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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/solar_panel_defect_model.onnx.data filter=lfs diff=lfs merge=lfs -text

app.py

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+import onnxruntime as ort
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+import gradio as gr
+
+
+# Path to ONNX model
+ONNX_PATH = "models/solar_panel_defect_model.onnx"
+
+# Load ONNX runtime session
+ort_session = ort.InferenceSession(ONNX_PATH, providers=["CPUExecutionProvider"])
+
+print("✅ ONNX model loaded")
+
+# Class labels (VERY IMPORTANT: order must match training)
+CLASS_NAMES = [
+    'Bird-drop',
+    'Clean',
+    'Dusty',
+    'Electrical-damage',
+    'Physical-Damage',
+    'Snow-Covered'
+]
+
+# Same preprocessing as training
+IMG_SIZE = 224
+
+preprocess = transforms.Compose([
+    transforms.Resize((IMG_SIZE, IMG_SIZE)),
+    transforms.Grayscale(num_output_channels=3),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+])
+
+def predict_image(pil_image):
+    """
+    Input: PIL Image
+    Output: (predicted_class, confidence, full_probs_dict)
+    """
+    img = preprocess(pil_image).unsqueeze(0).numpy().astype(np.float32)
+
+    # ONNX inference
+    outputs = ort_session.run(
+        None,
+        {"input_image": img}
+    )[0]
+
+    # Softmax
+    exp_scores = np.exp(outputs)
+    probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
+
+    probs = probs[0]
+    pred_idx = int(np.argmax(probs))
+
+    predicted_class = CLASS_NAMES[pred_idx]
+    confidence = float(probs[pred_idx])
+
+    prob_dict = {CLASS_NAMES[i]: float(probs[i]) for i in range(len(CLASS_NAMES))}
+
+    return predicted_class, confidence, prob_dict
+
+
+print("✅ Inference pipeline ready")
+
+
+# STEP G3: Gradio Interface
+
+def gradio_predict(image):
+    pred_class, confidence, prob_dict = predict_image(image)
+
+    confidence_percent = f"{confidence * 100:.2f}%"
+
+    return pred_class, confidence_percent, prob_dict
+
+
+iface = gr.Interface(
+    fn=gradio_predict,
+    inputs=gr.Image(type="pil", label="Upload Solar Panel Image (Thermal / IR / RGB)"),
+    outputs=[
+        gr.Textbox(label="Predicted Defect Class"),
+        gr.Textbox(label="Confidence"),
+        gr.Label(label="All Class Probabilities")
+    ],
+    title="AI-Powered Solar Panel Defect Detection",
+    description=(
+        "Upload any solar panel image (thermal, infrared, or RGB). "
+        "The AI model classifies defects such as soiling, electrical damage, "
+        "physical damage, snow coverage, or clean panels."
+    ),
+    examples=None
+)
+
+iface.launch(debug=True)

models/solar_panel_defect_model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ab00b00767429a687a12fbf9b41ada718c8a3457313c9f788c7e437126981187
+size 603578

models/solar_panel_defect_model.onnx.data

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76814cf9528fda3433229d1ed5422635ce29e15ae19c14db49eefd70596fe0f5
+size 16056320

requirements.txt

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+onnxruntime
+torch
+torchvision
+gradio
+Pillow
+numpy
+torchaudio