Skin Disease Prediction Application

This application is designed to detect skin anomalies and diseases from images using machine learning. It provides functionalities to predict, train, and evaluate a skin disease classification model.

About

This application is created using streamlit, TensorFlow, and other Python libraries. For more details, refer to the code in the repository.

Usage

Predict Tab: Upload an image to run through the machine learning model for detecting skin anomalies. You can choose the model type (MobileNetV2 or ResNet50) and customize the number of prediction results.
Train Tab: Train the model again after replacing the dataset or changing the underlying code. Advanced settings are available for batch size, number of epochs, and an option to override the check for existing model files.
Evaluate Tab: Evaluate the model by testing it on a separate test dataset. It provides insights such as the confusion matrix and evaluation results.

Directory and File Health Check

The application performs a health check on the following directories:

Model Directory
Training Directory
Test Directory
Result Directory
Image Upload Directory
Log Directory

The status of each directory is displayed in the sidebar.

Models Available

Choose between MobileNetV2 and ResNet50 for skin disease prediction.

Overview of Functions

Main Application (app.py)

main(): Main function coordinating the streamlit application.
- Calls directory_status(), directory_info(), and displays health check and dataset info in the sidebar.
- Implements tabs for "Predict," "Train," and "Evaluate" with corresponding functionalities.

Skin Disease Model (_ml.py)

SkinDiseaseModel Class:
- __init__(self, model_type): Initializes the model, sets model type, and creates a LabelEncoder.
- create_label_encoder(self, data_path): Creates a LabelEncoder for class labels.
- get_num_classes(self, data_path): Returns the number of classes in the dataset.
- check_directory_balance(self, train_dir, test_dir): Checks if training and test directories are balanced.
- create_base_model(self): Creates the base model (MobileNetV2 or ResNet50).
- create_skin_disease_model(self, base_model): Adds custom layers to create the skin disease classification model.
- train_model(self, data_path, batch_size, epochs, override): Trains the model on the specified dataset.
- evaluate_model(self, test_path, batch_size, num_predictions): Evaluates the model on the test set.
- predict_class(self, img_path, num_predictions): Predicts skin disease class for a given image.

Additional Modules (_config.py)

_config.py:
- Configuration file containing directory paths, logger setup, and other constants.

Key Features

Predict Tab:
- Allows users to upload an image for skin anomaly detection.
- Provides prediction results with class labels and confidence scores.
- Option to display raw JSON output.
Train Tab:
- Enables training the machine learning model with custom datasets.
- Supports advanced settings such as batch size and number of epochs.
- Displays the most recent training accuracy graph.
Evaluate Tab:
- Evaluates the model performance on a test dataset.
- Presents evaluation results, including a confusion matrix.
Directory and Dataset Health Check:
- Sidebar displays the health status of various directories (e.g., model, training, test).
- Provides insights into the dataset, such as the number of classes and images.
Logging and Configuration:
- Utilizes logging to capture and handle errors during training and evaluation.
- Configuration file (_config.py) centralizes constants and directory paths.

How to Run

In the command prompt, set PIPENV_VENV_IN_PROJECT=1
Run pipenv install
Download dataset from here, unzip and copy it over to dataset folder.
Run the application: streamlit run app.py from scripts/app folder.

Screenshots

Uploading an image for prediction.

Prediction results.

Training the model.

