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| import cv2 | |
| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| import PIL.Image | |
| 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>" | |
| ) | |
| # Model yükleniyor | |
| model = tf.keras.models.load_model("number_recognition_model_colab.keras") | |
| img_size = 28 | |
| # Etiketler (0'dan 9'a kadar sayılar) | |
| labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] | |
| # Tahmin fonksiyonu | |
| def predict(img): | |
| try: | |
| # If the input is a dictionary (Gradio sketchpad), extract the image | |
| if isinstance(img, dict): | |
| img = img.get('image', None) # Get the image from the dictionary | |
| if img is None: | |
| raise ValueError("No image data found") | |
| # Convert the input image to a NumPy array if needed | |
| if not isinstance(img, np.ndarray): | |
| img = np.array(img) | |
| # Print shape and type of the input image | |
| print(f"Initial image type: {type(img)}, shape: {img.shape}") | |
| # Ensure the image is in grayscale and has a single channel | |
| if img.ndim == 3 and img.shape[-1] == 3: | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| elif img.ndim == 2: | |
| img = np.expand_dims(img, axis=-1) | |
| # Print the shape of the grayscale image | |
| print(f"Grayscale image shape: {img.shape}") | |
| # Resize the image | |
| img = cv2.resize(img, (img_size, img_size)) | |
| # Normalize the image | |
| img = img.astype('float32') / 255.0 | |
| img = img.reshape(1, img_size, img_size, 1) | |
| # Print the shape after resizing and normalizing | |
| print(f"Processed image shape: {img.shape}") | |
| # Get the predictions from the model | |
| preds = model.predict(img)[0] | |
| # Print the predictions | |
| print("Predictions:", preds) | |
| # Return the predictions for each label | |
| 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)} | |
| # Set up the Gradio interface with the input as a sketchpad and output as labels | |
| label = gr.Label(num_top_classes=3) | |
| # Gradio arayüzü | |
| interface = gr.Interface( | |
| fn=predict, | |
| inputs="sketchpad", | |
| outputs="textbox", | |
| title="Sketch Recognition App", | |
| description="Draw a number (0-9) and see the model's top predictions." | |
| ) | |
| interface.launch(debug=True) | |