# -*- coding: utf-8 -*-
"""gradioApp.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/19rOnZUE7tNaMyAjlhnO4vLKb8mojrf2V
"""

# Commented out IPython magic to ensure Python compatibility.
# %%capture
# #Use capture to not show the output of installing the libraries!
# !pip install gradio

import gradio as gr
import numpy as np
import tensorflow as tf

model = tf.keras.models.load_model('/content/drive/MyDrive/project_image_2023_NO/saved_models/saved_model/densenet')
labels = ['Healthy', 'Patient']

def classify_image(inp):
  inp = inp.reshape((-1, 224, 224, 3))
  inp = tf.keras.applications.densenet.preprocess_input(inp)
  prediction = model.predict(inp)
  confidences = {labels[i]: float(prediction[0][i]) for i in range(2)}
  return confidences

gr.Interface(fn=classify_image, 
             inputs=gr.Image(shape=(224, 224)),
             outputs=gr.Label(num_top_classes = 2),
             title="Demo",
             description="Here's a sample image classification. Enjoy!",     
             ).launch(share = True)