Brancheneinstufung2/GEMINI.md

Gemini Code Assistant Context

Wichtige Hinweise

• Projektdokumentation: Die primäre und umfassendste Dokumentation für dieses Projekt befindet sich in der Datei readme.md. Bitte ziehen Sie diese Datei für ein detailliertes Verständnis der Architektur und der einzelnen Module zu Rate.
• Git-Repository: Dieses Projekt wird über ein Git-Repository verwaltet. Alle Änderungen am Code werden versioniert. Beachten Sie den Abschnitt "Git Workflow & Conventions" für unsere Arbeitsregeln.

Project Overview

This project is a Python-based system for automated company data enrichment and lead generation. It uses a variety of data sources, including web scraping, Wikipedia, and the OpenAI API, to enrich company data from a CRM system. The project is designed to run in a Docker container and can be controlled via a Flask API.

The system is modular and consists of the following key components:

• brancheneinstufung_167.py: The core module for data enrichment, including web scraping, Wikipedia lookups, and AI-based analysis.
• company_deduplicator.py: A module for intelligent duplicate checking, both for external lists and internal CRM data.
• generate_marketing_text.py: An engine for creating personalized marketing texts.
• app.py: A Flask application that provides an API to run the different modules.

Git Workflow & Conventions

• Commit-Nachrichten: Commits sollen einem klaren Format folgen:
  • Titel: Eine prägnante Zusammenfassung unter 100 Zeichen.
  • Beschreibung: Detaillierte Änderungen als Liste mit - am Zeilenanfang (keine Bulletpoints).
• Datei-Umbenennungen: Um die Git-Historie einer Datei zu erhalten, muss sie zwingend mit git mv alter_name.py neuer_name.py umbenannt werden.
• Commit & Push Prozess: Änderungen werden zuerst lokal committet. Das Pushen auf den Remote-Server erfolgt erst nach expliziter Bestätigung durch Sie.
• Anzeige der Historie: Web-Oberflächen wie Gitea zeigen die Historie einer umbenannten Datei möglicherweise nicht vollständig an. Die korrekte und vollständige Historie kann auf der Kommandozeile mit git log --follow <dateiname> eingesehen werden.

Building and Running

The project is designed to be run in a Docker container. The Dockerfile contains the instructions to build the container.

To build the Docker container:

docker build -t company-enrichment .

To run the Docker container:

docker run -p 8080:8080 company-enrichment

The application will be available at http://localhost:8080.

Development Conventions

• Configuration: The project uses a config.py file to manage configuration settings.
• Dependencies: Python dependencies are listed in the requirements.txt file.
• Modularity: The code is modular and well-structured, with helper functions and classes to handle specific tasks.
• API: The Flask application in app.py provides an API to interact with the system.
• Logging: The project uses the logging module to log information and errors.
• Error Handling: The readme.md indicates a critical error related to the openai library. The next step is to downgrade the library to a compatible version.

Current Status (Jan 05, 2026) - GTM & Market Intel Fixes

• GTM Architect (v2.4) - UI/UX Refinement:

  • Corporate Design Integration: A central, customizable CORPORATE_DESIGN_PROMPT was introduced in config.py to ensure all generated images strictly follow a "clean, professional, photorealistic" B2B style, avoiding comic aesthetics.
  • Aspect Ratio Control: Implemented user-selectable aspect ratios (16:9, 9:16, 1:1, 4:3) in the frontend (Phase 6), passing through to the Google Imagen/Gemini 2.5 API.
  • Frontend Fix: Resolved a double-declaration bug in App.tsx that prevented the build.

• Market Intelligence Tool (v1.2) - Backend Hardening:

  • "Failed to fetch" Resolved: Fixed a critical Nginx routing issue by forcing the frontend to use relative API paths (./api) instead of absolute ports, ensuring requests correctly pass through the reverse proxy in Docker.
  • JSON Stability: The Python Orchestrator and Node.js bridge were hardened against invalid JSON output. The system now robustly handles stdout noise and logs full raw output to /app/Log/server_dump.txt in case of errors.
  • Language Support: Implemented a --language flag. The tool now correctly respects the frontend language selection (defaulting to German) and forces the LLM to output German text for signals, ICPs, and outreach campaigns.
  • Logging: Fixed log volume mounting paths to ensure debug logs are persisted and accessible.

Current Status (Jan 2026) - GTM Architect & Core Updates

• GTM Architect (v2.2) - FULLY OPERATIONAL:
  • Image Generation Fixed: Successfully implemented a hybrid image generation pipeline.
    • Text-to-Image: Uses imagen-4.0-generate-001 for generic scenes.
    • Image-to-Image: Uses gemini-2.5-flash-image with reference image upload for product-consistent visuals.
    • Prompt Engineering: Strict prompts ensure the product design remains unaltered.
  • Library Upgrade: Migrated core AI logic to google-genai (v1.x) to resolve deprecation warnings and access newer models. Pillow added for image processing.
  • Model Update: Switched text generation to gemini-2.0-flash due to regional unavailability of 1.5.
  • Frontend Stability: Fixed a critical React crash in Phase 3 by handling object-based role descriptions robustly.
  • Infrastructure: Updated Docker configurations (gtm-architect/requirements.txt) to support new dependencies.

Next Steps

• Monitor Logs: Check Log_from_docker/ for detailed execution traces of the GTM Architect.
• Feedback Loop: Verify the quality of the generated GTM strategies and adjust prompts in gtm_architect_orchestrator.py if necessary.