2. **Metric Cascade:** Sucht gezielt nach der branchenspezifischen Metrik ("Scraper Search Term").
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@@ -58,6 +58,9 @@ class AnalysisRequest(BaseModel):
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company_id: int
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force_scrape: bool = False
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class IndustryUpdateModel(BaseModel):
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industry_ai: str
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# --- Events ---
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@app.on_event("startup")
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def on_startup():
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@@ -137,6 +140,137 @@ def analyze_company(req: AnalysisRequest, background_tasks: BackgroundTasks, db:
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background_tasks.add_task(run_analysis_task, company.id)
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return {"status": "queued"}
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@app.put("/api/companies/{company_id}/industry")
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def update_company_industry(
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company_id: int,
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data: IndustryUpdateModel,
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background_tasks: BackgroundTasks,
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db: Session = Depends(get_db)
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):
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company = db.query(Company).filter(Company.id == company_id).first()
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if not company:
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raise HTTPException(404, detail="Company not found")
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# 1. Update Industry
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company.industry_ai = data.industry_ai
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company.updated_at = datetime.utcnow()
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db.commit()
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# 2. Trigger Metric Re-extraction in Background
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background_tasks.add_task(run_metric_reextraction_task, company.id)
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return {"status": "updated", "industry_ai": company.industry_ai}
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@app.post("/api/companies/{company_id}/reevaluate-wikipedia")
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def reevaluate_wikipedia(company_id: int, background_tasks: BackgroundTasks, db: Session = Depends(get_db)):
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company = db.query(Company).filter(Company.id == company_id).first()
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if not company:
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raise HTTPException(404, detail="Company not found")
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background_tasks.add_task(run_wikipedia_reevaluation_task, company.id)
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return {"status": "queued"}
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@app.delete("/api/companies/{company_id}")
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def delete_company(company_id: int, db: Session = Depends(get_db)):
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company = db.query(Company).filter(Company.id == company_id).first()
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if not company:
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raise HTTPException(404, detail="Company not found")
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# Delete related data first (Cascade might handle this but being explicit is safer)
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db.query(EnrichmentData).filter(EnrichmentData.company_id == company_id).delete()
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db.query(Signal).filter(Signal.company_id == company_id).delete()
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db.query(Contact).filter(Contact.company_id == company_id).delete()
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db.delete(company)
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db.commit()
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return {"status": "deleted"}
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@app.post("/api/companies/{company_id}/override/website")
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def override_website(company_id: int, url: str, db: Session = Depends(get_db)):
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company = db.query(Company).filter(Company.id == company_id).first()
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if not company:
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raise HTTPException(404, detail="Company not found")
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company.website = url
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company.updated_at = datetime.utcnow()
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db.commit()
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return {"status": "updated", "website": company.website}
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@app.post("/api/companies/{company_id}/override/impressum")
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def override_impressum(company_id: int, url: str, background_tasks: BackgroundTasks, db: Session = Depends(get_db)):
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company = db.query(Company).filter(Company.id == company_id).first()
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if not company:
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raise HTTPException(404, detail="Company not found")
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# Create or update manual impressum lock
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existing = db.query(EnrichmentData).filter(
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EnrichmentData.company_id == company_id,
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EnrichmentData.source_type == "impressum_override"
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).first()
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if not existing:
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db.add(EnrichmentData(
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company_id=company_id,
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source_type="impressum_override",
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content={"url": url},
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is_locked=True
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))
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else:
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existing.content = {"url": url}
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existing.is_locked = True
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db.commit()
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return {"status": "updated"}
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def run_wikipedia_reevaluation_task(company_id: int):
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from .database import SessionLocal
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db = SessionLocal()
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try:
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company = db.query(Company).filter(Company.id == company_id).first()
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if not company: return
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logger.info(f"Re-evaluating Wikipedia metric for {company.name} (Industry: {company.industry_ai})")
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industry = db.query(Industry).filter(Industry.name == company.industry_ai).first()
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if industry:
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classifier.reevaluate_wikipedia_metric(company, db, industry)
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logger.info(f"Wikipedia metric re-evaluation complete for {company.name}")
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else:
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logger.warning(f"Industry '{company.industry_ai}' not found for re-evaluation.")
