feat(company-explorer): Initial Web UI & Backend with Enrichment Flow

This commit introduces the foundational elements for the new "Company Explorer" web application, marking a significant step away from the legacy Google Sheets / CLI system.

Key changes include:
- Project Structure: A new  directory with separate  (FastAPI) and  (React/Vite) components.
- Data Persistence: Migration from Google Sheets to a local SQLite database () using SQLAlchemy.
- Core Utilities: Extraction and cleanup of essential helper functions (LLM wrappers, text utilities) into .
- Backend Services: , ,  for AI-powered analysis, and  logic.
- Frontend UI: Basic React application with company table, import wizard, and dynamic inspector sidebar.
- Docker Integration: Updated  and  for multi-stage builds and sideloading.
- Deployment & Access: Integrated into central Nginx proxy and dashboard, accessible via .

Lessons Learned & Fixed during development:
- Frontend Asset Loading: Addressed issues with Vite's  path and FastAPI's .
- TypeScript Configuration: Added  and .
- Database Schema Evolution: Solved  errors by forcing a new database file and correcting  override.
- Logging: Implemented robust file-based logging ().

This new foundation provides a powerful and maintainable platform for future B2B robotics lead generation.

company-explorer/backend/services/classification.py

@@ -0,0 +1,77 @@
+import json
+import logging
+import os
+from typing import Dict, Any, List
+from ..lib.core_utils import call_gemini
+from ..config import settings
+
+logger = logging.getLogger(__name__)
+
+ALLOWED_INDUSTRIES_FILE = os.path.join(os.path.dirname(__file__), "../data/allowed_industries.json")
+
+class ClassificationService:
+    def __init__(self):
+        self.allowed_industries = self._load_allowed_industries()
+
+    def _load_allowed_industries(self) -> List[str]:
+        try:
+            with open(ALLOWED_INDUSTRIES_FILE, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        except Exception as e:
+            logger.error(f"Failed to load allowed industries: {e}")
+            return ["Sonstige"]
+
+    def analyze_robotics_potential(self, company_name: str, website_text: str) -> Dict[str, Any]:
+        """
+        Analyzes the company for robotics potential based on website content.
+        Returns strict JSON.
+        """
+        if not website_text or len(website_text) < 100:
+            return {"error": "Insufficient text content"}
+
+        prompt = f"""
+        You are a Senior B2B Market Analyst for 'Roboplanet', a robotics distributor.
+        Your job is to analyze a target company based on their website text and determine their potential for using robots.
+
+        --- TARGET COMPANY ---
+        Name: {company_name}
+        Website Content (Excerpt):
+        {website_text[:15000]} 
+        
+        --- ALLOWED INDUSTRIES (STRICT) ---
+        You MUST assign the company to exactly ONE of these industries. If unsure, choose the closest match or "Sonstige".
+        {json.dumps(self.allowed_industries, ensure_ascii=False)}
+
+        --- ANALYSIS TASKS ---
+        1. **Industry Classification:** Pick one from the list.
+        2. **Robotics Potential Scoring (0-100):**
+           - **Cleaning:** Does the company manage large floors, hospitals, hotels, or public spaces? (Keywords: Hygiene, Cleaning, SPA, Facility Management)
+           - **Transport/Logistics:** Do they move goods internally? (Keywords: Warehouse, Intralogistics, Production line, Hospital logistics)
+           - **Security:** Do they have large perimeters or night patrols? (Keywords: Werkschutz, Security, Monitoring)
+           - **Service:** Do they interact with guests/patients? (Keywords: Reception, Restaurant, Nursing)
+        
+        3. **Explanation:** A short, strategic reason for the scoring (German).
+
+        --- OUTPUT FORMAT (JSON ONLY) ---
+        {{
+            "industry": "String (from list)",
+            "summary": "Short business summary (German)",
+            "potentials": {{
+                "cleaning": {{ "score": 0-100, "reason": "..." }},
+                "transport": {{ "score": 0-100, "reason": "..." }},
+                "security": {{ "score": 0-100, "reason": "..." }},
+                "service": {{ "score": 0-100, "reason": "..." }}
+            }}
+        }}
+        """
+
+        try:
+            response_text = call_gemini(
+                prompt=prompt,
+                json_mode=True,
+                temperature=0.2 # Low temp for consistency
+            )
+            return json.loads(response_text)
+        except Exception as e:
+            logger.error(f"Classification failed: {e}")
+            return {"error": str(e)}