[31388f42] Final session polish: Refined UI, improved ingest parsing, and completed documentation

.dev_session/SESSION_INFO

@@ -1 +1 @@
-{"task_id": "31588f42-8544-800b-8c82-e17c067bdf69", "token": "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", "readme_path": "connector-superoffice/README.md", "session_start_time": "2026-02-28T18:45:32.220313"}
+{"task_id": "31388f42-8544-81d0-9016-e3bf25383da3", "token": "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", "readme_path": null, "session_start_time": "2026-03-02T07:27:14.846513"}

add_mapping.py

@@ -1,12 +0,0 @@
-import sqlite3
-
-def add_mapping():
-    conn = sqlite3.connect('/app/companies_v3_fixed_2.db')
-    cursor = conn.cursor()
-    cursor.execute("INSERT INTO job_role_mappings (pattern, role, created_at) VALUES ('%geschäftsführung%', 'Wirtschaftlicher Entscheider', '2026-02-22T14:30:00')")
-    conn.commit()
-    conn.close()
-    print("Added mapping for geschäftsführung")
-
-if __name__ == "__main__":
-    add_mapping()

company-explorer/backend/app.py

@@ -647,6 +647,17 @@ def create_contact_endpoint(contact: ContactCreate, db: Session = Depends(get_db
     if not company:
         raise HTTPException(status_code=404, detail="Company not found")
         
+    # Automatic Role Mapping logic
+    final_role = contact.role
+    if contact.job_title and not final_role:
+        role_mapping_service = RoleMappingService(db)
+        found_role = role_mapping_service.get_role_for_job_title(contact.job_title)
+        if found_role:
+            final_role = found_role
+        else:
+            # Log unclassified title for future mining
+            role_mapping_service.add_or_update_unclassified_title(contact.job_title)
+
     # Check if contact with same email already exists for this company
     if contact.email:
         existing = db.query(Contact).filter(Contact.company_id == contact.company_id, Contact.email == contact.email).first()
@@ -655,7 +666,7 @@ def create_contact_endpoint(contact: ContactCreate, db: Session = Depends(get_db
             existing.first_name = contact.first_name
             existing.last_name = contact.last_name
             existing.job_title = contact.job_title
-            existing.role = contact.role
+            existing.role = final_role
             db.commit()
             db.refresh(existing)
             return existing
@@ -666,7 +677,7 @@ def create_contact_endpoint(contact: ContactCreate, db: Session = Depends(get_db
         last_name=contact.last_name,
         email=contact.email,
         job_title=contact.job_title,
-        role=contact.role,
+        role=final_role,
         is_primary=contact.is_primary,
         status="ACTIVE",
         unsubscribe_token=str(uuid.uuid4())

lead-engine/Dockerfile

@@ -8,4 +8,5 @@ COPY . .
 RUN pip install streamlit pandas requests python-dotenv
 
 ENV PYTHONUNBUFFERED=1
-CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Start monitor in background and streamlit in foreground
+CMD ["sh", "-c", "python monitor.py & streamlit run app.py --server.port=8501 --server.address=0.0.0.0"]

lead-engine/app.py

@@ -8,22 +8,40 @@ from enrich import run_sync, refresh_ce_data, sync_single_lead
 from generate_reply import generate_email_draft
 
