update

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load trained model
+model = tf.keras.models.load_model("eurosat_cnn_model.h5")
+
+# Define class labels
+class_labels = [
+    "AnnualCrop", "Forest", "Highway", "Industrial", "Pasture",
+    "PermanentCrop", "Residential", "River", "SeaLake", "HerbaceousVegetation"
+]
+
+# Function to process image and make predictions
+def predict_image(image):
+    image = image.resize((64, 64))  # Resize image
+    img_array = np.array(image) / 255.0  # Normalize
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    
+    prediction = model.predict(img_array)  # Predict
+    return {class_labels[i]: float(prediction[0][i]) for i in range(len(class_labels))}
+
+# Create Gradio Interface
+iface = gr.Interface(fn=predict_image, inputs="image", outputs="label")
+iface.launch()