Create app.py

app.py

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+import os
+import tensorflow as tf
+import gradio as gr
+import numpy as np
+from PIL import Image
+
+# Disable all GPUS
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+current_directory = os.path.abspath(os.path.dirname(__file__))
+
+# Load your pre-trained model
+def load_model():
+    model = tf.keras.models.load_model(os.path.join(current_directory, "model.h5")) # Replace with your model's path
+    return model
+
+model = load_model()
+
+# Define the labels (categories)
+labels = ['Water', 'Cloudy', 'Desert', 'Green Area']
+
+# Function to preprocess the image and predict the class
+def classify_image(image):
+    # Ensure the image is in PIL format
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+    
+    img = image.resize((128, 128)) # Resize the image
+    img = np.array(img) / 255.0 # Normalize the image
+    img = np.expand_dims(img, axis=0) # Add batch dimension
+    prediction = model.predict(img)
+    predicted_class = labels[np.argmax(prediction)]
+
+    # Prepare output with probabilities
+    return {labels[i]: float(prediction[0][i]) for i in range(len(labels))}
+
+# Define the Gradio interface
+image_input = gr.Image(type="pil")  # Use "pil" as the type for PIL images
+label_output = gr.Label(num_top_classes=4)
+
+# Launch the interface
+gr.Interface(fn=classify_image,
+             inputs=image_input,
+             outputs=label_output,
+             title="Satellite Image Classification",
+             description="Classify satellite images into four types: Water, Cloudy, Desert, Green Area").launch()