Dependency graph

opencv-python==4.8.1.78
├── numpy [required: >=1.21.2, installed: 1.26.2]
├── numpy [required: >=1.23.5, installed: 1.26.2]
├── numpy [required: >=1.17.0, installed: 1.26.2]
├── numpy [required: >=1.17.3, installed: 1.26.2]
└── numpy [required: >=1.19.3, installed: 1.26.2]
scikit-learn==1.3.2
├── joblib [required: >=1.1.1, installed: 1.3.2]
├── numpy [required: >=1.17.3,<2.0, installed: 1.26.2]
├── scipy [required: >=1.5.0, installed: 1.11.4]
│   └── numpy [required: >=1.21.6,<1.28.0, installed: 1.26.2]
└── threadpoolctl [required: >=2.0.0, installed: 3.2.0]
seaborn==0.13.0
├── matplotlib [required: >=3.3,!=3.6.1, installed: 3.8.2]
│   ├── contourpy [required: >=1.0.1, installed: 1.2.0]
│   │   └── numpy [required: >=1.20,<2.0, installed: 1.26.2]
│   ├── cycler [required: >=0.10, installed: 0.12.1]
│   ├── fonttools [required: >=4.22.0, installed: 4.46.0]
│   ├── kiwisolver [required: >=1.3.1, installed: 1.4.5]
│   ├── numpy [required: >=1.21,<2, installed: 1.26.2]
│   ├── packaging [required: >=20.0, installed: 23.2]
│   ├── pillow [required: >=8, installed: 10.1.0]
│   ├── pyparsing [required: >=2.3.1, installed: 3.1.1]
│   └── python-dateutil [required: >=2.7, installed: 2.8.2]
│       └── six [required: >=1.5, installed: 1.16.0]
├── numpy [required: >=1.20,!=1.24.0, installed: 1.26.2]
└── pandas [required: >=1.2, installed: 2.1.4]
    ├── numpy [required: >=1.23.2,<2, installed: 1.26.2]
    ├── python-dateutil [required: >=2.8.2, installed: 2.8.2]
    │   └── six [required: >=1.5, installed: 1.16.0]
    ├── pytz [required: >=2020.1, installed: 2023.3.post1]
    └── tzdata [required: >=2022.1, installed: 2023.3]
streamlit==1.29.0
├── altair [required: >=4.0,<6, installed: 5.2.0]
│   ├── jinja2 [required: Any, installed: 3.1.2]
│   │   └── MarkupSafe [required: >=2.0, installed: 2.1.3]
│   ├── jsonschema [required: >=3.0, installed: 4.20.0]
│   │   ├── attrs [required: >=22.2.0, installed: 23.1.0]
│   │   ├── jsonschema-specifications [required: >=2023.03.6, installed: 2023.11.2]
│   │   │   └── referencing [required: >=0.31.0, installed: 0.32.0]
│   │   │       ├── attrs [required: >=22.2.0, installed: 23.1.0]
│   │   │       └── rpds-py [required: >=0.7.0, installed: 0.14.1]
│   │   ├── referencing [required: >=0.28.4, installed: 0.32.0]
│   │   │   ├── attrs [required: >=22.2.0, installed: 23.1.0]
│   │   │   └── rpds-py [required: >=0.7.0, installed: 0.14.1]
│   │   └── rpds-py [required: >=0.7.1, installed: 0.14.1]
│   ├── numpy [required: Any, installed: 1.26.2]
│   ├── packaging [required: Any, installed: 23.2]
│   ├── pandas [required: >=0.25, installed: 2.1.4]
│   │   ├── numpy [required: >=1.23.2,<2, installed: 1.26.2]
│   │   ├── python-dateutil [required: >=2.8.2, installed: 2.8.2]
│   │   │   └── six [required: >=1.5, installed: 1.16.0]
│   │   ├── pytz [required: >=2020.1, installed: 2023.3.post1]
│   │   └── tzdata [required: >=2022.1, installed: 2023.3]
│   └── toolz [required: Any, installed: 0.12.0]
├── blinker [required: >=1.0.0,<2, installed: 1.7.0]
├── cachetools [required: >=4.0,<6, installed: 5.3.2]
├── click [required: >=7.0,<9, installed: 8.1.7]
│   └── colorama [required: Any, installed: 0.4.6]
├── gitpython [required: >=3.0.7,<4,!=3.1.19, installed: 3.1.40]
│   └── gitdb [required: >=4.0.1,<5, installed: 4.0.11]
│       └── smmap [required: >=3.0.1,<6, installed: 5.0.1]
├── importlib-metadata [required: >=1.4,<7, installed: 6.11.0]
│   └── zipp [required: >=0.5, installed: 3.17.0]
├── numpy [required: >=1.19.3,<2, installed: 1.26.2]
├── packaging [required: >=16.8,<24, installed: 23.2]