 def clean_html_to_text(html_content):
-    """Simple helper to convert HTML email body to readable plain text."""
+    """Surgical helper to extract relevant Tradingtwins data and format it cleanly."""
     if not html_content:
         return ""
-    # Remove head and style tags entirely
+    
+    # 1. Strip head and style
     clean = re.sub(r'<head.*?>.*?</head>', '', html_content, flags=re.DOTALL | re.IGNORECASE)
     clean = re.sub(r'<style.*?>.*?</style>', '', clean, flags=re.DOTALL | re.IGNORECASE)
-    # Replace <br> and </p> with newlines
+    
+    # 2. Extract the core data block (from 'Datum:' until the matchmaking plug)
+    # We look for the first 'Datum:' label
+    start_match = re.search(r'Datum:', clean, re.IGNORECASE)
+    end_match = re.search(r'Kennen Sie schon Ihr persönliches Konto', clean, re.IGNORECASE)
+    
+    if start_match:
+        start_pos = start_match.start()
+        end_pos = end_match.start() if end_match else len(clean)
+        clean = clean[start_pos:end_pos]
+    
+    # 3. Format Table Structure: </td><td> should be a space/tab, </tr> a newline
+    # This prevents the "Label on one line, value on next" issue
+    clean = re.sub(r'</td>\s*<td.*?>', '  ', clean, flags=re.IGNORECASE)
+    clean = re.sub(r'</tr>', '\n', clean, flags=re.IGNORECASE)
+    
+    # 4. Standard Cleanup
     clean = re.sub(r'<br\s*/?>', '\n', clean, flags=re.IGNORECASE)
     clean = re.sub(r'</p>', '\n', clean, flags=re.IGNORECASE)
-    # Remove all other tags
     clean = re.sub(r'<.*?>', '', clean)
-    # Decode some common entities
-    clean = clean.replace('&nbsp;', ' ').replace('&amp;', '&').replace('&quot;', '"')
-    # Cleanup multiple newlines
-    clean = re.sub(r'\n\s*\n+', '\n\n', clean).strip()
-    return clean
+    
+    # 5. Entity Decoding
+    clean = clean.replace('&nbsp;', ' ').replace('&amp;', '&').replace('&quot;', '"').replace('&gt;', '>')
+    
+    # 6. Final Polish: remove empty lines and leading/trailing whitespace
+    lines = [line.strip() for line in clean.split('\n') if line.strip()]
+    return '\n'.join(lines)
 
 st.set_page_config(page_title="TradingTwins Lead Engine", layout="wide")
 
@@ -140,13 +158,15 @@ if not df.empty:
             if meta.get('is_low_quality'):
                 st.warning("⚠️ **Low Quality Lead detected** (Free-mail or missing company).")
 
-            # --- SECTION 1: LEAD INFO (2 Columns) ---
-            st.markdown("### 📋 Lead Data")
-            c1, c2 = st.columns(2)
+            # --- SECTION 1: LEAD INFO & INTELLIGENCE ---
+            col_lead, col_intel = st.columns(2)
             
-            with c1:
+            with col_lead:
+                st.markdown("### 📋 Lead Data")
+                st.write(f"**Salutation:** {meta.get('salutation', '-')}")
                 st.write(f"**Contact:** {row['contact_name']}")
                 st.write(f"**Email:** {row['email']}")
+                st.write(f"**Phone:** {meta.get('phone', row.get('phone', '-'))}")
                 
                 role = meta.get('role')
                 if role:
@@ -158,58 +178,56 @@ if not df.empty:
                             found_role = enrich_contact_role(row)
                             if found_role: st.success(f"Found: {found_role}"); st.rerun()
                             else: st.error("No role found.")
-            
-            with c2:
+                
                 st.write(f"**Area:** {meta.get('area', '-')}")
                 st.write(f"**Purpose:** {meta.get('purpose', '-')}")
+                st.write(f"**Functions:** {meta.get('cleaning_functions', '-')}")
                 st.write(f"**Location:** {meta.get('zip', '')} {meta.get('city', '')}")
-                
-                with st.expander("Original Body Preview"):
-                    st.text(clean_html_to_text(row['raw_body']))
-                    if st.checkbox("Show HTML", key=f"raw_{row['id']}"):
-                        st.code(row['raw_body'], language="html")
 