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except Exception as e:
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logger.error(f"Wikipedia Re-evaluation Task Error: {e}", exc_info=True)
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finally:
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db.close()
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def run_metric_reextraction_task(company_id: int):
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from .database import SessionLocal
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db = SessionLocal()
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try:
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company = db.query(Company).filter(Company.id == company_id).first()
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if not company: return
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logger.info(f"Re-extracting metrics for {company.name} (Industry: {company.industry_ai})")
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industries = db.query(Industry).all()
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industry = next((i for i in industries if i.name == company.industry_ai), None)
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if industry:
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classifier.extract_metrics_for_industry(company, db, industry)
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company.status = "ENRICHED"
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db.commit()
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logger.info(f"Metric re-extraction complete for {company.name}")
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else:
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logger.warning(f"Industry '{company.industry_ai}' not found for re-extraction.")
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except Exception as e:
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logger.error(f"Metric Re-extraction Task Error: {e}", exc_info=True)
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finally:
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db.close()
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def run_discovery_task(company_id: int):
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from .database import SessionLocal
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db = SessionLocal()
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@@ -10,10 +10,10 @@ try:
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class Settings(BaseSettings):
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# App Info
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APP_NAME: str = "Company Explorer"
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VERSION: str = "0.7.0"
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VERSION: str = "0.6.4"
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DEBUG: bool = True
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# Database (Store in App dir for simplicity)
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# Database (FINAL CORRECT PATH for Docker Container)
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DATABASE_URL: str = "sqlite:////app/companies_v3_fixed_2.db"
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# API Keys
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@@ -32,20 +32,25 @@ try:
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except ImportError:
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# Fallback wenn pydantic-settings nicht installiert ist
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class Settings:
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class FallbackSettings:
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APP_NAME = "Company Explorer"
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VERSION = "0.2.1"
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VERSION = "0.6.4"
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DEBUG = True
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DATABASE_URL = "sqlite:////app/logs_debug/companies_debug.db"
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DATABASE_URL = "sqlite:////app/companies_v3_fixed_2.db" # FINAL CORRECT PATH
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GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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SERP_API_KEY = os.getenv("SERP_API_KEY")
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LOG_DIR = "/app/logs_debug"
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settings = Settings()
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settings = FallbackSettings()
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# Ensure Log Dir
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try:
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os.makedirs(settings.LOG_DIR, exist_ok=True)
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except FileExistsError:
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pass
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except Exception as e:
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logging.warning(f"Could not create log directory {settings.LOG_DIR}: {e}")
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# API Key Loading Helper (from file if env missing)
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def load_api_key_from_file(filename: str) -> Optional[str]:
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@@ -54,10 +59,10 @@ def load_api_key_from_file(filename: str) -> Optional[str]:
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with open(filename, 'r') as f:
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return f.read().strip()
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except Exception as e:
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print(f"Could not load key from {filename}: {e}") # Print because logging might not be ready
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logging.warning(f"Could not load key from {filename}: {e}")
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return None
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# Auto-load keys if not in env
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# Auto-load keys assuming the app runs in the Docker container's /app context
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if not settings.GEMINI_API_KEY:
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settings.GEMINI_API_KEY = load_api_key_from_file("/app/gemini_api_key.txt")
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135
company-explorer/backend/lib/metric_parser.py
Normal file
135
company-explorer/backend/lib/metric_parser.py
Normal file
@@ -0,0 +1,135 @@
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import re
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import logging
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from typing import Optional, Union
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logger = logging.getLogger(__name__)
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class MetricParser:
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"""
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Robust parser for extracting numeric values from text, specialized for
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German formats and business metrics (Revenue, Employees).
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Reconstructs legacy logic to handle thousands separators and year-suffixes.
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"""
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||||
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@staticmethod
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def extract_numeric_value(text: str, is_revenue: bool = False) -> Optional[float]:
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"""
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Extracts a float value from a string, handling German locale and suffixes.
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|
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Args:
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text: The raw text containing the number (e.g. "1.005 Mitarbeiter (2020)").
