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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load your model
+model = tf.keras.models.load_model("disaster_mobilenet.keras")
+
+# Your class names
+CLASS_NAMES = ["collapsed_building", "fire", "flooded_areas", "normal", "traffic_incident"]
+
+def predict(image):
+    # Preprocess
+    image = image.resize((96,96))
+    img_array = np.array(image)/255.0
+    img_array = np.expand_dims(img_array, 0)  # batch
+    
+    # Predict
+    preds = model.predict(img_array)
+    class_idx = np.argmax(preds[0])
+    return {CLASS_NAMES[i]: float(preds[0][i]) for i in range(len(CLASS_NAMES))}
+
+# Gradio interface
+iface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs=gr.Label())
+iface.launch()

requirements.txt

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+tensorflow
+gradio
+pillow
+numpy
+python-multipart