-            st.divider()
-
-            # --- SECTION 2: INTELLIGENCE (CE) ---
-            st.markdown("### 🔍 Intelligence (CE)")
-            enrichment = json.loads(row['enrichment_data']) if row['enrichment_data'] else {}
-            ce_id = enrichment.get('ce_id')
-            
-            if ce_id:
-                st.success(f"✅ Linked to Company Explorer (ID: {ce_id})")
-                ce_data = enrichment.get('ce_data', {})
+            with col_intel:
+                st.markdown("### 🔍 Intelligence (CE)")
+                enrichment = json.loads(row['enrichment_data']) if row['enrichment_data'] else {}
+                ce_id = enrichment.get('ce_id')
                 
-                vertical = ce_data.get('industry_ai') or ce_data.get('vertical')
-                summary = ce_data.get('research_dossier') or ce_data.get('summary')
-                
-                intel_col1, intel_col2 = st.columns([1, 2])
-                with intel_col1:
+                if ce_id:
+                    st.success(f"✅ Linked to Company Explorer (ID: {ce_id})")
+                    ce_data = enrichment.get('ce_data', {})
+                    
+                    vertical = ce_data.get('industry_ai') or ce_data.get('vertical')
+                    summary = ce_data.get('research_dossier') or ce_data.get('summary')
+                    
                     if vertical and vertical != 'None':
                         st.info(f"**Industry:** {vertical}")
                     else:
                         st.warning("Industry Analysis pending...")
                     
+                    if summary:
+                        with st.expander("Show AI Research Dossier", expanded=True):
+                            st.write(summary)
+                    
                     if st.button("🔄 Refresh CE Data", key=f"refresh_{row['id']}"):
                         with st.spinner("Fetching..."):
                             refresh_ce_data(row['id'], ce_id)
                             st.rerun()
-                
-                with intel_col2:
-                    if summary:
-                        with st.expander("Show AI Research Dossier", expanded=True):
-                            st.write(summary)
-            else:
-                st.warning("⚠️ Not synced with Company Explorer yet")
-                if st.button("🚀 Sync to Company Explorer", key=f"sync_single_{row['id']}"):
-                    with st.spinner("Syncing..."):
-                        sync_single_lead(row['id'])
-                        st.rerun()
+                else:
+                    st.warning("⚠️ Not synced with Company Explorer yet")
+                    if st.button("🚀 Sync to Company Explorer", key=f"sync_single_{row['id']}"):
+                        with st.spinner("Syncing..."):
+                            sync_single_lead(row['id'])
+                            st.rerun()
 
             st.divider()
 
-            # --- SECTION 3: RESPONSE DRAFT ---
-            st.markdown("### ✉️ Response Draft")
+            # --- SECTION 2: ORIGINAL EMAIL ---
+            with st.expander("✉️ View Original Email Content"):
+                st.text(clean_html_to_text(row['raw_body']))
+                if st.checkbox("Show Raw HTML", key=f"raw_{row['id']}"):
+                    st.code(row['raw_body'], language="html")
+
+            st.divider()
+
+            # --- SECTION 3: RESPONSE DRAFT (Full Width) ---
+            st.markdown("### 📝 Response Draft")
             if row['status'] != 'new' and ce_id:
                 if st.button("✨ Generate Expert Reply", key=f"gen_{row['id']}", type="primary"):
                     with st.spinner("Writing email..."):

lead-engine/db.py

@@ -57,6 +57,9 @@ def insert_lead(lead_data):
         'zip': lead_data.get('zip'),
         'city': lead_data.get('city'),
         'role': lead_data.get('role'),
+        'salutation': lead_data.get('salutation'),
+        'phone': lead_data.get('phone'),
+        'cleaning_functions': lead_data.get('cleaning_functions'),
         'is_free_mail': lead_data.get('is_free_mail', False),
         'is_low_quality': lead_data.get('is_low_quality', False)
     }

lead-engine/generate_reply.py

@@ -3,8 +3,9 @@ import json
 import requests
 import sqlite3
 import re
+import datetime
 
