Marius Mutu
0b732f7a7a
- Oracle ERP ROA integration with sales analytics and margin analysis - Excel multi-sheet reports with conditional formatting - PDF executive summaries with charts via ReportLab - Optimized SQL queries (no cartesian products) - Docker support for cross-platform deployment - Configurable alert thresholds for business intelligence 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
94 lines
2.9 KiB
Markdown
94 lines
2.9 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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Data Intelligence Report Generator for ERP ROA (Oracle Database). Generates Excel and PDF business intelligence reports with sales analytics, margin analysis, stock tracking, and alerts.
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## Commands
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### Option 1: Virtual Environment (WSL or Windows)
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```bash
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# Create and activate virtual environment
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python -m venv .venv
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source .venv/bin/activate # Linux/WSL
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# or: .venv\Scripts\activate # Windows
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# Install dependencies
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pip install -r requirements.txt
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# Run report
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python main.py
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```
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### Option 2: Docker (Windows Docker Desktop / Linux)
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```bash
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# Copy and configure environment
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cp .env.example .env
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# Edit .env with your Oracle credentials
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# Run with docker-compose
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docker-compose run --rm report-generator
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# Or with custom months
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docker-compose run --rm report-generator python main.py --months 6
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```
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### Common Options
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```bash
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# Run with custom period
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python main.py --months 6
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# Custom output directory
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python main.py --output-dir /path/to/output
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```
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## Oracle Connection from Different Environments
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| Environment | ORACLE_HOST value |
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|-------------|-------------------|
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| Windows native | `127.0.0.1` |
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| WSL | Windows IP (run: `cat /etc/resolv.conf \| grep nameserver`) |
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| Docker | `host.docker.internal` (automatic in docker-compose) |
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## Architecture
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**Entry point**: `main.py` - CLI interface, orchestrates query execution and report generation
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**Data flow**:
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1. `config.py` loads Oracle connection settings from `.env` file
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2. `queries.py` contains all SQL queries in a `QUERIES` dictionary with metadata (title, description, params)
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3. `main.py` executes queries via `OracleConnection` context manager, stores results in `results` dict
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4. `report_generator.py` receives dataframes and generates:
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- `ExcelReportGenerator`: Multi-sheet workbook with conditional formatting
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- `PDFReportGenerator`: Executive summary with charts via ReportLab
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**Key patterns**:
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- Queries use parameterized `:months` for configurable analysis period
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- Sheet order in `main.py:sheet_order` controls Excel tab sequence
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- Charts are generated via matplotlib, converted to images for PDF
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## Oracle Database Schema
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Required views: `fact_vfacturi2`, `fact_vfacturi_detalii`, `vnom_articole`, `vnom_parteneri`, `vstoc`, `vrul`
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Filter conventions:
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- `sters = 0` excludes deleted records
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- `tip NOT IN (7, 8, 9, 24)` excludes returns/credit notes
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- Account codes: `341`, `345` = own production; `301` = raw materials
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## Adding New Reports
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1. Add SQL query constant in `queries.py`
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2. Add entry to `QUERIES` dict with `sql`, `params`, `title`, `description`
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3. Add query name to `sheet_order` list in `main.py` (line ~143)
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4. For PDF inclusion, add rendering logic in `main.py:generate_reports()`
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## Alert Thresholds (in config.py)
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- Low margin: < 15%
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- Price variation: > 20%
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- Slow stock: > 90 days without movement
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- Minimum sales for analysis: 1000 RON
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