[0.1.0] - 2025-06-22

Initial public release of MetriCalc
- Added full PySide6 GUI for cross-platform support
- Introduced dual processing modes: single file and batch folder
- Implemented localized interface with real-time Czech/English switching
- Developed core metric engine:
  - Precision (user accuracy)
  - Recall (producer accuracy)
  - F1-score
  - Overall accuracy
  - Kappa index
- Enabled reading of 2x2 confusion matrices from semicolon-separated CSV
- Built Excel export functionality with language-specific headers
- Added support for:
  - One output file per CSV
  - One consolidated file with multiple sheets
- Non-blocking batch processing with cancellation and progress dialog
- Clean code structure divided into `core`, `ui`, `utils`, and `assets`

Author: Jakub-Espandr

.gitignore

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+# macOS system files
+.DS_Store
+.AppleDouble
+.LSOverride
+Icon
+._*
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+*.zip
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+.pytest_cache/
+.coverage
+htmlcov/
+.tox/
+.nox/
+.hypothesis/
+.pytest_cache/
+*.mo
+*.pot
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+instance/
+.webassets-cache
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+.python-version
+
+# Node.js
+node_modules/
+npm-debug.log
+yarn-debug.log
+yarn-error.log
+.pnpm-debug.log
+.npm
+.yarn-integrity
+
+# Build outputs
+dist/
+build/
+out/
+.next/
+.nuxt/
+.output/
+
+# IDE and editor files
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+.project
+.classpath
+.settings/
+*.sublime-workspace
+*.sublime-project
+
+# Environment files
+.env
+.env.local
+.env.*.local
+.env.development
+.env.test
+.env.production
+
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# Testing
+coverage/
+.nyc_output/
+
+# Misc
+.cache/
+.temp/
+.tmp/
+*.bak
+*.tmp
+*.temp 

CHANGELOG.md

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+# Changelog
+
+## [0.1.0] - 2025-06-22
+
+### Added
+
+- **Initial Release** of MetriCalc.
+- **Modern User Interface**: Application using PySide6 to provide a modern, responsive, and cross-platform user experience.
+- **Internationalization (i18n)**: Full UI and export support for Czech and English, with a simple dropdown to switch languages in real-time.
+- **Single File Processing**: Process a single confusion matrix from a `.csv` file and export results to an `.xlsx` file.
+- **Batch Processing**: Process all `.csv` files within a selected folder with two output options:
+    - **Separate Files**: Generate an individual Excel file for each processed CSV.
+    - **Single File**: Consolidate all results into a single Excel file, with each result on its own dedicated sheet.
+- **Core Metric Calculation**: 
+    - Reads 2x2 confusion matrices from semicolon-separated `.csv` files.
+    - Calculates Precision (user accuracy), Recall (producer accuracy), F1-score, Overall Accuracy, and the Kappa Index.
+    - Provides metrics for each class and a macro average.
+- **User Experience Enhancements**:
+    - Asynchronous processing for batch operations to keep the UI responsive.
+    - Progress dialog with a cancellation option for batch jobs.

README.md

@@ -1 +1,112 @@
-# MetriCalc
+<p align="center">
+  <a href="https://i.ibb.co/0pyTV8nq/icon.png">
+    <img src="https://i.ibb.co/0pyTV8nq/icon.png" alt="MetriCalc Logo" width="220"/>
+  </a>
+</p>
+
+<h1 align="center">MetriCalc</h1>
+<p align="center"><em>(Born4Flight | FlyCamCzech)</em></p>
+
+## Overview
+MetriCalc is a clean, efficient tool designed to calculate key statistical metrics from confusion matrices exported from ArcGIS Pro or other platforms. With support for single and batch processing, multi-language output, and polished export to Excel, MetriCalc is ideal for rapid performance evaluation in classification tasks.
+
+---
+
+## ✨ Features
+
+- **Modern & Responsive UI**
+  - Built with PySide6 for a fast, native user experience
+  - Segmented tabs for single and batch processing workflows
+
+- **Internationalization (i18n)**
+  - Toggle between **Czech** and **English** for both interface and output
+  - Instant language switching without restart
+
+- **Metric Computation**
+  - Reads semicolon-delimited confusion matrix `.csv` files
+  - Calculates:
+    - Precision (User Accuracy)
+    - Recall (Producer Accuracy)
+    - F1-score
+    - Overall Accuracy
+    - Kappa Index
+  - Reports metrics per class and macro average
+
+- **Batch Processing**
+  - Process all `.csv` files in a selected folder
+  - Two export modes:
+    - One Excel per file
+    - One Excel file with multiple sheets
+
+- **Excel Export**
+  - XLSX output with localized metric headers
+  - Two sheets per result:
+    - **Metrics**: Computed values
+    - **Data**: Raw confusion matrix
+
+- **Performance**
+  - Asynchronous background thread keeps the interface responsive
+  - Real-time progress bar with cancel option
+
+---
+
+## 📦 Requirements
+
+- Python 3.8+
+- PySide6 >= 6.5.0
+- pandas >= 1.5.0
+- scikit-learn >= 1.2.0
+- openpyxl >= 3.1.0
+
+---
+
+## 🚀 Installation
+
+```bash
+git clone https://github.com/Jakub-Espandr/MetriCalc.git
+cd MetriCalc
+pip install -r requirements.txt
+python main.py
+```
+
+---
+
+## 🛠️ Usage
+
+1. Open the app and select **Single** or **Batch** tab
+2. Load `.csv` confusion matrix or folder
+3. Choose output location
+4. Select language: 🇨🇿 Čeština / 🇺🇸 English
+5. Click **Start Processing**
+
+---
+
+## 📁 Project Structure
+
+```
+MetriCalc/
+├── main.py
+├── core/            # Metric calculations and logic
+├── ui/              # PySide6 GUI components
+├── utils/           # Helpers and localization
+├── assets/
+│   ├── fonts/
+│   └── icons/
+├── requirements.txt
+└── LICENSE
+```
+
+---
+
+## 🔐 License
+
+This project is licensed under the **MIT License**  
+© 2025 Jakub Ešpandr – Born4Flight, FlyCamCzech
+
+See the [LICENSE](https://github.com/Jakub-Espandr/MetriCalc/raw/main/LICENSE) file for full terms.
+
+---
+
+## 🙏 Acknowledgments
+
+- Built with ❤️ using PySide6, pandas, and openpyxl

