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()