🌍 EcoImpact AI

Predicting environmental consequences of mineral resource development using artificial intelligence
Прогнозирование экологических последствий разработки минеральных ресурсов с использованием искусственного интеллекта

🌐 Language Navigation | Навигация по языкам

Language	Section
🇺🇸 English	Click here to jump to English version
🇷🇺 Русский	Нажмите здесь для перехода к русской версии

📑 Table of Contents | Содержание

🇷🇺 Русские разделы:

📖 О проекте
🎯 Цели проекта
✨ Ключевые возможности
🛠️ Технологический стек
🚀 Быстрый старт
🏆 Образовательная ценность

🇺🇸 English Version

📖 About the Project

EcoImpact AI is an innovative web application developed by 9th-grade high school students for a project competition. Our tool uses artificial intelligence to assess the environmental impact of mining operations and mineral resource development projects.

The application analyzes various factors such as:

💧 Water contamination risks
🌬️ Air quality degradation
🌱 Land and biodiversity impact

Our AI model provides comprehensive risk scores and detailed reports to help environmental engineers and decision-makers make informed choices about mining projects.

🎯 Project Goals

Create an accessible tool for environmental impact assessment
Demonstrate the power of AI in environmental protection
Provide educational value about sustainable mining practices
Show how technology can support environmental conservation

✨ Key Features

🤖 AI-Powered Analysis: Advanced machine learning for environmental risk assessment
📊 Risk Scoring System: Numerical scores from 0-10 for different environmental factors
📄 PDF Report Generation: Professional reports ready for stakeholders
🌐 User-Friendly Interface: Simple web interface accessible to everyone
📈 Real-Time Processing: Instant analysis and results
🔍 Detailed Breakdowns: Category-specific environmental impact analysis

🛠️ Technology Stack

Component	Technology	Purpose
Backend	Python Flask	Web application framework
AI Model	LM Studio (Local)	Environmental analysis AI
Frontend	HTML/CSS/JavaScript	User interface
PDF Generation	WeasyPrint	Report creation
Logging	Python logging	Application monitoring

📊 System Architecture

Main Application Flow

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graph TB
    subgraph Client["🖥️ Client Side"]
        A["👤 User Input Form"]
        B["🌐 Web Browser"]
        C["📄 PDF Download"]
    end
    
    subgraph Server["🖥️ Server Side"]
        D["🐍 Flask Application"]
        E["📊 Data Processor"]
        F["📋 Form Validator"]
        G["📝 Logging System"]
    end
    
    subgraph AI["🤖 AI Processing"]
        H["🧠 LM Studio Server"]
        I["🔍 Environmental Analyzer"]
        J["📈 Risk Calculator"]
    end
    
    subgraph Output["📤 Output Generation"]
        K["🎯 JSON Results"]
        L["📊 Web Dashboard"]
        M["📄 PDF Report"]
    end
    
    A --> D
    D --> F
    F --> E
    E --> H
    H --> I
    I --> J
    J --> K
    K --> L
    K --> M
    L --> B
    M --> C
    D --> G
    
    style A fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style B fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style C fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style D fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style E fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style F fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style G fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
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    style I fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style J fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style K fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style L fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style M fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style Client fill:#2d2d2d,stroke:#ffffff,stroke-width:3px,color:#ffffff
    style Server fill:#2d2d2d,stroke:#ffffff,stroke-width:3px,color:#ffffff
    style AI fill:#2d2d2d,stroke:#ffffff,stroke-width:3px,color:#ffffff
    style Output fill:#2d2d2d,stroke:#ffffff,stroke-width:3px,color:#ffffff

Data Flow Architecture

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flowchart LR
    subgraph Input["📥 Input Data"]
        A1["🗺️ Location"]
        A2["⛏️ Mining Type"]
        A3["🏗️ Project Scale"]
        A4["🌿 Environment Data"]
    end
    
    subgraph Processing["⚙️ AI Processing"]
        B1["📊 Data Validation"]
        B2["🧮 Feature Extraction"]
        B3["🤖 ML Analysis"]
        B4["📈 Risk Calculation"]
    end
    
    subgraph Analysis["🔬 Environmental Analysis"]
        C1["💧 Water Impact<br/>Score: 0-10"]
        C2["🌬️ Air Quality<br/>Score: 0-10"]
        C3["🌱 Biodiversity<br/>Score: 0-10"]
        C4["🎯 Overall Risk<br/>Score: 0-10"]
    end
    
    subgraph Output["📤 Results"]
        D1["📊 Interactive Dashboard"]
        D2["📄 PDF Report"]
        D3["📈 Risk Visualization"]
    end
    
