[2ff88f42] multiplikation vorbereitet

multiplikation vorbereitet

company-explorer/backend/scripts/generate_matrix.py

@@ -0,0 +1,162 @@
+
+import sys
+import os
+import json
+import argparse
+from typing import List
+
+# Setup Environment
+sys.path.append(os.path.join(os.path.dirname(__file__), "../../"))
+
+from backend.database import SessionLocal, Industry, Persona, MarketingMatrix
+
+# --- Configuration ---
+MODEL = "gpt-4o"
+
+def generate_prompt(industry: Industry, persona: Persona) -> str:
+    """
+    Builds the prompt for the AI to generate the marketing texts.
+    Combines Industry context with Persona specific pains/gains.
+    """
+    
+    # Safely load JSON lists
+    try:
+        persona_pains = json.loads(persona.pains) if persona.pains else []
+        persona_gains = json.loads(persona.gains) if persona.gains else []
+    except:
+        persona_pains = [persona.pains] if persona.pains else []
+        persona_gains = [persona.gains] if persona.gains else []
+
+    industry_pains = industry.pains if industry.pains else "Allgemeine Effizienzprobleme"
+    
+    prompt = f"""
+Du bist ein erfahrener B2B-Copywriter für Robotik-Lösungen (Reinigung, Transport, Service).
+Ziel: Erstelle personalisierte E-Mail-Textbausteine für einen Outreach.
+
+--- KONTEXT ---
+ZIELBRANCHE: {industry.name}
+BRANCHEN-KONTEXT: {industry.description or 'Keine spezifische Beschreibung'}
+BRANCHEN-PAINS: {industry_pains}
+
+ZIELPERSON (ARCHETYP): {persona.name}
+PERSÖNLICHE PAINS (Herausforderungen):
+{chr(10).join(['- ' + p for p in persona_pains])}
+
+GEWÜNSCHTE GAINS (Ziele):
+{chr(10).join(['- ' + g for g in persona_gains])}
+
+--- AUFGABE ---
+Erstelle ein JSON-Objekt mit genau 3 Textbausteinen. 
+Tonalität: Professionell, lösungsorientiert, auf den Punkt. Keine Marketing-Floskeln ("Game Changer").
+
+1. "subject": Betreffzeile (Max 6 Wörter). Muss neugierig machen und einen Pain adressieren.
+2. "intro": Einleitungssatz (1-2 Sätze). Verbinde die Branchen-Herausforderung mit der persönlichen Rolle des Empfängers. Zeige Verständnis für seine Situation.
+3. "social_proof": Ein Satz, der Vertrauen aufbaut. Nenne generische Erfolge (z.B. "Unternehmen in der {industry.name} senken so ihre Kosten um 15%"), da wir noch keine spezifischen Logos nennen dürfen.
+
+--- FORMAT ---
+{{
+  "subject": "...",
+  "intro": "...",
+  "social_proof": "..."
+}}
+"""
+    return prompt
+
+def mock_openai_call(prompt: str):
+    """Simulates an API call for dry runs."""
+    print(f"\n--- [MOCK] GENERATING PROMPT ---\n{prompt[:300]}...\n--------------------------------")
+    return {
+        "subject": "[MOCK] Effizienzsteigerung in der Produktion",
+        "intro": "[MOCK] Als Produktionsleiter wissen Sie, wie teuer Stillstand ist. Unsere Roboter helfen.",
+        "social_proof": "[MOCK] Ähnliche Betriebe sparten 20% Kosten."
+    }
+
+def real_openai_call(prompt: str):
+    # This would link to the actual OpenAI client
+    # For now, we keep it simple or import from a lib
+    import openai
+    from backend.config import settings
+    
+    if not settings.OPENAI_API_KEY:
+        raise ValueError("OPENAI_API_KEY not set")
+        
+    client = openai.OpenAI(api_key=settings.OPENAI_API_KEY)
+    response = client.chat.completions.create(
+        model=MODEL,
+        response_format={"type": "json_object"},
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0.7
+    )
+    return json.loads(response.choices[0].message.content)
+
+def run_matrix_generation(dry_run: bool = True, force: bool = False):
+    db = SessionLocal()
+    try:
+        industries = db.query(Industry).all()
+        personas = db.query(Persona).all()
+        
+        print(f"Found {len(industries)} Industries and {len(personas)} Personas.")
+        print(f"Mode: {'DRY RUN (No API calls, no DB writes)' if dry_run else 'LIVE'}")
+        
+        total_combinations = len(industries) * len(personas)
+        processed = 0
+        
+        for ind in industries:
+            for pers in personas:
+                processed += 1
+                print(f"[{processed}/{total_combinations}] Check: {ind.name} x {pers.name}")
+                
+                # Check existing
+                existing = db.query(MarketingMatrix).filter(
+                    MarketingMatrix.industry_id == ind.id,
+                    MarketingMatrix.persona_id == pers.id
+                ).first()
+                
+                if existing and not force:
+                    print(f"  -> Skipped (Already exists)")
+                    continue
+                
+                # Generate
+                prompt = generate_prompt(ind, pers)
+                
+                if dry_run:
+                    result = mock_openai_call(prompt)
+                else:
+                    try:
+                        result = real_openai_call(prompt)
+                    except Exception as e:
+                        print(f"  -> API ERROR: {e}")
+                        continue
+
+                # Write to DB (only if not dry run)
+                if not dry_run:
+                    if not existing:
+                        new_entry = MarketingMatrix(
+                            industry_id=ind.id,
+                            persona_id=pers.id,
+                            subject=result.get("subject"),
+                            intro=result.get("intro"),
+                            social_proof=result.get("social_proof")
+                        )
+                        db.add(new_entry)
+                        print(f"  -> Created new entry.")
+                    else:
+                        existing.subject = result.get("subject")
+                        existing.intro = result.get("intro")
+                        existing.social_proof = result.get("social_proof")
+                        print(f"  -> Updated entry.")
+                    
+                    db.commit()
+
+    except Exception as e:
+        print(f"Error: {e}")
+    finally:
+        db.close()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--live", action="store_true", help="Actually call OpenAI and write to DB")
+    parser.add_argument("--force", action="store_true", help="Overwrite existing matrix entries")
+    args = parser.parse_args()
+    
+    run_matrix_generation(dry_run=not args.live, force=args.force)