Update app.py

app.py

@@ -1,29 +1,102 @@
+import numpy as np
 import gradio as gr
+from pathlib import Path
+import os
+from PIL import Image
 
-def letter_counter(word, letter):
+def prime_factors(n):
     """
-    Count the number of occurrences of a letter in a word or text.
+    Compute the prime factorization of a positive integer.
 
     Args:
-        word (str): The input text to search through
-        letter (str): The letter to search for
+        n (int): The integer to factorize. Must be greater than 1.
 
     Returns:
-        str: A message indicating how many times the letter appears
-    """
-    word = word.lower()
-    letter = letter.lower()
-    count = word.count(letter)
-    return count
-
-# Create and launch the Gradio interface
-demo = gr.Interface(
-    fn=letter_counter,
-    inputs=["textbox", "textbox"],
-    outputs="number",
-    title="Letter Counter",
-    description="Enter text and a letter to count how many times the letter appears in the text."
+        List[int]: A list of prime factors in ascending order.
+
+    Raises:
+        ValueError: If n is not greater than 1.
+    """
+    n = int(n)
+    if n <= 1:
+        raise ValueError("Input must be an integer greater than 1.")
+
+    factors = []
+    while n % 2 == 0:
+        factors.append(2)
+        n //= 2
+
+    divisor = 3
+    while divisor * divisor <= n:
+        while n % divisor == 0:
+            factors.append(divisor)
+            n //= divisor
+        divisor += 2
+
+    if n > 1:
+        factors.append(n)
+
+    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 (str): 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, strict_cors=False)
+    demo.launch(mcp_server=True)