first up load

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+
+# Load the trained model
+model = load_model("m32.h5")
+
+# Class labels
+class_labels = [
+    'Cell', 'Cell-Multi', 'Cracking', 'Diode', 'Diode-Multi',
+    'Hot-Spot', 'Hot-Spot-Multi', 'No-Anomaly', 'Offline-Module',
+    'Shadowing', 'Soiling', 'Vegetation'
+]
+
+# Preprocessing and prediction function
+def predict_image(img: Image.Image):
+    img = img.convert("RGB")
+    img = img.resize((24, 40))  # Resize to (width=24, height=40)
+    img_array = np.array(img)
+    img_input = img_array.reshape(1, 40, 24, 3)
+
+    prediction = model.predict(img_input)
+    max_prob = np.max(prediction[0])
+    class_ind = np.argmax(prediction[0])
+    predicted_class = class_labels[class_ind]
+
+    return f"Predicted class: {predicted_class} with probability {max_prob:.4f}"
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Solar Module Anomaly Classifier",
+    description="Upload an infrared image to detect anomalies in solar modules."
+)
+
+interface.launch()

m32.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8568c8ecc5dbc2769b27b627fcba40981107ab70520ffb395590f2e7256209f2
+size 22104488

requirements.txt

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+gradio
+tensorflow
+Pillow
+numpy