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README.md
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---
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title:
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sdk: gradio
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sdk_version: 5.
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app_file:
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pinned: false
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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title: mcp_tools
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emoji: 🔥
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colorFrom: indigo
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sdk: gradio
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sdk_version: 5.28.0
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app_file: run.py
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pinned: false
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hf_oauth: true
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cheetah.jpg
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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}
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run.py
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import numpy as np
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import gradio as gr
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from pathlib import Path
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import os
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from PIL import Image
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def prime_factors(n):
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"""
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Compute the prime factorization of a positive integer.
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Args:
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n (int): The integer to factorize. Must be greater than 1.
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Returns:
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List[int]: A list of prime factors in ascending order.
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Raises:
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ValueError: If n is not greater than 1.
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"""
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n = int(n)
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if n <= 1:
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raise ValueError("Input must be an integer greater than 1.")
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factors = []
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while n % 2 == 0:
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factors.append(2)
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n //= 2
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divisor = 3
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while divisor * divisor <= n:
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while n % divisor == 0:
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factors.append(divisor)
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n //= divisor
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divisor += 2
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if n > 1:
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factors.append(n)
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return factors
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def generate_cheetah_image():
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"""
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Generate a cheetah image.
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Returns:
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The generated cheetah image.
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"""
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return Path(os.path.dirname(__file__)) / "cheetah.jpg"
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def image_orientation(image: Image.Image) -> str:
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"""
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Returns whether image is portrait or landscape.
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Args:
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image (Image.Image): The image to check.
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Returns:
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str: "Portrait" if image is portrait, "Landscape" if image is landscape.
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"""
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return "Portrait" if image.height > image.width else "Landscape"
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def sepia(input_img):
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"""
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Apply a sepia filter to the input image.
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Args:
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input_img (str): The input image to apply the sepia filter to.
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Returns:
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The sepia filtered image.
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"""
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sepia_filter = np.array([
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[0.393, 0.769, 0.189],
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[0.349, 0.686, 0.168],
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[0.272, 0.534, 0.131]
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])
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sepia_img = input_img.dot(sepia_filter.T)
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sepia_img /= sepia_img.max()
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return sepia_img
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demo = gr.TabbedInterface(
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[
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gr.Interface(prime_factors, gr.Textbox(), gr.Textbox(), api_name="prime_factors"),
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gr.Interface(generate_cheetah_image, None, gr.Image(), api_name="generate_cheetah_image"),
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gr.Interface(image_orientation, gr.Image(type="pil"), gr.Textbox(), api_name="image_orientation"),
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gr.Interface(sepia, gr.Image(), gr.Image(), api_name="sepia"),
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],
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[
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"Prime Factors",
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"Cheetah Image",
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"Image Orientation Checker",
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"Sepia Filter",
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]
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)
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if __name__ == "__main__":
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demo.launch(mcp_server=True)
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