Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+
+# Load the trained model
+model = tf.keras.models.load_model("eff_model.h5")
+
+# Same normalization you used in training
+def preprocess_image(image: Image.Image):
+    image = image.resize((512, 512)).convert("RGB")
+    image = np.array(image).astype(np.float32) / 255.0
+
+    mean = np.array([0.44101639, 0.45513914, 0.40195001])
+    std = np.array([0.28792392, 0.29775171, 0.29840153])
+
+    image = (image - mean) / std
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+def predict(image: Image.Image):
+    processed = preprocess_image(image)
+    prediction = model.predict(processed)[0][0]  # sigmoid output
+
+    label = "🌋 Volcanic Eruption" if prediction > 0.5 else "✅ No Eruption"
+    confidence = f"{prediction:.2%}" if prediction > 0.5 else f"{(1 - prediction):.2%}"
+    return f"{label} (Confidence: {confidence})"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Satellite Image"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Volcanic Eruption Detection",
+    description="Upload a satellite image to detect a volcanic eruption using EfficientNetB7."
+)
+
+if __name__ == "__main__":
+    interface.launch()