Upload app.py

app.py

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+import gradio as gr
+#from transformers import pipeline
+from tensorflow.keras.models import load_model
+
+#pipe = pipeline(task="image-classification", model="SuperSecureHuman/Flower-CNN")
+
+model = load_model('./model.h5')
+
+
+def predict_image(img):
+    img_4d = img.reshape(-1, 224, 224, 3)
+    prediction = model.predict(img_4d)[0]
+    return {class_names[i]: float(prediction[i]) for i in range(3)}
+
+
+class_names = ['Crescentia_Cujete', 'Fiddle_Wood', 'Gold_Apple', 'Hill_Mango', 'Indian_Tulip_Tree', 'Mahagony', 'Pala_Indigo_Plant', 'Spanish_Cherry', 'Teak', 'Yellow_Trumpet']
+
+image = gr.inputs.Image(shape=(224, 224))
+
+label = gr.outputs.Label(num_top_classes=3)
+
+gr.Interface(fn=predict_image,
+             title="Tree Classification",
+             description="Tree CNN",
+             inputs=image,
+             outputs=label,
+             live=True,
+             interpretation='default',
+             allow_flagging="never").launch()