    Input --> Processing
    Processing --> Analysis
    Analysis --> Output
    
    style A1 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style A2 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style A3 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style A4 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style B1 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style B2 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style B3 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style B4 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style C1 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style C2 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style C3 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style C4 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style D1 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style D2 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style D3 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style Input fill:#2d2d2d,stroke:#ffffff,stroke-width:3px,color:#ffffff
    style Processing fill:#2d2d2d,stroke:#ffffff,stroke-width:3px,color:#ffffff
    style Analysis fill:#2d2d2d,stroke:#ffffff,stroke-width:3px,color:#ffffff
    style Output fill:#2d2d2d,stroke:#ffffff,stroke-width:3px,color:#ffffff

🚀 Quick Start

Clone the Repository

git clone https://github.com/Ping-2o/daryn EcoImpactAI
cd EcoImpactAI

Install Dependencies
```
pip3 install -r requirements.txt
```
Setup LM Studio
- Download and install LM Studio
- Load an AI model suitable for text analysis (recommended: Granite 4.0 H Tiny or similar)
- Start the local server on http://127.0.0.1:1234 (use the "Local Server" tab in LM Studio)
- Ensure the server is running before starting the Flask application
Run the Application
```
python app.py
```
Access the Web Interface
- Open your browser and go to http://127.0.0.1:5000
- Fill in the mining project details
- Get instant AI-powered environmental impact assessment!

📊 Risk Assessment Methodology

Environmental Risk Categories

%%{init: {
  'theme':'dark', 
  'themeVariables': {
    'pie1':'#0077be', 
    'pie2':'#87CEEB', 
    'pie3':'#228B22', 
    'pie4':'#8B4513', 
    'pie5':'#808080',
    'pieTitleTextSize':'18px', 
    'pieTitleTextColor':'#ffffff', 
    'pieSectionTextSize':'16px', 
    'pieSectionTextColor':'#ffffff', 
    'pieOuterStrokeWidth':'2px', 
    'pieOuterStrokeColor':'#ffffff'
  }
}}%%
pie title Environmental Impact Distribution
    "Water Contamination" : 35
    "Air Quality" : 25
    "Biodiversity Loss" : 20
    "Soil Degradation" : 15
    "Noise Pollution" : 5

Risk Scoring System

%%{init: {'theme':'dark', 'themeVariables': {'primaryColor':'#1a1a1a','primaryTextColor':'#ffffff','primaryBorderColor':'#ffffff','lineColor':'#ffffff','secondaryColor':'#2d2d2d','tertiaryColor':'#404040','background':'#1a1a1a','mainBkg':'#1a1a1a','secondBkg':'#2d2d2d','tertiaryBkg':'#404040'}}}%%
graph LR
    subgraph Scoring["📊 Risk Score Levels"]
        A["0-2: 🟢 Low Risk"]
        B["3-4: 🟡 Moderate Risk"]
        C["5-6: 🟠 Medium Risk"]
        D["7-8: 🔴 High Risk"]
        E["9-10: 🚨 Critical Risk"]
    end
    
    subgraph Factors["🔍 Assessment Factors"]
        F1["📍 Location Sensitivity"]
        F2["⚖️ Project Scale"]
        F3["⏱️ Duration"]
        F4["🌿 Ecosystem Type"]
        F5["💧 Water Proximity"]
    end
    
    Factors --> Scoring
    
    style A fill:#1a1a1a,stroke:#4caf50,stroke-width:3px,color:#ffffff
    style B fill:#1a1a1a,stroke:#ffeb3b,stroke-width:3px,color:#ffffff
    style C fill:#1a1a1a,stroke:#ff9800,stroke-width:3px,color:#ffffff
    style D fill:#1a1a1a,stroke:#f44336,stroke-width:3px,color:#ffffff
    style E fill:#1a1a1a,stroke:#9c27b0,stroke-width:3px,color:#ffffff
    style F1 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style F2 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style F3 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style F4 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style F5 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style Scoring fill:#2d2d2d,stroke:#ffffff,stroke-width:3px,color:#ffffff
    style Factors fill:#2d2d2d,stroke:#ffffff,stroke-width:3px,color:#ffffff