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is_revenue: If True, prioritizes currency logic (e.g. handling "Mio").
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Returns:
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The parsed float value or None if no valid number found.
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"""
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||||
if not text:
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||||
return None
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||||
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# 1. Cleaning: Remove Citations [1], [note 2]
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||||
clean_text = re.sub(r'\[.*?\]', '', text)
|
||||
|
||||
# 2. Cleaning: Remove Year/Date in parentheses to prevent "80 (2020)" -> 802020
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||||
# Matches (2020), (Stand 2021), (31.12.2022), etc.
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||||
# We replace them with space to avoid merging numbers.
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||||
clean_text = re.sub(r'\(\s*(?:Stand\s*|ab\s*)?(?:19|20)\d{2}.*?\)', ' ', clean_text)
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||||
|
||||
# 3. Identify Multipliers (Mio, Mrd)
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||||
multiplier = 1.0
|
||||
lower_text = clean_text.lower().replace('.', '') # Remove dots for word matching (e.g. "Mio." -> "mio")
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||||
|
||||
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)
|
||||
@@ -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.
|
||||
Analysiere den folgenden Text, um spezifische Metrik-Informationen zu extrahieren.
|
||||
Du bist ein Datenextraktions-Spezialist für Unternehmens-Kennzahlen.
|
||||
Analysiere den folgenden Text, um spezifische Werte zu extrahieren.
|
||||
|
||||
--- KONTEXT ---
|
||||
Unternehmen ist in der Branche: {industry_name}
|
||||
Gesuchter Wert (Rohdaten): '{search_term}'
|
||||
Branche: {industry_name}
|
||||
Primär gesuchte Metrik: '{search_term}'
|
||||
|
||||
--- TEXT ---
|
||||
{text_content_excerpt}
|
||||
|
||||
--- AUFGABE ---
|
||||
1. Finde den numerischen Wert für '{search_term}'.
|
||||
2. Versuche auch, eine explizit genannte Gesamtfläche in Quadratmetern (m²) zu finden, falls relevant und vorhanden.
|
||||
1. Finde den numerischen Wert für die primäre Metrik '{search_term}'.
|
||||
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_unit': Die Einheit des raw_value (z.B. "Betten", "Stellplätze"). null, falls nicht gefunden.
|
||||
'area_value': Ein gefundener numerischer Wert für eine Gesamtfläche in m² (als Zahl). null, falls nicht gefunden.
|
||||
'metric_name': Der Name der Metrik, nach der gesucht wurde (also '{search_term}').
|
||||
'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_value': Der numerische Wert für '{search_term}'. null, falls nicht gefunden.
|
||||
'raw_unit': Die Einheit (z.B. "Besucher", "Passagiere"). null, falls nicht gefunden.
|
||||
'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):
|
||||
results["calculated_metric_value"] = llm_result.get("raw_value")
|
||||
print(f"--- DEBUG: LLM Result for {source_name}: {llm_result}")
|
||||
|
||||
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:
|
||||
results["standardized_metric_value"] = llm_result.get("area_value")
|
||||
elif llm_result.get("raw_value") is not None and standardization_logic:
|
||||
results["standardized_metric_value"] = self._parse_standardization_logic(standardization_logic, llm_result["raw_value"])
|
||||
# 3. Area Extraction Logic (Cascading)
|
||||
area_val = llm_result.get("area_value")
|
||||
# Try to refine area_value if a segment exists
|
||||
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:
|
||||
logger.info(f"Starting classification for {company.name}")
|
||||
def extract_metrics_for_industry(self, company: Company, db: Session, industry: Industry) -> Company:
|
||||
"""
|
||||
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
|
||||
|
||||
@@ -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>
|
||||
