Create model.py

model.py

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+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+# Load model from Hugging Face (replace with your actual model)
+MODEL_NAME = "your-hf-username/solar-panel-fault-detector"
+
+processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
+model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
+model.eval()
+
+def predict_fault(image: Image.Image) -> str:
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    label = model.config.id2label[predicted_class_idx]
+    return label