Sample Risk Analysis Results

%%{init: {'theme':'dark', 'themeVariables': {'xyChart': {'backgroundColor': '#1a1a1a', 'titleColor': '#ffffff', 'xAxisTitleColor': '#ffffff', 'xAxisLabelColor': '#ffffff', 'yAxisTitleColor': '#ffffff', 'yAxisLabelColor': '#ffffff', 'plotColorPalette': '#ffffff,#cccccc,#999999,#666666,#333333'}}}%%
xychart-beta
    title "Environmental Risk Analysis - Sample Project"
    x-axis ["Water", "Air", "Biodiversity", "Soil", "Noise"]
    y-axis "Risk Score (0-10)" 0 --> 10
    bar [7.2, 5.8, 6.5, 4.3, 3.1]

AI Processing Timeline

gantt
    title AI Analysis Process Timeline
    dateFormat YYYY-MM-DD
    axisFormat %H:%M:%S
    
    section Data Input
        Form Validation    :milestone, m1, 2024-01-01, 0d
        Data Preprocessing :active, task1, after m1, 1d
    
    section AI Analysis  
        Model Loading      :task2, after task1, 2d
        Risk Calculation   :crit, task3, after task2, 7d
        Result Generation  :task4, after task3, 3d
    
    section Output
        JSON Response      :milestone, m2, after task4, 0d
        PDF Generation     :task5, after m2, 4d
        Display Results    :task6, after task5, 2d

Processing Speed Breakdown

%%{init: {
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    'pie5':'#9b59b6',
    'pieTitleTextSize':'18px', 
    'pieTitleTextColor':'#ffffff', 
    'pieSectionTextSize':'16px', 
    'pieSectionTextColor':'#ffffff', 
    'pieOuterStrokeWidth':'2px', 
    'pieOuterStrokeColor':'#ffffff'
  }
}}%%
pie title "AI Processing Time Distribution (Total: ~14 seconds)"
    "Model Loading" : 23
    "Risk Calculation" : 32
    "Data Processing" : 18
    "Result Generation" : 14
    "PDF Creation" : 13

System Response Flow

%%{init: {'theme':'dark', 'themeVariables': {'primaryColor':'#1a1a1a','primaryTextColor':'#ffffff','primaryBorderColor':'#ffffff','lineColor':'#ffffff','secondaryColor':'#2d2d2d','tertiaryColor':'#404040','background':'#1a1a1a','mainBkg':'#1a1a1a','secondBkg':'#2d2d2d','tertiaryBkg':'#404040'}}}%%
flowchart LR
    A["⏱️ 0s<br/>User Submit"] --> B["⏱️ 2s<br/>Data Validated"]
    B --> C["⏱️ 5s<br/>Model Ready"]
    C --> D["⏱️ 12s<br/>AI Analysis"]
    D --> E["⏱️ 15s<br/>Results Ready"]
    E --> F["⏱️ 20s<br/>PDF Generated"]
    F --> G["⏱️ 22s<br/>Complete"]
    
    style A fill:#1a1a1a,stroke:#ff5722,stroke-width:3px,color:#ffffff
    style B fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style C fill:#1a1a1a,stroke:#ffc107,stroke-width:3px,color:#ffffff
    style D fill:#1a1a1a,stroke:#4caf50,stroke-width:3px,color:#ffffff
    style E fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style F fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
    style G fill:#1a1a1a,stroke:#2196f3,stroke-width:3px,color:#ffffff

📈 Usage Examples

Input Parameters

Project Location: GPS coordinates or region name
Mining Type: Surface mining, underground, etc.
Resource Type: Coal, metals, minerals
Project Scale: Small, medium, large operations
Environmental Sensitivity: Nearby water sources, protected areas

Sample Output

{
  "overall_risk_score": 6.8,
  "summary": "The proposed mining operation presents moderate to high environmental risks...",
  "risks": [
    {
      "category": "Water Contamination",
      "score": 7.2,
      "details": "High risk due to proximity to groundwater sources..."
    }
  ]
}

📸 Suggested Photos and Illustrations

For documentation and presentation:

Screenshots needed:
- Main web interface with form fields
- Results page showing risk scores and analysis
- PDF report example
- AI model configuration in LM Studio
Diagrams to create:
- System architecture flowchart
- Environmental impact assessment process
- Risk scoring methodology visualization
- Data flow between components
Additional visual elements:
- Before/after environmental impact comparisons
- Charts showing different risk levels
- Mining operation types illustrated
- Environmental protection infographics

🏆 Educational Value

This project demonstrates:

STEM Integration: Combining computer science, environmental science, and mathematics
Real-World Problem Solving: Addressing actual environmental challenges
AI Applications: Practical use of machine learning in environmental protection
Web Development Skills: Full-stack development experience
Data Analysis: Working with complex environmental datasets

🌟 Future Enhancements

🗺️ Interactive Maps: Visualize mining locations and environmental risks
📱 Mobile App: Extend access to field researchers
🌍 Multi-language Support: Expand to more languages
📊 Advanced Analytics: Historical trend analysis
🤝 API Integration: Connect with external environmental databases

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🇷🇺 Русская версия

📖 О проекте

EcoImpact AI — это инновационное веб-приложение, разработанное учениками 9 класса для конкурса проектов. Наш инструмент использует искусственный интеллект для оценки воздействия горнодобывающих операций и проектов разработки минеральных ресурсов на окружающую среду.

