# -*- coding: utf-8 -*-
"""Copy of dogs_cats.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1pu75-TcRCtcDPFHn3zA6IqSsH8BM5yka

## Gradio Pets

# New Section
"""

# !pip install -Uqq fastai


#/ default_exp app

from fastai.vision.all import *
import gradio as gr

def is_cat(x): return x[0].isupper()

# path = untar_data(URLs.PETS)/'images'

# dls = ImageDataLoaders.from_name_func('.',
#     get_image_files(path), valid_pct=0.2, seed=42, #giving
#     label_func=is_cat,
#     item_tfms=Resize(192))

# dls.show_batch()

# learn = vision_learner(dls, resnet18, metrics=error_rate)
# learn.fine_tune(3)

# learn.export('model.pkl')

# cell 
learn= load_learner('model.pk1')

categories= ('Dog', 'Cat')

# # function we need to define for gradio

# # predcition is a string and prob ius a

# # grradio wants dict with each category and prob of each

# # zip -- putting together .... dict-- putting into correct format

# # gradio doesnt work with tensors thus need to convert to float


def classify_image(img):
  pred,idx,probs= learn.predict(img)
  return dict(zip(categories, map(float, probs)))

im=PILImage.create('dog.jpg')
im.thumbnail((192,192))
im

learn.predict(im)

classify_image(im)

examples= ['dog.jpg', 'cat.jpg']

gr.Interface(fn=classify_image, inputs=[gr.Image(type="pil")], outputs=[gr.Label(num_top_classes=2)], examples=examples).launch()