-# Load API Key
+# --- Helper: Get Gemini Key ---
 def get_gemini_key():
     candidates = [
         "gemini_api_key.txt",                     # Current dir
@@ -57,34 +58,63 @@ def get_matrix_context(industry_name, persona_name):
         
     return context
 
-def get_product_recommendation(area_str):
+def get_suggested_date():
+    """Calculates a suggested meeting date (3-4 days in future, avoiding weekends)."""
+    now = datetime.datetime.now()
+    # Jump 3 days ahead
+    suggested = now + datetime.timedelta(days=3)
+    # If weekend, move to Monday
+    if suggested.weekday() == 5: # Saturday
+        suggested += datetime.timedelta(days=2)
+    elif suggested.weekday() == 6: # Sunday
+        suggested += datetime.timedelta(days=1)
+    
+    days_de = ["Montag", "Dienstag", "Mittwoch", "Donnerstag", "Freitag", "Samstag", "Sonntag"]
+    return f"{days_de[suggested.weekday()]}, den {suggested.strftime('%d.%m.')} um 10:00 Uhr"
+
+def clean_company_name(name):
+    """Removes legal suffixes like GmbH, AG, etc. for a more personal touch."""
+    if not name: return ""
+    # Remove common German legal forms
+    cleaned = re.sub(r'\s+(GmbH|AG|GmbH\s+&\s+Co\.\s+KG|KG|e\.V\.|e\.K\.|Limited|Ltd|Inc)\.?(?:\s|$)', '', name, flags=re.IGNORECASE)
+    return cleaned.strip()
+
+def get_multi_solution_recommendation(area_str, purpose_str):
     """
-    Selects the right robot based on the surface area mentioned in the lead.
+    Selects a range of robots based on surface area AND requested purposes.
     """
-    # Naive extraction of first number in the string
+    recommendations = []
+    purpose_lower = purpose_str.lower()
+    
+    # 1. Cleaning Logic (Area based)
     nums = re.findall(r'\d+', area_str.replace('.', '').replace(',', ''))
     area_val = int(nums[0]) if nums else 0
     
-    if area_val >= 5000 or "über 10.000" in area_str:
-        return {
-            "name": "Scrubber 75",
-            "reason": "als industrielles Kraftpaket für Großflächen ausgelegt",
-            "usp": "höchste Effizienz und Autonomie auf mehreren tausend Quadratmetern"
-        }
-    elif area_val >= 1000:
-        return {
-            "name": "Scrubber 50 oder Phantas",
-            "reason": "die optimale Balance zwischen Reinigungsleistung und Wendigkeit",
-            "usp": "ideal für mittelgroße Fertigungs- und Lagerbereiche"
-        }
-    else:
-        return {
-            "name": "Phantas oder Pudu CC1",
-            "reason": "kompakt und wendig für komplexe Umgebungen",
-            "usp": "perfekt für Büros, Praxen oder engere Verkehrswege"
-        }
+    if "reinigung" in purpose_lower:
+        if area_val >= 5000 or "über 10.000" in area_str:
+            recommendations.append("den Scrubber 75 als industrielles Kraftpaket für Ihre Großflächen")
+        elif area_val >= 1000:
+            recommendations.append("den Scrubber 50 oder Phantas für eine wendige und gründliche Bodenreinigung")
+        else:
+            recommendations.append("den Phantas oder Pudu CC1 für eine effiziente Reinigung Ihrer Räumlichkeiten")
 
-def generate_email_draft(lead_data, company_data, booking_link="[IHR BUCHUNGSLINK - BITTE IN .ENV EINTRAGEN]"):
+    # 2. Service/Transport Logic
+    if any(word in purpose_lower for word in ["servieren", "abräumen", "speisen", "getränke"]):
+        recommendations.append("den BellaBot zur Entlastung Ihres Teams beim Transport von Speisen und Getränken")
+    
+    # 3. Marketing/Interaction Logic
+    if any(word in purpose_lower for word in ["marketing", "gästebetreuung", "kundenansprache"]):
+        recommendations.append("den KettyBot als interaktiven Begleiter für Marketing und Patienteninformation")
+
+    if not recommendations:
+        recommendations.append("unsere wendigen Allrounder wie den Phantas")
+
+    return {
+        "solution_text": " und ".join(recommendations),
+        "has_multi": len(recommendations) > 1
+    }
+
+def generate_email_draft(lead_data, company_data, booking_link="[IHR BUCHUNGSLINK]"):
     """
     Generates a high-end, personalized sales email using Gemini API and Matrix knowledge.
     """
@@ -93,7 +123,8 @@ def generate_email_draft(lead_data, company_data, booking_link="[IHR BUCHUNGSLIN
         return "Error: Gemini API Key not found."
 