├── pandas [required: >=1.3.0,<3, installed: 2.1.4]
│   ├── numpy [required: >=1.23.2,<2, installed: 1.26.2]
│   ├── python-dateutil [required: >=2.8.2, installed: 2.8.2]
│   │   └── six [required: >=1.5, installed: 1.16.0]
│   ├── pytz [required: >=2020.1, installed: 2023.3.post1]
│   └── tzdata [required: >=2022.1, installed: 2023.3]
├── pillow [required: >=7.1.0,<11, installed: 10.1.0]
├── protobuf [required: >=3.20,<5, installed: 4.23.4]
├── pyarrow [required: >=6.0, installed: 14.0.1]
│   └── numpy [required: >=1.16.6, installed: 1.26.2]
├── pydeck [required: >=0.8.0b4,<1, installed: 0.8.1b0]
│   ├── jinja2 [required: >=2.10.1, installed: 3.1.2]
│   │   └── MarkupSafe [required: >=2.0, installed: 2.1.3]
│   └── numpy [required: >=1.16.4, installed: 1.26.2]
├── python-dateutil [required: >=2.7.3,<3, installed: 2.8.2]
│   └── six [required: >=1.5, installed: 1.16.0]
├── requests [required: >=2.27,<3, installed: 2.31.0]
│   ├── certifi [required: >=2017.4.17, installed: 2023.11.17]
│   ├── charset-normalizer [required: >=2,<4, installed: 3.3.2]
│   ├── idna [required: >=2.5,<4, installed: 3.6]
│   └── urllib3 [required: >=1.21.1,<3, installed: 2.1.0]
├── rich [required: >=10.14.0,<14, installed: 13.7.0]
│   ├── markdown-it-py [required: >=2.2.0, installed: 3.0.0]
│   │   └── mdurl [required: ~=0.1, installed: 0.1.2]
│   └── pygments [required: >=2.13.0,<3.0.0, installed: 2.17.2]
├── tenacity [required: >=8.1.0,<9, installed: 8.2.3]
├── toml [required: >=0.10.1,<2, installed: 0.10.2]
├── tornado [required: >=6.0.3,<7, installed: 6.4]
├── typing-extensions [required: >=4.3.0,<5, installed: 4.9.0]
├── tzlocal [required: >=1.1,<6, installed: 5.2]
│   └── tzdata [required: Any, installed: 2023.3]
├── validators [required: >=0.2,<1, installed: 0.22.0]
└── watchdog [required: >=2.1.5, installed: 3.0.0]
tensorflow==2.15.0
└── tensorflow-intel [required: ==2.15.0, installed: 2.15.0]
    ├── absl-py [required: >=1.0.0, installed: 2.0.0]
    ├── astunparse [required: >=1.6.0, installed: 1.6.3]
    │   ├── six [required: >=1.6.1,<2.0, installed: 1.16.0]
    │   └── wheel [required: >=0.23.0,<1.0, installed: 0.42.0]
    ├── flatbuffers [required: >=23.5.26, installed: 23.5.26]
    ├── gast [required: >=0.2.1,!=0.5.2,!=0.5.1,!=0.5.0, installed: 0.5.4]
    ├── google-pasta [required: >=0.1.1, installed: 0.2.0]
    │   └── six [required: Any, installed: 1.16.0]
    ├── grpcio [required: >=1.24.3,<2.0, installed: 1.60.0]
    ├── h5py [required: >=2.9.0, installed: 3.10.0]
    │   └── numpy [required: >=1.17.3, installed: 1.26.2]
    ├── keras [required: >=2.15.0,<2.16, installed: 2.15.0]
    ├── libclang [required: >=13.0.0, installed: 16.0.6]
    ├── ml-dtypes [required: ~=0.2.0, installed: 0.2.0]
    │   ├── numpy [required: >1.20, installed: 1.26.2]
    │   ├── numpy [required: >=1.23.3, installed: 1.26.2]
    │   └── numpy [required: >=1.21.2, installed: 1.26.2]
    ├── numpy [required: >=1.23.5,<2.0.0, installed: 1.26.2]
    ├── opt-einsum [required: >=2.3.2, installed: 3.3.0]
    │   └── numpy [required: >=1.7, installed: 1.26.2]
    ├── packaging [required: Any, installed: 23.2]
    ├── protobuf [required: >=3.20.3,<5.0.0dev,!=4.21.5,!=4.21.4,!=4.21.3,!=4.21.2,!=4.21.1,!=4.21.0, installed: 4.23.4]
    ├── setuptools [required: Any, installed: 69.0.2]
    ├── six [required: >=1.12.0, installed: 1.16.0]
    ├── tensorboard [required: >=2.15,<2.16, installed: 2.15.1]
    │   ├── absl-py [required: >=0.4, installed: 2.0.0]
    │   ├── google-auth [required: >=1.6.3,<3, installed: 2.25.2]
