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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.28.0
+sdk_version: 5.29.0
 app_file: run.py
 pinned: false
 hf_oauth: true

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: mcp_tools"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/mcp_tools/cheetah.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "import gradio as gr\n", "from pathlib import Path\n", "import os\n", "from PIL import Image\n", "\n", "def prime_factors(n):\n", "    \"\"\"\n", "    Compute the prime factorization of a positive integer.\n", "\n", "    Args:\n", "        n (int): The integer to factorize. Must be greater than 1.\n", "\n", "    Returns:\n", "        List[int]: A list of prime factors in ascending order.\n", "\n", "    Raises:\n", "        ValueError: If n is not greater than 1.\n", "    \"\"\"\n", "    n = int(n)\n", "    if n <= 1:\n", "        raise ValueError(\"Input must be an integer greater than 1.\")\n", "\n", "    factors = []\n", "    while n % 2 == 0:\n", "        factors.append(2)\n", "        n //= 2\n", "\n", "    divisor = 3\n", "    while divisor * divisor <= n:\n", "        while n % divisor == 0:\n", "            factors.append(divisor)\n", "            n //= divisor\n", "        divisor += 2\n", "\n", "    if n > 1:\n", "        factors.append(n)\n", "\n", "    return factors\n", "\n", "\n", "def generate_cheetah_image():\n", "    \"\"\"\n", "    Generate a cheetah image.\n", "\n", "    Returns:\n", "        The generated cheetah image.\n", "    \"\"\"\n", "    return Path(os.path.abspath('')) / \"cheetah.jpg\"\n", "\n", "\n", "def image_orientation(image: Image.Image) -> str:\n", "    \"\"\"\n", "    Returns whether image is portrait or landscape.\n", "\n", "    Args:\n", "        image (Image.Image): The image to check.\n", "\n", "    Returns:\n", "        str: \"Portrait\" if image is portrait, \"Landscape\" if image is landscape.\n", "    \"\"\"\n", "    return \"Portrait\" if image.height > image.width else \"Landscape\"\n", "\n", "\n", "def sepia(input_img):\n", "    \"\"\"\n", "    Apply a sepia filter to the input image.\n", "\n", "    Args:\n", "        input_img (str): The input image to apply the sepia filter to.\n", "\n", "    Returns:\n", "        The sepia filtered image.\n", "    \"\"\"\n", "    sepia_filter = np.array([\n", "        [0.393, 0.769, 0.189],\n", "        [0.349, 0.686, 0.168],\n", "        [0.272, 0.534, 0.131]\n", "    ])\n", "    sepia_img = input_img.dot(sepia_filter.T)\n", "    sepia_img /= sepia_img.max()\n", "    return sepia_img\n", "\n", "\n", "\n", "demo = gr.TabbedInterface(\n", "    [\n", "        gr.Interface(prime_factors, gr.Textbox(), gr.Textbox(), api_name=\"prime_factors\"),\n", "        gr.Interface(generate_cheetah_image, None, gr.Image(), api_name=\"generate_cheetah_image\"),\n", "        gr.Interface(image_orientation, gr.Image(type=\"pil\"), gr.Textbox(), api_name=\"image_orientation\"),\n", "        gr.Interface(sepia, gr.Image(), gr.Image(), api_name=\"sepia\"),\n", "    ],\n", "    [\n", "        \"Prime Factors\",\n", "        \"Cheetah Image\",\n", "        \"Image Orientation Checker\",\n", "        \"Sepia Filter\",\n", "    ]\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(mcp_server=True)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: mcp_tools"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/mcp_tools/cheetah.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "import gradio as gr\n", "from pathlib import Path\n", "import os\n", "from PIL import Image\n", "\n", "def prime_factors(n: str):\n", "    \"\"\"\n", "    Compute the prime factorization of a positive integer.\n", "\n", "    Args:\n", "        n (str): The integer to factorize. Must be greater than 1.\n", "    \"\"\"\n", "    n_int = int(n)\n", "    if n_int <= 1:\n", "        raise ValueError(\"Input must be an integer greater than 1.\")\n", "\n", "    factors = []\n", "    while n_int % 2 == 0:\n", "        factors.append(2)\n", "        n_int //= 2\n", "\n", "    divisor = 3\n", "    while divisor * divisor <= n_int:\n", "        while n_int % divisor == 0:\n", "            factors.append(divisor)\n", "            n_int //= divisor\n", "        divisor += 2\n", "\n", "    if n_int > 1:\n", "        factors.append(n_int)\n", "\n", "    return factors\n", "\n", "\n", "def generate_cheetah_image():\n", "    \"\"\"\n", "    Generate a cheetah image.\n", "\n", "    Returns:\n", "        The generated cheetah image.\n", "    \"\"\"\n", "    return Path(os.path.abspath('')) / \"cheetah.jpg\"\n", "\n", "\n", "def image_orientation(image: Image.Image) -> str:\n", "    \"\"\"\n", "    Returns whether image is portrait or landscape.\n", "\n", "    Args:\n", "        image (Image.Image): The image to check.\n", "\n", "    Returns:\n", "        str: \"Portrait\" if image is portrait, \"Landscape\" if image is landscape.\n", "    \"\"\"\n", "    return \"Portrait\" if image.height > image.width else \"Landscape\"\n", "\n", "\n", "def sepia(input_img):\n", "    \"\"\"\n", "    Apply a sepia filter to the input image.\n", "\n", "    Args:\n", "        input_img (np.array): The input image to apply the sepia filter to.\n", "\n", "    Returns:\n", "        The sepia filtered image.\n", "    \"\"\"\n", "    sepia_filter = np.array([\n", "        [0.393, 0.769, 0.189],\n", "        [0.349, 0.686, 0.168],\n", "        [0.272, 0.534, 0.131]\n", "    ])\n", "    sepia_img = input_img.dot(sepia_filter.T)\n", "    sepia_img /= sepia_img.max()\n", "    return sepia_img\n", "\n", "\n", "\n", "demo = gr.TabbedInterface(\n", "    [\n", "        gr.Interface(prime_factors, gr.Textbox(), gr.Textbox(), api_name=\"prime_factors\"),\n", "        gr.Interface(generate_cheetah_image, None, gr.Image(), api_name=\"generate_cheetah_image\"),\n", "        gr.Interface(image_orientation, gr.Image(type=\"pil\"), gr.Textbox(), api_name=\"image_orientation\"),\n", "        gr.Interface(sepia, gr.Image(), gr.Image(), api_name=\"sepia\"),\n", "    ],\n", "    [\n", "        \"Prime Factors\",\n", "        \"Cheetah Image\",\n", "        \"Image Orientation Checker\",\n", "        \"Sepia Filter\",\n", "    ]\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(mcp_server=True)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -4,37 +4,31 @@ from pathlib import Path
 import os
 from PIL import Image
 
