Spaces:
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### -------------------------------- ###
### libraries ###
### -------------------------------- ###
import gradio as gr
import numpy as np
import os
import tensorflow as tf
### -------------------------------- ###
### model loading ###
### -------------------------------- ###
model = tf.keras.models.load_model('model.h5')
## --------------------------------- ###
### 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()
## --------------------------------- ###
### page description ###
### -------------------------------- ###
title = "Cast parts: Deffective or Okay?"
description = "A Hugging Space demo created by datasith!"
article = \
'''
#### Deffective or Okay?
Demo app including a binary classification model for casted parts
This is a test project to get familiar with Hugging Face!
The space includes the necessary files for everything to run smoothly on HF's Spaces:
- app.py
- reader.py
- requirements.txt
- model.h5 (TensorFlow/Keras)
- categories.txt
- info.txt
The data used to train the model is available as a
[Kaggle dataset](https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product).
The space was inspired by @Isabel's wonderful
[cat or pug](https://huggingface.co/spaces/isabel/pug-or-cat-image-classifier) one.
If you enjoy my work feel free to follow me here on HF and/or on:
- [GitHub](https://github.com/datasith)
- [Kaggle](https://kaggle.com/datasith)
- [Twitter](https://twitter.com/datasith)
- [LinkedIn](https://linkedin.com/in/datasith)
Either way, enjoy!
'''
### -------------------------------- ###
### interface creation ###
### -------------------------------- ###
samples = ['defective.jpeg', 'okay.jpeg']
def preprocess(image):
img_grayscale = image[:,:,1]
img_array = tf.keras.utils.img_to_array(img_grayscale)
img_array = tf.expand_dims(img_array, 0)
# image = np.array(image) / 255
# image = np.expand_dims(image, axis=0)
return img_array
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)
predictions = model.predict(preprocess(image))
scores = tf.nn.softmax(predictions[0])
results = {}
for idx, res in enumerate(scores):
results[labels[idx]] = float(res)
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=article, theme='default',
title=title, allow_flagging='never', description=description,
examples=samples)
interface.launch()
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