app.py

### -------------------------------- ###
###            libraries             ###
### -------------------------------- ###
import gradio as gr
import numpy as np
import os
from tensorflow.keras.models import load_model
from reader import get_article


### -------------------------------- ###
###           model loading          ###
### -------------------------------- ###
model = load_model('model.h5') # single file model from colab


## --------------------------------- ###
###     reading: categories.txt      ###
### -------------------------------- ###
labels = ['please upload categories.txt' for i in range(10)] # placeholder

if os.path.isfile("categories.txt"):
    # open categories.txt in read mode
    categories = open("categories.txt", "r")
    labels = categories.readline().split()


## --------------------------------- ###
###        reading: info.txt         ###
### -------------------------------- ###
# borrow file reading functionality from reader.py
info = get_article()


### -------------------------------- ###
###        interface creation        ###
### -------------------------------- ###
samples = ['pug.jpeg', 'cheetah.jpeg']

def preprocess(image):
  image = np.array(image) / 255
  image = np.expand_dims(image, axis=0)
  return image
  
def predict_image(image):
  pred = model.predict(preprocess(image))
  results = {}
  for row in pred:
    for idx, item in enumerate(row):
      results[labels[idx]] = float(item)
  return results

# generate img input and text label output
image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
label = gr.outputs.Label(num_top_classes=len(labels))

# generate and launch interface
interface = gr.Interface(fn=predict_image, inputs=image, outputs=label, article=info['article'], css=info['css'], theme='default', title=info['title'], allow_flagging='never', description=info['description'], examples=samples)
interface.launch()