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.streamlit/config.toml

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+[global]
+emailCapture = false

Dockerfile

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+# Используем официальный образ Python
+FROM python:3.9-slim
+
+# Обновляем систему и устанавливаем необходимые пакеты
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Задаём рабочую директорию
+WORKDIR /app
+
+# Копируем файл зависимостей и устанавливаем их
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Копируем все файлы проекта
+COPY . .
+
+# Открываем порт Streamlit (по умолчанию 8501)
+EXPOSE 8501
+
+# Запускаем приложение
+CMD ["streamlit", "run", "app.py", "--server.enableCORS", "false"]

LICENSE

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README.md

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----
-license: cc0-1.0
----
+# Skin-ai
+
+Skin-ai - это приложение на Streamlit для классификации кожных заболеваний с использованием обученной нейросети на PyTorch. Архитектуру использвовал ResNet-50 и обучил с разморозкой весов.
+
+## Функционал
+- Загрузка изображения кожи через веб-интерфейс
+- Обнаружение и классификация заболевания
+- Вывод предсказанного класса и уровня уверенности модели
+
+### Датасет для обучения модели был взят отсюда 
+https://www.kaggle.com/datasets/pacificrm/skindiseasedataset
+## Установка и запуск
+
+### Локальный запуск
+1. Клонируйте репозиторий:
+    ```sh
+    git clone https://github.com/username/Skin-ai.git
+    cd Skin-ai
+    ```
+2. Установите зависимости:
+    ```sh
+    pip install -r requirements.txt
+    ```
+3. Запустите приложение:
+    ```sh
+    streamlit run app.py
+    ```
+
+### Запуск через Docker
+1. Соберите Docker-образ:
+    ```sh
+    docker build -t skin-ai .
+    ```
+2. Запустите контейнер:
+    ```sh
+    docker run -p 8501:8501 skin-ai
+    ```
+3. Откройте в браузере:
+    ```
+    http://localhost:8501
+    ```
+
+## Структура проекта
+```
+Skin-ai/
+│── .github/workflows/      # GitHub Actions для CI/CD
+│── .streamlit/             # Конфигурация Streamlit
+│── Dockerfile              # Файл для сборки Docker-образа
+│── LICENSE                 # Лицензия проекта
+│── README.md               # Документация проекта
+│── app.py                  # Основной код приложения
+│── labels.txt              # Файл с метками классов
+│── requirements.txt        # Список зависимостей
+│── skin_disease_model_jit.pt # JIT-запакованная модель PyTorch
+```
+
+My telegram @eralyf

app.py

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+import streamlit as st
+import torch
+from torchvision import transforms
+from PIL import Image
+import os
+
+# Настройки страницы
+st.set_page_config(
+    page_title="Классификация кожных заболеваний",
+    page_icon="🩺",
+    layout="centered",
+    initial_sidebar_state="collapsed"
+)
+
+# Заголовок
+st.title("🩺 Классификация кожных заболеваний")
+
+# Предупреждение
+st.warning(
+    "⚠️ **Важно!** Данное приложение использует искусственный интеллект для анализа изображений, "
+    "но оно **не является медицинским инструментом**. Результаты предсказания могут быть неточными. "
+    "Для точной диагностики обратитесь к врачу-специалисту."
+)
+
+st.markdown("Загрузите изображение кожи, чтобы получить предсказание.")
+
+# Ввод путей к модели и labels.txt
+model_path = st.text_input("Введите путь к модели", "/home/eraly/Skin-ai/skin_disease_model_jit.pt")
+labels_path = st.text_input("Введите путь к файлу labels.txt", "/home/eraly/Skin-ai/labels.txt")
+
+# Проверка наличия модели
+if not os.path.exists(model_path):
+    st.error(f"Модель не найдена: {model_path}")
+    st.stop()
+
+# Загрузка модели
+model = torch.jit.load(model_path, map_location=torch.device('cpu'))
+model.eval()
+
+# Проверка наличия labels.txt
+if not os.path.exists(labels_path):
+    st.error("Файл меток (labels.txt) не найден.")
+    st.stop()
+
+with open(labels_path, "r") as f:
+    labels = [line.strip() for line in f.readlines()]
+
+# Предобработка изображений
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225]),
+])
+
+# Загрузка изображения
+uploaded_file = st.file_uploader("Выберите изображение", type=["jpg", "jpeg", "png"], key="file_uploader")
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")  # Принудительно переводим в RGB
+    st.image(image, caption="Загруженное изображение", use_container_width=True)
+
+    # Отображение прогресса загрузки
+    progress_bar = st.progress(0)
+    for i in range(100):
+        progress_bar.progress(i + 1)
+
+    # Предсказание
+    try:
+        with st.spinner("Модель обрабатывает изображение..."):
+            image_tensor = preprocess(image).unsqueeze(0)
+            with torch.no_grad():
+                output = model(image_tensor)
+
+            scores = torch.nn.functional.softmax(output[0], dim=0)
+            max_score_index = torch.argmax(scores).item()
+            predicted_class = labels[max_score_index]
+            confidence = scores[max_score_index].item()
+
+        # Вывод результата
+        st.markdown(f"""
+            ### 🏥 Результат анализа:
+            - **Результат:** `{predicted_class}`
+            - **Вероятность результата:** `{confidence:.2f}`
+        """)
+    except Exception as e:
+        st.error(f"Ошибка при классификации: {e}")
+
+else:
+    st.info("Пожалуйста, загрузите изображение.")
+

labels.txt

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+Acne - Акне (угревая сыпь)  
+Actinic Keratosis - Актинический кератоз  
+Benign Tumors - Доброкачественные опухоли  
+Bullous - Буллезные заболевания  
+Candidiasis - Кандидоз  
+Drug Eruption - Лекарственная сыпь  
+Eczema - Экзема  
+Infestations/Bites - Паразитарные инфекции / укусы  
+Lichen - Лишай  
+Lupus - Волчанка  
+Moles - Родинки  
+Psoriasis - Псориаз  
+Rosacea - Розацеа (розовые угри)  
+Seborrheic Keratoses - Себорейный кератоз  
+Skin Cancer - Рак кожи  
+Sun/Sunlight Damage - Повреждение кожи солнцем  
+Tinea - Грибковые инфекции кожи (Тинея)  
+Unknown/Normal - Неизвестно / Норма  
+Vascular Tumors - Сосудистые опухоли  
+Vasculitis - Васкулит  
+Vitiligo - Витилиго  
+Warts - Бородавки  
+

requirements.txt

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+torch==1.13.0
+torchvision==0.14.0
+pillow==9.5.0
+streamlit==1.26.0