Spaces:
Sleeping
Sleeping
| import cv2 | |
| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| from PIL import 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 | |
| labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] | |
| def predict(img): | |
| try: | |
| # Enhanced image validation and conversion | |
| if img is None: | |
| raise ValueError("No image provided") | |
| # Convert to numpy array if it's a PIL Image | |
| if isinstance(img, Image.Image): | |
| img = np.array(img) | |
| # Handle base64 image strings | |
| elif isinstance(img, str): | |
| # Check if it's a base64 data URL | |
| if img.startswith('data:image'): | |
| # Split and decode base64 part | |
| img = img.split(',')[1] | |
| # Decode base64 to image | |
| try: | |
| img = Image.open(io.BytesIO(base64.b64decode(img))) | |
| img = np.array(img) | |
| except Exception as e: | |
| print(f"Base64 decoding error: {e}") | |
| raise ValueError("Invalid base64 image") | |
| # Validate numpy array | |
| if not isinstance(img, np.ndarray): | |
| raise ValueError("Input could not be converted to a valid image") | |
| # Print initial image details for debugging | |
| print(f"Initial image type: {type(img)}, shape: {img.shape}") | |
| # Handle color channels | |
| if img.ndim == 3: | |
| if img.shape[-1] == 3: # Color image | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| elif img.shape[-1] == 4: # RGBA image | |
| img = cv2.cvtColor(img, cv2.COLOR_RGBA2GRAY) | |
| # Ensure single channel | |
| if img.ndim == 2: | |
| img = np.expand_dims(img, axis=-1) | |
| # Resize and normalize | |
| img = cv2.resize(img, (img_size, img_size)) | |
| img = img.astype('float32') / 255.0 | |
| img = img.reshape(1, img_size, img_size, 1) | |
| # Print processed image details | |
| print(f"Processed image shape: {img.shape}") | |
| # Get predictions from the model | |
| preds = model.predict(img)[0] | |
| # Print predictions for debugging | |
| print("Predictions:", preds) | |
| # Return predictions as a dictionary | |
| return {label: float(pred) for label, pred in zip(labels, preds)} | |
| except Exception as e: | |
| # Comprehensive error logging | |
| print(f"Full 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=gr.Sketchpad(type="pil"), | |
| outputs=label, | |
| title="Sketch Recognition App", | |
| description="Draw a number (0-9) and see the model's top predictions." | |
| ) | |
| interface.launch(debug=True, share=True) |