assets/fonts/fccTYPO-Bold.ttf

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+import pandas as pd
+from pathlib import Path
+from openpyxl import Workbook
+from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score, cohen_kappa_score
+from .translations import TRANSLATIONS
+
+def compute_metrics(df, language='cs'):
+    """Compute metrics from confusion matrix data"""
+    df = df.copy()
+    df.columns = df.columns.astype(str)
+
+    # Filter for C_1 and C_2 rows
+    df_cm = df[df['ClassValue'].isin(['C_1', 'C_2'])]
+
+    # Handle decimal commas
+    df_cm['C_1'] = df_cm['C_1'].astype(str).str.replace(',', '.').astype(float).astype(int)
+    df_cm['C_2'] = df_cm['C_2'].astype(str).str.replace(',', '.').astype(float).astype(int)
+
+    cm = df_cm[['C_1', 'C_2']].to_numpy()
+
+    y_true = [0] * int(cm[0, :].sum()) + [1] * int(cm[1, :].sum())
+    y_pred = [0] * int(cm[0, 0]) + [1] * int(cm[0, 1]) + [0] * int(cm[1, 0]) + [1] * int(cm[1, 1])
+
+    precision = precision_score(y_true, y_pred, average=None, zero_division=0)
+    recall = recall_score(y_true, y_pred, average=None, zero_division=0)
+    f1 = f1_score(y_true, y_pred, average=None, zero_division=0)
+    accuracy = round(accuracy_score(y_true, y_pred), 3)
+    kappa = round(cohen_kappa_score(y_true, y_pred), 3)
+
+    avg_precision = round(precision_score(y_true, y_pred, average='macro', zero_division=0), 3)
+    avg_recall = round(recall_score(y_true, y_pred, average='macro', zero_division=0), 3)
+    avg_f1 = round(f1_score(y_true, y_pred, average='macro', zero_division=0), 3)
+
+    class_names = TRANSLATIONS[language]['class_names']
+    return [
+        [class_names[0], round(precision[0], 3), round(recall[0], 3), round(f1[0], 3), accuracy, kappa],
+        [class_names[1], round(precision[1], 3), round(recall[1], 3), round(f1[1], 3), accuracy, kappa],
+        [class_names[2], avg_precision, avg_recall, avg_f1, accuracy, kappa]
+    ]
+
+def export_to_excel(input_path, output_path, language='cs'):
+    """Export metrics to Excel file"""
+    try:
+        df = pd.read_csv(input_path, sep=';')
+        metrics = compute_metrics(df, language)
+        wb = Workbook()
+        sheetname = Path(input_path).stem
+
+        # Metrics sheet
+        ws1 = wb.active
+        ws1.title = f"{TRANSLATIONS[language]['excel_metrics_sheet']}_{sheetname}"
+        headers = TRANSLATIONS[language]['headers']
+        for col, header in enumerate(headers, start=1):
+            ws1.cell(row=1, column=col).value = header
+        for row_i, row_data in enumerate(metrics, start=2):
+            for col_i, value in enumerate(row_data, start=1):
+                ws1.cell(row=row_i, column=col_i).value = value
+
+        # Data sheet
+        ws2 = wb.create_sheet(f"{TRANSLATIONS[language]['excel_data_sheet']}_{sheetname}")
+        ws2.append(list(df.columns))
+        for r in df.itertuples(index=False):
+            ws2.append(list(r))
+
+        wb.save(output_path)
+        return True, None, (metrics, df)
+    except Exception as e:
+        return False, str(e), None
+
+def add_to_workbook(wb, input_path, metrics, df, language='cs'):
+    """Add data to existing workbook"""
+    try:
+        sheetname = Path(input_path).stem
+        
+        # Metrics sheet
+        ws1 = wb.create_sheet(f"{TRANSLATIONS[language]['excel_metrics_sheet']}_{sheetname}")
+        headers = TRANSLATIONS[language]['headers']
+        for col, header in enumerate(headers, start=1):
+            ws1.cell(row=1, column=col).value = header
+        for row_i, row_data in enumerate(metrics, start=2):
+            for col_i, value in enumerate(row_data, start=1):
+                ws1.cell(row=row_i, column=col_i).value = value
+
+        # Data sheet
+        ws2 = wb.create_sheet(f"{TRANSLATIONS[language]['excel_data_sheet']}_{sheetname}")
+        ws2.append(list(df.columns))
+        for r in df.itertuples(index=False):
+            ws2.append(list(r))
+        
+        return True, None
+    except Exception as e:
+        return False, str(e)