Create app.py

app.py

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+import tensorflow as tf
+import gradio as gr
+import numpy as np
+from tensorflow.keras.preprocessing import image
+
+# Load the model
+model = tf.keras.models.load_model('model.keras')
+
+# Define the class labels
+class_labels = {
+    0: "Buildings",
+    1: "Forest",
+    2: "Glacier",
+    3: "Mountain",
+    4: "Sea",
+    5: "Street"
+}
+
+# Prediction function
+def classify_image(img):
+    # Resize the image to the input size expected by your model
+    img = img.resize((150, 150))  # Replace 150 with your model's input size
+
+    # Convert the image to a numpy array and preprocess it
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0  # Normalize if your model expects normalized inputs
+
+    # Make a prediction
+    predictions = model.predict(img_array)
+    predicted_class = np.argmax(predictions, axis=1)
+
+    # Get the class label from the predicted class index
+    predicted_label = class_labels.get(predicted_class[0], "Unknown")
+
+    # Return the predicted label
+    return f"Predicted class: {predicted_label}"
+
+# Gradio interface
+interface = gr.Interface(
+    fn=classify_image,  # Function to call
+    inputs=gr.Image(type="pil"),  # Input type (image)
+    outputs="text",  # Output type (text)
+    title="CNN Image Classification",
+    description="Upload an image, and the model will classify it into one of the following classes: Buildings, Forest, Glacier, Mountain, Sea, Street."
+)
+
+# Launch the interface
+interface.launch()