     # Extract Data from Lead Engine
-    company_name = lead_data.get('company_name', 'Interessent')
+    company_raw = lead_data.get('company_name', 'Interessent')
+    company_name = clean_company_name(company_raw) 
     contact_name = lead_data.get('contact_name', 'Damen und Herren')
     
     # Metadata from Lead
@@ -105,14 +136,17 @@ def generate_email_draft(lead_data, company_data, booking_link="[IHR BUCHUNGSLIN
     area = meta.get('area', 'Unbekannte Fläche')
     purpose = meta.get('purpose', 'Reinigung')
     role = meta.get('role', 'Wirtschaftlicher Entscheider')
+    salutation = meta.get('salutation', 'Damen und Herren')
+    cleaning_functions = meta.get('cleaning_functions', '')
 
     # Data from Company Explorer
     ce_summary = company_data.get('research_dossier') or company_data.get('summary', '')
-    ce_vertical = company_data.get('industry_ai') or company_data.get('vertical', 'Industry - Manufacturing')
+    ce_vertical = company_data.get('industry_ai') or company_data.get('vertical', 'Healthcare')
     ce_opener = company_data.get('ai_opener', '') 
 
-    # Product logic
-    product = get_product_recommendation(area)
+    # Multi-Solution Logic
+    solution = get_multi_solution_recommendation(area, purpose)
+    suggested_date = get_suggested_date()
 
     # Fetch "Golden Records" from Matrix
     matrix = get_matrix_context(ce_vertical, role)
@@ -122,46 +156,35 @@ def generate_email_draft(lead_data, company_data, booking_link="[IHR BUCHUNGSLIN
     Du bist ein Senior Sales Executive bei Robo-Planet. Antworte auf eine Anfrage von Tradingtwins.
     Schreibe eine E-Mail auf "Human Expert Level".
     
-    WICHTIGE STRATEGIE:
-    - Starte NICHT mit seiner Position (CFO). Starte mit der Wertschätzung für sein UNTERNEHMEN ({company_name}).
-    - Der Empfänger soll durch die Tiefe der Argumente MERKEN, dass wir für einen Entscheider schreiben.
-    - Mappe ihn erst später als "finanziellen/wirtschaftlichen Entscheider".
-    - Erwähne eine ROI-Perspektive (Amortisation).
-    
-    KONTEXT (Vom Company Explorer):
-    - Firma: {company_name}
-    - Branche: {ce_vertical}
-    - Branchen-Pains (Nutze diese für die Argumentation): {matrix['industry_pains']}
-    - Branchen-Gains: {matrix['industry_gains']}
-    - Dossier/Business-Profil: {ce_summary}
-    - Strategischer Aufhänger: {ce_opener}
-    
-    ANSPRECHPARTNER:
+    WICHTIGE IDENTITÄT:
+    - Anrede-Form: {salutation} (z.B. Herr, Frau)
     - Name: {contact_name}
-    - Rolle: {role}
+    - Firma: {company_name}
     