Приложение анализирует различные факторы, такие как:

💧 Риски загрязнения воды
🌬️ Ухудшение качества воздуха
🌱 Воздействие на землю и биоразнообразие

Наша модель ИИ предоставляет комплексные оценки рисков и подробные отчёты, помогая инженерам-экологам и лицам, принимающим решения, делать осознанный выбор в отношении горнодобывающих проектов.

🎯 Цели проекта

Создать доступный инструмент для оценки воздействия на окружающую среду
Продемонстрировать возможности ИИ в области защиты окружающей среды
Обеспечить образовательную ценность в области устойчивых методов добычи
Показать, как технологии могут поддержать сохранение окружающей среды

✨ Ключевые возможности

🤖 Анализ на основе ИИ: Продвинутое машинное обучение для оценки экологических рисков
📊 Система оценки рисков: Численные оценки от 0 до 10 для различных экологических факторов
📄 Генерация PDF-отчётов: Профессиональные отчёты для заинтересованных сторон
🌐 Удобный интерфейс: Простой веб-интерфейс, доступный каждому
📈 Обработка в реальном времени: Мгновенный анализ и результаты
🔍 Подробные разбивки: Анализ воздействия на окружающую среду по категориям

🛠️ Технологический стек

Компонент	Технология	Назначение
Бэкенд	Python Flask	Фреймворк веб-приложения
Модель ИИ	LM Studio (Локально)	ИИ для экологического анализа
Фронтенд	HTML/CSS/JavaScript	Пользовательский интерфейс
Генерация PDF	WeasyPrint	Создание отчётов
Логирование	Python logging	Мониторинг приложения

🚀 Быстрый старт

Клонирование репозитория

git clone https://github.com/Ping-2o/daryn EcoImpactAI
cd EcoImpactAI

Установка зависимостей
```
pip3 install -r requirements.txt
```
Настройка LM Studio
- Скачайте и установите LM Studio
- Загрузите модель ИИ, подходящую для анализа текста (рекомендуется: Granite 4.0 H Tiny или аналог)
- Запустите локальный сервер на http://127.0.0.1:1234 (используйте вкладку "Local Server" в LM Studio)
- Убедитесь, что сервер запущен перед запуском Flask-приложения
Запуск приложения
```
python app.py
```
Доступ к веб-интерфейсу
- Откройте браузер и перейдите на http://127.0.0.1:5000
- Заполните детали горнодобывающего проекта
- Получите мгновенную оценку воздействия на окружающую среду на основе ИИ!

📊 Примеры использования

Входные параметры

Местоположение проекта: GPS-координаты или название региона
Тип добычи: Открытая разработка, подземная добыча и др.
Тип ресурса: Уголь, металлы, минералы
Масштаб проекта: Малые, средние, крупные операции
Экологическая чувствительность: Близлежащие водные источники, охраняемые территории

🏆 Образовательная ценность

Этот проект демонстрирует:

Интеграцию STEM: Сочетание информатики, экологии и математики
Решение реальных проблем: Обращение к актуальным экологическим вызовам
Применение ИИ: Практическое использование машинного обучения в защите окружающей среды
Навыки веб-разработки: Опыт полноценной разработки
Анализ данных: Работа со сложными экологическими наборами данных

🌟 Будущие улучшения

🗺️ Интерактивные карты: Визуализация местоположений добычи и экологических рисков
📱 Мобильное приложение: Расширение доступа для полевых исследователей
🌍 Многоязычная поддержка: Расширение на большее количество языков
📊 Продвинутая аналитика: Анализ исторических тенденций
🤝 Интеграция API: Подключение к внешним экологическим базам данных

📄 Лицензия

Этот проект лицензирован под лицензией MIT — см. файл LICENSE для подробностей.

