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import numpy as np
import gradio as gr
import tensorflow as tf
import cv2
# App title
title = "Welcome to your first sketch recognition app!"
# App description
head = (
"<center>"
"<img src='./mnist-classes.png' width=400>"
"<p>The model is trained to classify numbers (from 0 to 9). "
"To test it, draw your number in the space provided.</p>"
"</center>"
)
# GitHub repository link
ref = "Find the complete code [here](https://github.com/ovh/ai-training-examples/tree/main/apps/gradio/sketch-recognition)."
# Image size: 28x28
img_size = 28
# Class names (from 0 to 9)
labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
# Load model (trained on MNIST dataset)
model = tf.keras.models.load_model("./sketch_recognition_numbers_model.h5")
# Prediction function for sketch recognition
def predict(data):
# Extract the 'image' key from the input dictionary
img = data['image']
# Convert to NumPy array
img = np.array(img)
# Convert to grayscale
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Resize image to 28x28
img = cv2.resize(img, (img_size, img_size))
# Normalize pixel values
img = img / 255.0
# Reshape image to match model input
img = img.reshape(1, img_size, img_size, 1)
# Model predictions
preds = model.predict(img)[0]
# Return the probability for each class
return {label: float(pred) for label, pred in zip(labels, preds)}
# Top 3 classes
label = gr.Label(num_top_classes=3)
# Open Gradio interface for sketch recognition
interface = gr.Interface(
fn=predict,
inputs=gr.Sketchpad(),
outputs=label,
title=title,
description=head,
article=ref
)
interface.launch() |