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LPX55
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Parent(s):
re-init
Browse files- .gitattributes +34 -0
- .gitignore +1 -0
- README.md +13 -0
- __pycache__/style.cpython-310.pyc +0 -0
- app.py +139 -0
- requirements.txt +3 -0
- style.py +39 -0
.gitattributes
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.gitignore
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env/
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README.md
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---
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title: REST API with Gradio and Huggingface Spaces
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emoji: 👩💻
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colorFrom: blue
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colorTo: green
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sdk: gradio
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sdk_version: 5.34.2
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app_file: app.py
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pinned: false
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license: openrail
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---
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This is a demo from this article: https://www.tomsoderlund.com/ai/building-ai-powered-rest-api
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__pycache__/style.cpython-310.pyc
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Binary file (726 Bytes). View file
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app.py
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import gradio as gr
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import gradio.themes
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import numpy as np
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from style import js_func, css_func
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import tempfile
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import pandas as pd
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def greet(name="Stranger", intensity=1, exclaim=False):
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greeting = f"Hello, {name}{'!' * int(intensity)}"
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if exclaim:
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greeting = greeting.upper()
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return greeting
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def calculator(num1, operation, num2):
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if operation == "add":
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result = num1 + num2
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elif operation == "subtract":
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result = num1 - num2
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elif operation == "multiply":
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result = num1 * num2
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elif operation == "divide":
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if num2 == 0:
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raise gr.Error("Cannot divide by zero!")
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result = num1 / num2
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return result
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def sepia(input_img):
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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 @ sepia_filter.T
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sepia_img = np.clip(sepia_img, 0, 255).astype(np.uint8)
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return sepia_img
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def download_text(text):
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if not text:
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text = ""
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with tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode="w", encoding="utf-8") as f:
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f.write(text)
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return f.name
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def download_csv(result):
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if result is None:
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result = ""
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df = pd.DataFrame({"Result": [result]})
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with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode="w", encoding="utf-8") as f:
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df.to_csv(f, index=False)
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return f.name
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theme = gr.Theme.from_hub("LPX55/modal_ai")
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with gr.Blocks(theme=theme, js=js_func, css=css_func) as demo:
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with gr.Sidebar(width="25vw"):
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gr.Image("logo.png", width=80, show_label=False)
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gr.Markdown("""
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# 🤖 Gradio API Demo
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_Build, test, and share AI tools instantly!_
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---
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**Navigation**
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- [Greeting](#greeting)
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- [Calculator](#calculator)
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- [Sepia Image](#sepia-image)
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---
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[GitHub Repo](https://github.com/yourrepo) | [Docs](https://yourdocs)
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---
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**Tips:**
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- Try the examples in each tab!
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- Download your results with the button on the right.
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---
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_v1.0.0 | Powered by Gradio_
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""")
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gr.Code(
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"""from gradio_client import Client
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client = Client("YOUR_URL")
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print(client.predict("Alex", 5, False, api_name="/greet"))
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""", language="python", label="**API Example:**"
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)
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gr.Button("Reset All", elem_id="reset-btn")
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with gr.Tab("Greeting"):
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gr.Markdown("## Greeting Generator")
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with gr.Row():
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with gr.Column():
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name = gr.Textbox(label="Name", info="Enter your name", placeholder="e.g. Alex")
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intensity = gr.Slider(1, 20, value=3, step=1, label="Intensity", info="How excited should the greeting be?")
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with gr.Accordion("Advanced Options", open=False):
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exclaim = gr.Checkbox(label="Shout (all caps)", info="Make the greeting all uppercase and add exclamations")
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greet_btn = gr.Button("Greet")
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with gr.Column():
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greet_output = gr.Textbox(label="Greeting", lines=2)
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download_greet_btn = gr.DownloadButton(label="Download Greeting", value=download_text, inputs=greet_output)
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gr.Examples(
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[["Jill", 1, False], ["Sam", 3, True], ["Alex", 5, False]],
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inputs=[name, intensity, exclaim],
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outputs=greet_output,
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fn=greet
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)
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greet_btn.click(greet, [name, intensity, exclaim], greet_output)
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with gr.Tab("Calculator"):
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gr.Markdown("## Toy Calculator")
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with gr.Row():
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with gr.Column():
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num1 = gr.Number(label="Number 1", info="First number")
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operation = gr.Radio(["add", "subtract", "multiply", "divide"], label="Operation", info="Choose the operation")
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num2 = gr.Number(label="Number 2", info="Second number")
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calc_btn = gr.Button("Calculate")
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with gr.Column():
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calc_output = gr.Number(label="Result")
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download_calc_btn = gr.DownloadButton(label="Download Result", value=download_csv, inputs=calc_output)
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gr.Examples(
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[[45, "add", 3], [3.14, "divide", 2], [144, "multiply", 2.5], [0, "subtract", 1.2]],
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inputs=[num1, operation, num2],
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outputs=calc_output,
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fn=calculator
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)
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calc_btn.click(calculator, [num1, operation, num2], calc_output)
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with gr.Tab("Sepia Image"):
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gr.Markdown("## Sepia Image Filter")
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image_input = gr.Image(label="Input Image", type="numpy")
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sepia_btn = gr.Button("Apply Sepia")
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image_output = gr.Image(label="Sepia Image")
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sepia_btn.click(sepia, image_input, image_output)
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gr.Markdown("""
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---
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## 📡 API Usage
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- Every function in this demo is automatically available as a REST API!
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- View the [OpenAPI schema](./openapi.json) or click "Use via API" in the footer.
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- Try the [gradio_client](https://www.gradio.app/guides/getting-started-with-the-python-client) or [@gradio/client](https://www.gradio.app/guides/getting-started-with-the-js-client) to call these endpoints programmatically.
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""")
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demo.launch(mcp_server=True)
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requirements.txt
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gradio[mcp]
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numpy
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pandas
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style.py
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js_func = """
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function refresh() {
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const url = new URL(window.location);
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if (url.searchParams.get('__theme') !== 'dark') {
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url.searchParams.set('__theme', 'dark');
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window.location.href = url.href;
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}
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}
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"""
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css_func = """
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.sidebar{
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background-color: (--block-border-color) !important;
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border-right: #373737 !important;
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border-right-width: 3px !important;
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border-right-style: solid !important;
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}
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.post{
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color: #1c1a1a !important;
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font-size: 0.8rem !important;
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padding: 4px 2px !important;
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}
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.highlight{
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color: #1c1a1a !important;
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font-size: 0.8rem !important;
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padding: 4px 2px !important;
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}
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.prose a{
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color: #aba9a9 !important;
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}
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.prose a:visited{
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color: #6b6767 !important;
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}
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.prose hr {
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margin-top: 2em !important;
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margin-bottom: 2em !important;
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"""
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