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| import cv2 | |
| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| title = "Welcome on your first sketch recognition app!" | |
| head = ( | |
| "<center>" | |
| "The robot was trained to classify numbers (from 0 to 9). To test it, write your number in the space provided." | |
| "</center>" | |
| ) | |
| ref = "Find the whole code [here](https://github.com/ovh/ai-training-examples/tree/main/apps/gradio/sketch-recognition)." | |
| img_size = 28 | |
| labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] | |
| model = tf.keras.models.load_model("number_recognition_model_colab.keras") | |
| def predict(img): | |
| try: | |
| # Convert the input image to a NumPy array if needed | |
| if not isinstance(img, np.ndarray): | |
| img = np.array(img) | |
| # Ensure the image has a single channel (grayscale) | |
| if img.ndim == 2: | |
| img = np.expand_dims(img, axis=-1) | |
| # Print the type and shape of the image | |
| print(type(img), img.shape) | |
| # Resize the image | |
| img = cv2.resize(img, (img_size, img_size)) | |
| img = img.reshape(1, img_size, img_size, 1) | |
| preds = model.predict(img)[0] | |
| # Print the predictions | |
| print("Predictions:", preds) | |
| return {label: float(pred) for label, pred in zip(labels, preds)} | |
| except Exception as e: | |
| # Print the exception to the console | |
| print(f"Error during prediction: {e}") | |
| return {"Error": str(e)} | |
| label = gr.Label(num_top_classes=3) | |
| interface = gr.Interface(fn=predict, inputs="sketchpad", outputs=label, title=title, description=head, article=ref) | |
| interface.launch() |