app.py

# -*- coding: utf-8 -*-
"""Biome_Classifier_4.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/18IX5V6Qgf-WhhfFRvT9KZZXu5_dCs4pT
"""

from google.colab import drive
drive.mount('/content/drive')

# Commented out IPython magic to ensure Python compatibility.
# #hide
# %%capture
# ! [ -e /content ] && pip install -Uqq fastbook
# ! pip install gradio==3.50
# ! pip install nbdev
# 
# import fastbook
# fastbook.setup_book

#hide
from fastbook import *
from fastai.vision.widgets import *
import skimage

gpath = '/content/drive/MyDrive/'
learn = load_learner(gpath +'/BiomeClassifierModel.pkl')

labels = learn.dls.vocab

def predict(img):
  img = PILImage.create(img)
  pred,pred_idx,probs = learn.predict(img)
  return {labels[i]: float(probs[i]) for i in range(len(labels))}

import gradio as gr
title = "Biome Classifier"
description = "World's first biome classifier powered by Artificial Intelligence<br>This neural network analyzes any nature photo and identifies what type of biome it depicts<br>Feel free to upload your own photo or try some examples below<br>Written by David Willis"
article="<p style='text-align: center'><a href='www.linkedin.com/in/david-willis-dev' target='_blank'>LinkedIn Profile,/a.</p>"
examples= [gpath + 'forest.jpeg', gpath + 'aquatic.jpeg', gpath + 'desert.jpg', gpath + 'grassland.jpg', gpath + 'tundra.jpeg']
interpretation='default'
enable_queue=True
gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch(share=True)