import numpy as np import gradio as gr from pathlib import Path import os from PIL import Image def prime_factors(n: str): """ Compute the prime factorization of a positive integer. Args: n (str): The integer to factorize. Must be greater than 1. """ n_int = int(n) if n_int <= 1: raise ValueError("Input must be an integer greater than 1.") factors = [] while n_int % 2 == 0: factors.append(2) n_int //= 2 divisor = 3 while divisor * divisor <= n_int: while n_int % divisor == 0: factors.append(divisor) n_int //= divisor divisor += 2 if n_int > 1: factors.append(n_int) return factors def generate_cheetah_image(): """ Generate a cheetah image. Returns: The generated cheetah image. """ return Path(os.path.dirname(__file__)) / "cheetah.jpg" def image_orientation(image: Image.Image) -> str: """ Returns whether image is portrait or landscape. Args: image (Image.Image): The image to check. Returns: str: "Portrait" if image is portrait, "Landscape" if image is landscape. """ return "Portrait" if image.height > image.width else "Landscape" def sepia(input_img): """ Apply a sepia filter to the input image. Args: input_img (np.array): The input image to apply the sepia filter to. Returns: The sepia filtered image. """ sepia_filter = np.array([ [0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131] ]) sepia_img = input_img.dot(sepia_filter.T) sepia_img /= sepia_img.max() return sepia_img demo = gr.TabbedInterface( [ gr.Interface(prime_factors, gr.Textbox(), gr.Textbox(), api_name="prime_factors"), gr.Interface(generate_cheetah_image, None, gr.Image(), api_name="generate_cheetah_image"), gr.Interface(image_orientation, gr.Image(type="pil"), gr.Textbox(), api_name="image_orientation"), gr.Interface(sepia, gr.Image(), gr.Image(), api_name="sepia"), ], [ "Prime Factors", "Cheetah Image", "Image Orientation Checker", "Sepia Filter", ] ) if __name__ == "__main__": demo.launch(mcp_server=True)