| import gradio as gr | |
| from keras.models import load_model,Sequential | |
| model = load_model("./Model_2.h5") | |
| class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips'] | |
| def predict_image(img): | |
| img_4d=img.reshape(-1,331,331,3) | |
| prediction=model.predict(img_4d)[0] | |
| return {class_names[i]: float(prediction[i]) for i in range(5)} | |
| image = gr.inputs.Image(shape=(331,331)) | |
| label = gr.outputs.Label(num_top_classes=5) | |
| iface = gr.Interface(fn=predict_image, | |
| inputs=image, | |
| outputs=label, | |
| interpretation='default', | |
| examples=['2480569557_f4e1f0dcb8_n.jpg','3464015936_6845f46f64.jpg','4746668678_0e2693b1b9_n.jpg','4764674741_82b8f93359_n.jpg','5470898169_52a5ab876c_n.jpg'], | |
| title = 'Flower Recognition App', | |
| description= 'Get probability for input image among daisy, dandelion, roses, sunflowers, tulips') | |
| iface.launch() |