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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() |