-    PRODUKT-EMPFEHLUNG (Basierend auf Fläche {area}):
-    - Modell: {product['name']}
-    - Warum: {product['reason']}
-    - USP: {product['usp']}
-
-    ANFRAGE-DETAILS:
-    - Bedarf: {area}
-    - Zweck: {purpose}
+    STRATEGIE:
+    - STARTE DIREKT mit dem strategischen Aufhänger aus dem Company Explorer ({ce_opener}). Baue daraus den ersten Absatz.
+    - KEIN "mit großem Interesse verfolge ich..." oder ähnliche Phrasen. Das wirkt unnatürlich.
+    - Deine Mail reagiert auf die Anfrage zu: {purpose} auf {area}.
+    - Fasse die vorgeschlagene Lösung ({solution['solution_text']}) KOMPAKT zusammen. Wir bieten ein ganzheitliches Entlastungskonzept an, keine Detail-Auflistung von Datenblättern.
+    
+    KONTEXT:
+    - Branche: {ce_vertical}
+    - Pains aus Matrix: {matrix['industry_pains']}
+    - Dossier/Wissen: {ce_summary}
+    - Strategischer Aufhänger (CE-Opener): {ce_opener}
 
     AUFGABE:
-    Schreibe eine E-Mail mit dieser Struktur:
-    1. EINSTIEG: Fokus auf Klemm Bohrtechnik und deren Marktstellung/Produkte (Bezug auf den 'Strategischen Aufhänger').
-    2. DIE BRÜCKE: Verknüpfe die Präzision ihrer Produkte mit der Notwendigkeit von sauberen Hallenböden (besonders bei {area}). Nutze den Schmerzpunkt "Prozesssicherheit/Sensorik".
-    3. DIE LÖSUNG: Positioniere den {product['name']} als genau die richtige Wahl für diese Größenordnung ({area}).
-    4. ROI-LOGIK: Sprich ihn als wirtschaftlichen Entscheider an. Erwähne, dass wir für solche Projekte ROI-Kalkulationen erstellen, die oft eine Amortisation in unter 18-24 Monaten zeigen.
-    5. CALL TO ACTION: Beratungsgespräch + Buchungslink: {booking_link}
+    1. ANREDE: Persönlich.
+    2. EINSTIEG: Nutze den inhaltlichen Kern von: "{ce_opener}".
+    3. DER ÜBERGANG: Verknüpfe dies mit der Anfrage zu {purpose}. Erkläre, dass manuelle Prozesse bei {area} angesichts der Dokumentationspflichten und des Fachkräftemangels zum Risiko werden.
+    4. DIE LÖSUNG: Schlage die Kombination aus {solution['solution_text']} als integriertes Konzept vor, um das Team in Reinigung, Service und Patientenansprache spürbar zu entlasten.
+    5. ROI: Sprich kurz die Amortisation (18-24 Monate) an – als Argument für den wirtschaftlichen Entscheider.
+    6. CTA: Schlag konkret den {suggested_date} vor. Alternativ: {booking_link}
 
-    STIL:
-    Senior, Augenhöhe, keine Floskeln, extrem fokussiert auf Effizienz und Qualität.
+    STIL: Senior, lösungsorientiert, direkt. Keine unnötigen Füllwörter.
     
     FORMAT:
-    Betreff: [Relevanter Betreff, der direkt auf Klemm Bohrtechnik / Effizienz zielt]
+    Betreff: [Prägnant, z.B. Automatisierungskonzept für {company_name}]
     
     [E-Mail Text]
     """
@@ -190,4 +213,4 @@ if __name__ == "__main__":
         "vertical": "Healthcare / Krankenhaus",
         "summary": "Ein großes Klinikum der Maximalversorgung mit Fokus auf Kardiologie."
     }
-    print(generate_email_draft(mock_lead, mock_company))
+    print(generate_email_draft(mock_lead, mock_company))

