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| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| from PIL import Image | |
| model_path = "DogClassifier2.2.keras" | |
| model = tf.keras.models.load_model(model_path) | |
| # Define the core prediction function | |
| def predict_bmwX(image): | |
| # Preprocess image | |
| image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image | |
| image = image.convert("RGB") # Ensure the image is in RGB format | |
| image = image.resize((150, 150)) # Resize the image to 150x150 | |
| image = np.array(image) | |
| image = np.expand_dims(image, axis=0) # Add batch dimension | |
| # Predict | |
| prediction = model.predict(image) | |
| # Apply softmax to get probabilities for each class | |
| prediction = tf.nn.softmax(prediction) | |
| # Debug statement to check the shape of the prediction | |
| print(f"Prediction shape: {prediction.shape}") | |
| # Define class names | |
| class_names = ['Afghan', 'African Wild Dog', 'Airedale', 'American Hairless', 'American Spaniel', 'Basenji', 'Basset', 'Beagle', | |
| 'Bearded Collie', 'Bermaise', 'Bichon Frise', 'Blenheim', 'Bloodhound', 'Bluetick', 'Border Collie', 'Borzoi', | |
| 'Boston Terrier', 'Boxer', 'Bull Mastiff', 'Bull Terrier', 'Bulldog', 'Cairn', 'Chihuahua', 'Chinese Crested', | |
| 'Chow', 'Clumber','Cockapoo', 'Cocker', 'Collie', 'Corgi', 'Coyote', 'Dalmation', 'Dhole', 'Dingo', 'Doberman', | |
| 'Elk Hound', 'French Bulldog', 'German Sheperd', 'Golden Retriever', 'Great Dane', 'Great Perenees', 'Greyhound', | |
| 'Groenendael', 'Irish Spaniel', 'Irish Wolfhound', 'Japanese Spaniel', 'Komondor', 'Labradoodle', 'Labrador', | |
| 'Lhasa', 'Malinois', 'Maltese', 'Mex Hairless', 'Newfoundland', 'Pekinese', 'Pit Bull', 'Pomeranian', | |
| 'Poodle', 'Pug', 'Rhodesian', 'Rottweiler', 'Saint Bernard', 'Schnauzer', 'Scotch Terrier', 'Shar_Pei', | |
| 'Shiba Inu', 'Shih-Tzu', 'Siberian Husky', 'Vizsla', 'Yorkie'] | |
| # Check if the number of predictions matches the number of class names | |
| if len(prediction[0]) != len(class_names): | |
| return f"Error: Number of model outputs ({len(prediction[0])}) does not match number of class names ({len(class_names)})." | |
| # Create a dictionary with the probabilities for each dog breed | |
| prediction_dict = {class_names[i]: np.round(float(prediction[0][i]), 2) for i in range(len(class_names))} | |
| # Sort the dictionary by value in descending order and get the top 3 classes | |
| sorted_predictions = dict(sorted(prediction_dict.items(), key=lambda item: item[1], reverse=True)) | |
| return sorted_predictions | |
| input_image = gr.Image() | |
| iface = gr.Interface( | |
| fn=predict_bmwX, | |
| inputs=input_image, | |
| outputs=gr.Label(), | |
| description="A simple MLP classification model for image classification using the MNIST dataset.") | |
| iface.launch(share=True) | |