|
||||
|
||||
@@ -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/${companyId}`)
|
||||
console.log("[Inspector] Delete successful")
|
||||
onClose() // Close the inspector on success
|
||||
window.location.reload() // Force reload to show updated list
|
||||
} catch (e: any) {
|
||||
console.error("[Inspector] Delete failed:", e)
|
||||
alert("Failed to delete company: " + (e.response?.data?.detail || e.message))
|
||||
}
|
||||
}
|
||||
|
||||
const handleLockToggle = async (sourceType: string, currentLockStatus: boolean) => {
|
||||
if (!companyId) return
|
||||
try {
|
||||
@@ -265,6 +333,13 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
|
||||
<div className="flex justify-between items-start mb-4">
|
||||
<h2 className="text-xl font-bold text-slate-900 dark:text-white leading-tight">{data.name}</h2>
|
||||
<div className="flex items-center gap-2">
|
||||
<button
|
||||
onClick={handleDelete}
|
||||
className="p-1.5 text-slate-500 hover:text-red-600 dark:hover:text-red-500 transition-colors"
|
||||
title="Delete Company"
|
||||
>
|
||||
<Trash2 className="h-4 w-4" />
|
||||
</button>
|
||||
<button
|
||||
onClick={handleExport}
|
||||
className="p-1.5 text-slate-500 hover:text-blue-600 dark:hover:text-blue-400 transition-colors"
|
||||
@@ -287,17 +362,22 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
|
||||
|
||||
<div className="flex flex-wrap gap-2 text-sm items-center">
|
||||
{!isEditingWebsite ? (
|
||||
<div className="flex items-center gap-2">
|
||||
<div className="flex items-center gap-2 group">
|
||||
{data.website && data.website !== "k.A." ? (
|
||||
<a href={data.website} target="_blank" className="flex items-center gap-1 text-blue-600 dark:text-blue-400 hover:text-blue-800 dark:hover:text-blue-300 transition-colors font-medium">
|
||||
<ExternalLink className="h-3 w-3" /> {new URL(data.website).hostname.replace('www.', '')}
|
||||
<a
|
||||
href={data.website.startsWith('http') ? data.website : `https://${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">
|
||||
{['cleaning', 'transport', 'security', 'service'].map(type => {
|
||||
const sig = data.signals.find(s => s.signal_type.includes(type))
|
||||
const score = sig ? sig.confidence : 0
|
||||
{data.calculated_metric_value != null || data.standardized_metric_value != null ? (
|
||||
<div className="bg-slate-50 dark:bg-slate-950 rounded-lg p-4 border border-slate-200 dark:border-slate-800 space-y-4">
|
||||
{/* Calculated Metric */}
|
||||
{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 (
|
||||
<div key={type} className="bg-slate-50 dark:bg-slate-800/50 p-3 rounded-lg border border-slate-200 dark:border-slate-700">
|
||||
<div className="flex justify-between mb-1">
|
||||
<span className="text-sm text-slate-700 dark:text-slate-300 capitalize">{type}</span>
|
||||
<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")}>
|
||||
{score}%
|
||||
</span>
|
||||
{/* Standardized Metric */}
|
||||
{data.standardized_metric_value != null && (
|
||||
<div className="flex items-start gap-3 pt-4 border-t border-slate-200 dark:border-slate-800">
|
||||
<div className="p-2 bg-white dark:bg-slate-800 rounded-lg text-green-500 mt-1">
|
||||
<Ruler className="h-4 w-4" />
|
||||
</div>
|
||||
<div className="w-full bg-slate-200 dark:bg-slate-700 h-1.5 rounded-full overflow-hidden">
|
||||
<div
|
||||
className={clsx("h-full rounded-full", score > 70 ? "bg-green-500" : score > 30 ? "bg-yellow-500" : "bg-slate-500")}
|
||||
style={{ width: `${score}%` }}
|
||||
/>
|
||||
<div>
|
||||
<div className="text-[10px] text-slate-500 uppercase font-bold tracking-tight">Standardized Potential ({data.standardized_metric_unit})</div>
|
||||
<div className="text-xl text-green-600 dark:text-green-400 font-bold">
|
||||
{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 dark:text-slate-500 mt-2 line-clamp-2" title={sig.proof_text}>
|
||||
"{sig.proof_text}"
|
||||
</p>
|
||||
<p className="text-xs text-slate-500 mt-1">Comparable value for potential analysis.</p>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 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 */}
|
||||
|
||||
Reference in New Issue
Block a user