Solar-Y

Solar Panel Visual Inspection System

Detects the visual condition of a solar panel from the uploaded images in runtime

Classes of Data

• Clean
• Dusty
• Bird Drop
• Electrical Damage
• Physical Damage
• Snow Covered

Suggests remedies or suggestive measures for each type of anomaly

Accuracy : 100%

Dataset Link: https://www.kaggle.com/datasets/pythonafroz/solar-panel-images

NOTE: Can also detect images from Google. But in case of outside images, the sensitivity increases for "Clear" class

Directions of use:

• download the files (click on Green "Code" button on top right of the files area in Github)
• extract the files
• open with VScode or Spyder or any IDE of your preference (Not Jupyter Notebooks)
• install the packages that are listed in requirements.txt
• open a terminal in your IDE or in CMD and type streamlit run app.py
• type your email in the terminal if you are using streamlit for the first time
• use the app
• to host, use streamlit services (As Heroku services are discotinued in Nov, 22)

ENJOY 🤩