-def prime_factors(n):
+def prime_factors(n: str):
     """
     Compute the prime factorization of a positive integer.
 
     Args:
-        n (int): The integer to factorize. Must be greater than 1.
-
-    Returns:
-        List[int]: A list of prime factors in ascending order.
-
-    Raises:
-        ValueError: If n is not greater than 1.
+        n (str): The integer to factorize. Must be greater than 1.
     """
-    n = int(n)
-    if n <= 1:
+    n_int = int(n)
+    if n_int <= 1:
         raise ValueError("Input must be an integer greater than 1.")
 
     factors = []
-    while n % 2 == 0:
+    while n_int % 2 == 0:
         factors.append(2)
-        n //= 2
+        n_int //= 2
 
     divisor = 3
-    while divisor * divisor <= n:
-        while n % divisor == 0:
+    while divisor * divisor <= n_int:
+        while n_int % divisor == 0:
             factors.append(divisor)
-            n //= divisor
+            n_int //= divisor
         divisor += 2
 
-    if n > 1:
-        factors.append(n)
+    if n_int > 1:
+        factors.append(n_int)
 
     return factors
 
@@ -67,7 +61,7 @@ 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.
+        input_img (np.array): The input image to apply the sepia filter to.
 
     Returns:
         The sepia filtered image.