import gradio as gr
from transformers import pipeline

def predict_image(img):
  img_4d=img.reshape(-1,180,180,3)
  prediction=model.predict(img_4d)[0]
  return {class_names[i]: float(prediction[i]) for i in range(4)}

pipe = pipeline(task = 'image-classification', model = "google/vit-base-patch16-224")

image = gr.inputs.Image(shape=(180,180))
label = gr.outputs.Label(num_top_classes=4)

gr.Interface.from_pipeline(pipe,
                           title = "Maize Leaf Disease Detection",
                           description = "Corn Leaf disease classification",
                           fn=predict_image, inputs=image, outputs=label,interpretation='default',
                           allow_flagging = "never").launch(inbrowser=True, debug='True', share='True')