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app.py

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+import gradio as gr
+import tensorflow as tf
+
+
+model = tf.keras.models.load_model('fire_model.h5') 
+
+def predict_input_image(img):
+    class_names = ['fire_images', 'non_fire_images']
+    img_4d = img.reshape(-1, 196, 196, 3)
+    prediction = model.predict(img_4d)[0]
+    pred = [1-prediction, prediction]
+#     predlab = classes[pred]
+
+    confidences = {class_names[i]: float(pred[i]) for i in range(2)}
+    print()
+    return confidences
+
+
+image = gr.inputs.Image(shape=(196, 196))
+label = gr.outputs.Label(num_top_classes=1)
+
+
+gr.Interface(fn=predict_input_image, 
+inputs=image, outputs=label,interpretation='default').launch()

fire_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bc0e885b634d6ecfbc0caf1c55dd2bff4ee8a81ff1af2d4fd85adcb27c543a8b
+size 5423712

requirements.txt

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+gradio==3.27.0
+tensorflow==2.10.0