# -*- coding: utf-8 -*-
"""Flower-recognition-app.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Oxv1S-3SmLY0i0vKtlsduK8RVcj4EccJ
"""

# Commented out IPython magic to ensure Python compatibility.
# %reload_ext autoreload
# %autoreload 2
# %matplotlib inline
bs = 32 # batch size

version = 2

# Installing all 3 libraries in one go: fastai, fastbook, nbdev
#!pip install -Uqq fastai fastbook nbdev

#!pip install gradio==3.50.0

from fastai import *
#from fastbook import *
from fastai.vision.all import *

# Commented out IPython magic to ensure Python compatibility.
# %cd /content/drive/My Drive/Mastercourse/Flower recognizer

flower_labels = [
        'Beli flower',
        'Gada flower',
        'Joba flower',
        'Kamini flower',
        'Kodom flower',
        'Palash flower',
        'Rose flower',
        'Sheuli flower',
        'Sunflower flower',
        'Water Lily'
]

img_path = 'test_images'
model_path = f'models/flower-recognizer-v{version}.pkl'

#load the model from the pickle file
model = load_learner(model_path)

# Defining a function to recognize an image
def recognize_image(image):
  pred, idx, probs = model.predict(image)
  return dict(zip(flower_labels, map(float, probs)))

img = PILImage.create(f'test_images/unknown_flower_00.jpg')
img.thumbnail((192,192))
img

recognize_image(img)

import gradio as gr

image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
examples = [
    'test_images/unknown_flower_00.jpg',
    'test_images/unknown_flower_01.jpg',
    'test_images/unknown_flower_02.jpg',
    'test_images/unknown_flower_03.jpg',
    'test_images/unknown_flower_04.jpg',
    'test_images/unknown_flower_05.jpg',
    ]

iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label, examples=examples)
iface.launch(share=True)