# -*- coding: utf-8 -*-
"""app

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/17bhnzAMKk6EBY64ESpv0omIba_RfKJfn
"""

import gradio as gr
import numpy as np
from keras.preprocessing import image

#loading the saved model 
from keras.models import load_model 
model = load_model('project_model.h5')

labels = ['Healthy','Unhealthy'] #classes

def classify_image(inp):
    img = inp.reshape((25,25,3)) #reshape input image
    #img=image.img_to_array(img)
    x=np.expand_dims(img, axis=0)
    images = np.vstack([x])
    if model.predict(images)[0][0] ==1:
      return "Healthy"
    elif model.predict(images)[0][1] ==1:
      return "Unhealthy"
    else:
      return "Error"
    #prediction = model.predict(img).tolist()[2] #prediction
    #return {labels[i]: prediction[i] for i in range(2)} #return classes
title = "Coccidiosis Detection"
image = gr.inputs.Image(shape=(25, 25))
label = gr.outputs.Label(num_top_classes=1)

gr.Interface(fn=classify_image, inputs=image, outputs=label,title=title, capture_session=True).launch(debug=True)


#end