    │   │   ├── cachetools [required: >=2.0.0,<6.0, installed: 5.3.2]
    │   │   ├── pyasn1-modules [required: >=0.2.1, installed: 0.3.0]
    │   │   │   └── pyasn1 [required: >=0.4.6,<0.6.0, installed: 0.5.1]
    │   │   └── rsa [required: >=3.1.4,<5, installed: 4.9]
    │   │       └── pyasn1 [required: >=0.1.3, installed: 0.5.1]
    │   ├── google-auth-oauthlib [required: >=0.5,<2, installed: 1.2.0]
    │   │   ├── google-auth [required: >=2.15.0, installed: 2.25.2]
    │   │   │   ├── cachetools [required: >=2.0.0,<6.0, installed: 5.3.2]
    │   │   │   ├── pyasn1-modules [required: >=0.2.1, installed: 0.3.0]
    │   │   │   │   └── pyasn1 [required: >=0.4.6,<0.6.0, installed: 0.5.1]
    │   │   │   └── rsa [required: >=3.1.4,<5, installed: 4.9]
    │   │   │       └── pyasn1 [required: >=0.1.3, installed: 0.5.1]
    │   │   └── requests-oauthlib [required: >=0.7.0, installed: 1.3.1]
    │   │       ├── oauthlib [required: >=3.0.0, installed: 3.2.2]
    │   │       └── requests [required: >=2.0.0, installed: 2.31.0]
    │   │           ├── certifi [required: >=2017.4.17, installed: 2023.11.17]
    │   │           ├── charset-normalizer [required: >=2,<4, installed: 3.3.2]
    │   │           ├── idna [required: >=2.5,<4, installed: 3.6]
    │   │           └── urllib3 [required: >=1.21.1,<3, installed: 2.1.0]
    │   ├── grpcio [required: >=1.48.2, installed: 1.60.0]
    │   ├── markdown [required: >=2.6.8, installed: 3.5.1]
    │   ├── numpy [required: >=1.12.0, installed: 1.26.2]
    │   ├── protobuf [required: >=3.19.6,<4.24, installed: 4.23.4]
    │   ├── requests [required: >=2.21.0,<3, installed: 2.31.0]
    │   │   ├── certifi [required: >=2017.4.17, installed: 2023.11.17]
    │   │   ├── charset-normalizer [required: >=2,<4, installed: 3.3.2]
    │   │   ├── idna [required: >=2.5,<4, installed: 3.6]
    │   │   └── urllib3 [required: >=1.21.1,<3, installed: 2.1.0]
    │   ├── setuptools [required: >=41.0.0, installed: 69.0.2]
    │   ├── six [required: >1.9, installed: 1.16.0]
    │   ├── tensorboard-data-server [required: >=0.7.0,<0.8.0, installed: 0.7.2]
    │   └── werkzeug [required: >=1.0.1, installed: 3.0.1]
    │       └── MarkupSafe [required: >=2.1.1, installed: 2.1.3]
    ├── tensorflow-estimator [required: >=2.15.0,<2.16, installed: 2.15.0]
    ├── tensorflow-io-gcs-filesystem [required: >=0.23.1, installed: 0.31.0]
    ├── termcolor [required: >=1.1.0, installed: 2.4.0]
    ├── typing-extensions [required: >=3.6.6, installed: 4.9.0]
    └── wrapt [required: >=1.11.0,<1.15, installed: 1.14.1]

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
app		app
screenshots		screenshots
.gitignore		.gitignore
Pipfile		Pipfile
Pipfile.lock		Pipfile.lock
README.md		README.md

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Skin Disease Prediction Application

About

Usage

Directory and File Health Check

Models Available

Overview of Functions

Main Application (app.py)

Skin Disease Model (_ml.py)

Additional Modules (_config.py)

Key Features

How to Run

Screenshots

Dependency graph

About

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anandukrishnaaa/skin-disease-analysis

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Repository files navigation

Skin Disease Prediction Application

About

Usage

Directory and File Health Check

Models Available

Overview of Functions

Main Application (app.py)

Skin Disease Model (_ml.py)

Additional Modules (_config.py)

Key Features

How to Run

Screenshots

Dependency graph

About

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