app.py

import json

import gradio as gr


def analyze_text(text: str) -> str:
    """Analyze text and return statistics.

    Args:
        text: The input text to analyze

    Returns:
        JSON string with analysis results
    """
    words = text.split()
    chars = len(text)
    chars_no_spaces = len(text.replace(" ", ""))
    sentences = text.count(".") + text.count("!") + text.count("?")

    avg_word_length = round(chars_no_spaces / len(words), 2) if words else 0
    avg_sentence_length = round(len(words) / max(sentences, 1), 2)

    return json.dumps(
        {
            "total_characters": chars,
            "characters_without_spaces": chars_no_spaces,
            "total_words": len(words),
            "total_sentences": max(sentences, 1),
            "average_word_length": avg_word_length,
            "average_sentence_length": avg_sentence_length,
            "unique_words": len(set(word.lower() for word in words)),
        }
    )


def extract_keywords(text: str, count: int = 5) -> str:
    """Extract keywords (most common words) from text.

    Args:
        text: The input text
        count: Number of keywords to return (default 5)

    Returns:
        JSON string with keywords and frequencies
    """
    # Remove common words
    stopwords = {
        "the",
        "a",
        "an",
        "and",
        "or",
        "but",
        "in",
        "on",
        "at",
        "to",
        "for",
        "of",
        "with",
        "is",
        "are",
        "was",
        "were",
        "be",
        "been",
        "by",
        "from",
    }

    words = text.lower().split()
    filtered = [w.strip(".,!?;:") for w in words if w.lower() not in stopwords]

    from collections import Counter

    word_freq = Counter(filtered)
    top_words = word_freq.most_common(count)

    return json.dumps({"keywords": [{"word": w, "frequency": f} for w, f in top_words]})


def check_reading_level(text: str) -> str:
    """Estimate reading difficulty level.

    Args:
        text: The input text

    Returns:
        JSON string with reading level estimate
    """
    sentences = max(
        text.count(".") + text.count(",") + text.count("!") + text.count("?"), 1
    )
    words = len(text.split())
    syllables = (
        text.count("a")
        + text.count("e")
        + text.count("i")
        + text.count("o")
        + text.count("u")
    )

    if words == 0:
        return json.dumps({"error": "No text to analyze"})

    # Flesh Kincaid Grade
    grade = (0.39 * (words / sentences)) + (11.8 * (syllables / words)) - 15.59
    grade = max(0, round(grade, 1))

    if grade < 6:
        level = "Elementary School"
    elif grade < 9:
        level = "Middle School"
    elif grade < 13:
        level = "High School"
    else:
        level = "College/Academic"

    return json.dumps({"grade_level": grade, "reading_level": level})


# Create web UI
with gr.Blocks(title="Text Processor") as demo:
    gr.Markdown("# Text Processing Tools")
    gr.Markdown(
        "Analyze text statistics, extract keywords, and check reading difficulty."
    )

    with gr.Tab("Text Analysis"):
        text_input1 = gr.Textbox(
            label="Enter text",
            lines=8,
            placeholder="Paste your text here...",
        )
        analysis_output = gr.Textbox(label="Analysis Result", lines=8)
        gr.Button("Analyze", size="lg").click(
            analyze_text, inputs=text_input1, outputs=analysis_output
        )

    with gr.Tab("Extract Keywords"):
        text_input2 = gr.Textbox(label="Enter text", lines=8)
        count_input = gr.Slider(1, 20, value=5, step=1, label="Number of keywords")
        keywords_output = gr.Textbox(label="Keywords ", lines=8)
        gr.Button("Extract", size="lg").click(
            extract_keywords, inputs=[text_input2, count_input], outputs=keywords_output
        )

    with gr.Tab("Reading Level"):
        text_input3 = gr.Textbox(label="Enter text", lines=8)
        level_output = gr.Textbox(label="Reading Level Analysis", lines=5)
        gr.Button("Check Level", size="lg").click(
            check_reading_level, inputs=text_input3, outputs=level_output
        )


if __name__ == "__main__":
    demo.launch(mcp_server=True)