solar_fault_model.py

from transformers import pipeline
from PIL import Image

# Load a pre-trained image classification model (can be any model suitable for image classification)
# Here, we use a simple CNN model like ResNet50 from Hugging Face, which can be fine-tuned for this task.
model = pipeline("image-classification", model="google/vit-base-patch16-224-in21k")

# Class labels for your solar panel damage types
damage_classes = ['cracked', 'dusted', 'shaded', 'overheated']

def predict_damage(image_path):
    # Open and preprocess the image
    image = Image.open(image_path)
    
    # Get prediction from the model
    result = model(image)
    
    # Return the damage prediction text (i.e., class with highest confidence)
    predicted_class = result[0]['label']
    confidence = result[0]['score']
    
    # Check the predicted label and map it to the respective damage type
    if predicted_class in damage_classes:
        return f"This panel is {predicted_class} (Confidence: {confidence:.2f})"
    else:
        return "Unable to determine the damage type"