lead-engine/repair_leads.py

@@ -1,59 +0,0 @@
-import sqlite3
-import json
-import re
-import os
-import sys
-
-# Add path to import db
-sys.path.append(os.path.dirname(__file__))
-from db import get_leads, update_lead_metadata, init_db
-
-def parse_tradingtwins_html_local(html_body):
-    """
-    Extracts data from the Tradingtwins HTML table structure.
-    Copied logic to ensure independence.
-    """
-    data = {}
-    field_map = {
-        'Einsatzzweck': 'purpose',
-        'Reinigungs-Fläche': 'area',
-        'PLZ': 'zip',
-        'Stadt': 'city'
-    }
-    
-    for label, key in field_map.items():
-        pattern = fr'>\s*{re.escape(label)}:\s*</p>.*?<p[^>]*>(.*?)</p>'
-        match = re.search(pattern, html_body, re.DOTALL | re.IGNORECASE)
-        if match:
-            raw_val = match.group(1).strip()
-            clean_val = re.sub(r'<[^>]+>', '', raw_val).strip()
-            data[key] = clean_val
-    return data
-
-def repair_database():
-    print("Initializing DB (migrating schema if needed)...")
-    init_db()
-    
-    leads = get_leads()
-    print(f"Found {len(leads)} leads to check.")
-    
-    count = 0
-    for lead in leads:
-        # Check if metadata is missing or empty
-        current_meta = lead.get('lead_metadata')
-        if not current_meta or current_meta == '{}' or current_meta == 'null':
-            print(f"Repairing Lead {lead['id']} ({lead['company_name']})...")
-            
-            raw_body = lead.get('raw_body', '')
-            if raw_body:
-                extracted = parse_tradingtwins_html_local(raw_body)
-                update_lead_metadata(lead['id'], extracted)
-                print(f" -> Extracted: {extracted}")
-                count += 1
-            else:
-                print(" -> No raw body found.")
-                
-    print(f"Repaired {count} leads.")
-
-if __name__ == "__main__":
-    repair_database()

lead-engine/repair_leads_v2.py

@@ -1,40 +0,0 @@
-import sqlite3
-import json
-import re
-import os
-import sys
-
-# Add path to import db
-sys.path.append(os.path.dirname(__file__))
-from db import get_leads, update_lead_metadata
-
-def parse_names(html_body):
-    data = {}
-    # Extract Vorname and Nachname from HTML if possible
-    v_match = re.search(r'>\s*Vorname:\s*</p>.*?<p[^>]*>(.*?)</p>', html_body, re.DOTALL | re.IGNORECASE)
-    n_match = re.search(r'>\s*Nachname:\s*</p>.*?<p[^>]*>(.*?)</p>', html_body, re.DOTALL | re.IGNORECASE)
-    
-    if v_match: data['contact_first'] = re.sub(r'<[^>]+>', '', v_match.group(1)).strip()
-    if n_match: data['contact_last'] = re.sub(r'<[^>]+>', '', n_match.group(1)).strip()
-    return data
-
-def repair_names():
-    leads = get_leads()
-    count = 0
-    for lead in leads:
-        meta = json.loads(lead['lead_metadata']) if lead['lead_metadata'] else {}
-        
-        # Only repair if names are missing in meta
-        if not meta.get('contact_first'):
-            raw_body = lead.get('raw_body', '')
-            if raw_body:
-                name_data = parse_names(raw_body)
-                if name_data:
-                    meta.update(name_data)
-                    update_lead_metadata(lead['id'], meta)
-                    print(f"Fixed names for {lead['company_name']}: {name_data}")
-                    count += 1
-    print(f"Finished. Repaired {count} lead names.")
-
-if __name__ == "__main__":
-    repair_names()

lead-engine/trading_twins_ingest.py

@@ -94,9 +94,11 @@ def parse_tradingtwins_html(html_body):
         'Firma': 'company',
         'Vorname': 'contact_first', 
         'Nachname': 'contact_last', 
+        'Anrede': 'salutation',
         'E-Mail': 'email',
         'Rufnummer': 'phone',
         'Einsatzzweck': 'purpose', 
+        'Reinigungs-Funktionen': 'cleaning_functions',
         'Reinigungs-Fläche': 'area', 
         'PLZ': 'zip',
         'Stadt': 'city',