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1e40083 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | """Gradio UI that doubles as an MCP server.
`demo.launch(mcp_server=True)` exposes each function wrapped in a click handler
as an MCP tool at /gradio_api/mcp/. The same code thus serves a human web UI
AND any MCP-compatible code agent (Claude Code, Codex, OpenCode, Pi).
Run locally: python app.py -> http://localhost:7860
-> MCP at http://localhost:7860/gradio_api/mcp/
Deployed: same on HF Spaces -> /gradio_api/mcp/
"""
from __future__ import annotations
import json
from collections import Counter
import gradio as gr
STOPWORDS = {
"the", "a", "an", "and", "or", "but", "in", "on", "at", "to", "for",
"of", "with", "is", "are", "was", "were", "be", "been", "by", "from",
}
def analyze_text(text: str) -> str:
"""Analyze text and return statistics.
Args:
text: The input text to analyze.
Returns:
JSON string with character, word, and sentence statistics.
"""
words = text.split()
chars = len(text)
chars_no_spaces = len(text.replace(" ", ""))
sentences = max(text.count(".") + text.count("!") + text.count("?"), 1)
avg_word_length = round(chars_no_spaces / len(words), 2) if words else 0
avg_sentence_length = round(len(words) / sentences, 2) if words else 0
return json.dumps({
"total_characters": chars,
"characters_without_spaces": chars_no_spaces,
"total_words": len(words),
"total_sentences": sentences,
"average_word_length": avg_word_length,
"average_sentence_length": avg_sentence_length,
}, indent=2)
def extract_keywords(text: str, count: int = 5) -> str:
"""Extract keywords (most common non-stopword words) from text.
Args:
text: The input text.
count: Number of keywords to return (default 5).
Returns:
JSON string with the top-N keywords and frequencies.
"""
filtered = [
w.strip(".,!?;:") for w in text.lower().split() if w not in STOPWORDS
]
top = Counter(filtered).most_common(max(1, int(count)))
return json.dumps(
{"keywords": [{"word": w, "frequency": f} for w, f in top]},
indent=2,
)
def check_reading_level(text: str) -> str:
"""Estimate reading difficulty via a Flesch-Kincaid-style grade.
Args:
text: The input text.
Returns:
JSON string with the numeric grade and a coarse label.
"""
sentences = max(text.count(".") + text.count("!") + text.count("?"), 1)
words = len(text.split())
if words == 0:
return json.dumps({"error": "No text to analyze"})
syllables = sum(1 for c in text.lower() if c in "aeiou")
grade = max(
0.0,
(0.39 * (words / sentences)) + (11.8 * (syllables / words)) - 15.59,
)
if grade < 6:
label = "Elementary School"
elif grade < 9:
label = "Middle School"
elif grade < 13:
label = "High School"
else:
label = "College/Academic"
return json.dumps(
{"grade_level": round(grade, 1), "reading_level": label},
indent=2,
)
with gr.Blocks(title="Text Processor (MCP)") as demo:
gr.Markdown("# Text Processing Tools")
gr.Markdown(
"Analyze text statistics, extract keywords, and check reading "
"difficulty. Also exposed as an MCP server at `/gradio_api/mcp/`."
)
with gr.Tab("Analyze"):
text_in1 = gr.Textbox(label="Enter text", lines=8,
placeholder="Paste your text here…")
out1 = gr.Textbox(label="Analysis", lines=10)
gr.Button("Analyze", variant="primary").click(
analyze_text, text_in1, out1
)
with gr.Tab("Extract Keywords"):
text_in2 = gr.Textbox(label="Enter text", lines=8)
count_in = gr.Slider(1, 20, value=5, step=1, label="Number of keywords")
out2 = gr.Textbox(label="Keywords", lines=10)
gr.Button("Extract", variant="primary").click(
extract_keywords, [text_in2, count_in], out2
)
with gr.Tab("Reading Level"):
text_in3 = gr.Textbox(label="Enter text", lines=8)
out3 = gr.Textbox(label="Reading level", lines=5)
gr.Button("Check", variant="primary").click(
check_reading_level, text_in3, out3
)
if __name__ == "__main__":
demo.launch(mcp_server=True)
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