📊 Project Statistics & Performance | Статистика проекта и производительность

Development Metrics

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pie title Code Distribution by Language
    "Python (Backend)" : 60
    "HTML/CSS (Frontend)" : 25
    "JavaScript (UI)" : 10
    "Configuration" : 5

Technology Stack Usage

%%{init: {'theme':'dark', 'themeVariables': {'primaryColor':'#1a1a1a','primaryTextColor':'#ffffff','primaryBorderColor':'#ffffff','lineColor':'#ffffff','secondaryColor':'#2d2d2d','tertiaryColor':'#404040','background':'#1a1a1a','mainBkg':'#1a1a1a','secondBkg':'#2d2d2d','tertiaryBkg':'#404040'}}}%%
graph TB
    subgraph Frontend["🖥️ Frontend - 35%"]
        A1["HTML5 - 15%"]
        A2["CSS3 - 10%"]
        A3["JavaScript - 10%"]
    end
    
    subgraph Backend["⚙️ Backend - 45%"]
        B1["Python Flask - 30%"]
        B2["API Integration - 10%"]
        B3["PDF Generation - 5%"]
    end
    
    subgraph AI_ML["🤖 AI/ML - 15%"]
        C1["LM Studio Integration - 10%"]
        C2["Model Configuration - 5%"]
    end
    
    subgraph Tools["🛠️ Development Tools - 5%"]
        D1["Version Control - 2%"]
        D2["Documentation - 2%"]
        D3["Testing - 1%"]
    end
    
    style A1 fill:#1a1a1a,stroke:#ffffff,stroke-width:2px,color:#ffffff
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    style Frontend fill:#2d2d2d,stroke:#4fc3f7,stroke-width:3px,color:#ffffff
    style Backend fill:#2d2d2d,stroke:#ab47bc,stroke-width:3px,color:#ffffff
    style AI_ML fill:#2d2d2d,stroke:#ffb74d,stroke-width:3px,color:#ffffff
    style Tools fill:#2d2d2d,stroke:#81c784,stroke-width:3px,color:#ffffff

Key Project Statistics

Metric	Value	Details
📝 Lines of Code	~1,200+	Well-structured and documented
🌐 Languages	4	Python, HTML, CSS, JavaScript
📦 Dependencies	8	Flask, WeasyPrint, Requests, etc.
⏱️ Development Time	3 months	Full academic semester project
👥 Team Size	2-4 students	9th grade high school team
🎯 Target Audience	Environmental engineers, researchers, students
🔧 Features	6 major	AI analysis, PDF reports, web interface, etc.
📊 Risk Categories	5 types	Water, Air, Biodiversity, Soil, Noise
🧪 Test Cases	20+	Comprehensive testing scenarios
📱 Platforms	Cross-platform	Windows, macOS, Linux compatible

Environmental Impact Categories Analysis

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quadrantChart
    title Environmental Risk Assessment Matrix
    x-axis Low Impact --> High Impact
    y-axis Low Probability --> High Probability
    
    quadrant-1 Monitor
    quadrant-2 Mitigate
    quadrant-3 Accept
    quadrant-4 Avoid
    
    Water Contamination: [0.8, 0.7]
    Air Pollution: [0.6, 0.5]
    Biodiversity Loss: [0.7, 0.8]
    Soil Degradation: [0.5, 0.6]
    Noise Impact: [0.3, 0.4]

🤝 Contributing | Участие в разработке

We welcome contributions from the community! Here's how you can help:

How to Contribute:

Fork the repository
Create a feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

Contribution Areas:

🐛 Bug fixes and issue resolution
✨ New features and enhancements
📝 Documentation improvements
🌍 Translations and localization
🧪 Test coverage expansion
💡 Suggestions and ideas

Мы приветствуем участие сообщества в разработке! Вот как вы можете помочь:

Как внести свой вклад:

Сделайте форк репозитория
Создайте ветку для новой функции (git checkout -b feature/AmazingFeature)
Зафиксируйте изменения (git commit -m 'Add some AmazingFeature')
Отправьте в ветку (git push origin feature/AmazingFeature)
Откройте Pull Request

Области участия:

🐛 Исправление ошибок и решение проблем
✨ Новые функции и улучшения
📝 Улучшение документации
🌍 Переводы и локализация
🧪 Расширение тестового покрытия
💡 Предложения и идеи

This project was created as part of a high school STEM education initiative to promote environmental awareness through technology.

Этот проект был создан в рамках инициативы STEM-образования в средней школе для повышения экологической осведомлённости через технологии.

Name		Name	Last commit message	Last commit date
static		static
templates		templates
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
app.py		app.py
requirements.txt		requirements.txt
Титульный лист.docx		Титульный лист.docx

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