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README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Mcp Tools
-emoji: ⚡
-colorFrom: yellow
-colorTo: green
+title: mcp_tools 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 5.27.1
-app_file: app.py
+sdk_version: 5.28.0
+app_file: run.py
 pinned: false
+hf_oauth: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

run.ipynb

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+{"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}

run.py

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+import numpy as np
+import gradio as gr
+from pathlib import Path
+import os
+from PIL import Image
+
+def prime_factors(n):
+    """
+    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 = 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)