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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
#!pip install tensorflow tensorflow-datasets gradio pillow matplotlib
model_path = "Dogs-model_transferlearning.keras"
model = tf.keras.models.load_model(model_path)
# Define the core prediction function
def predict_dogs(image):
# Preprocess image
image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image
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
probabilities = tf.nn.softmax(prediction)
# Map probabilities to Pokemon classes
dogs_classes = ['Goldie', 'Beagle', 'Husky']
probabilities_dict = {dogs_class: round(float(probability), 2) for dogs_class, probability in zip(dogs_classes, probabilities[0])}
return probabilities_dict
# Create the Gradio interface
input_image = gr.Image()
iface = gr.Interface(
fn=predict_dogs,
inputs=input_image,
outputs=gr.Label(),
live=True,
examples=["images/01.jpg", "images/02.jpg", "images/03.jpg", "images/04.jpg", "images/06.jpg", "images/06.jpg"],
description="A simple mlp classification model for image classification using the mnist dataset.")
iface.launch() |