Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+# Load the trained model
+model = tf.keras.models.load_model("preprocessed_model.keras")
+input_size = (224, 224)
+
+# EuroSAT class names
+class_names = [
+    'AnnualCrop', 'Forest', 'HerbaceousVegetation',
+    'Highway', 'Industrial', 'Pasture',
+    'PermanentCrop', 'Residential', 'River',
+    'SeaLake'
+]
+
+# Prediction function
+def classify_image(img: Image.Image):
+    try:
+        # Resize and preprocess the image
+        img_resized = img.resize(input_size)
+        img_array = np.array(img_resized) / 255.0  # Normalize
+        img_array = np.expand_dims(img_array, axis=0)
+
+        # Predict
+        predictions = model.predict(img_array)[0]
+        predicted_index = np.argmax(predictions)
+        predicted_class = class_names[predicted_index]
+        confidence = predictions[predicted_index]
+
+        # Create dictionary of class probabilities
+        result = {
+            class_names[i]: float(predictions[i])
+            for i in range(len(class_names))
+        }
+
+        return predicted_class, confidence, result
+    except Exception as e:
+        return f"Error: {str(e)}", 0.0, {}
+
+# Gradio Interface
+image_input = gr.Image(type="pil", label="Upload EuroSAT Image")
+label_output = gr.Label(num_top_classes=3, label="Top Predictions")
+text_output = gr.Textbox(label="Predicted Class with Confidence")
+
+interface = gr.Interface(
+    fn=lambda img: (
+        classify_image(img)[0] + f" ({classify_image(img)[1]*100:.2f}%)",
+        classify_image(img)[2]
+    ),
+    inputs=image_input,
+    outputs=[text_output, label_output],
+    title="EuroSAT Land Cover Classifier",
+    description="Upload a satellite image (EuroSAT-like) to classify its land cover type using a deep learning model."
+)
+
+# Launch locally or on HF Spaces
+if __name__ == "__main__":
+    interface.launch()