app.py

import re
import gradio as gr
import spaces


# ============================================================
# RULE-BASED SENTIMENT WORDS
# ============================================================

POSITIVE_WORDS = {
    "good",
    "great",
    "excellent",
    "amazing",
    "awesome",
    "happy",
    "love",
    "like",
    "wonderful",
    "fantastic",
    "best",
    "beautiful",
    "perfect",
    "nice",
    "enjoy",
    "enjoyed",
    "helpful",
    "successful",
    "success",
    "brilliant",
    "positive",
    "thank",
    "thanks"
}


NEGATIVE_WORDS = {
    "bad",
    "terrible",
    "awful",
    "horrible",
    "sad",
    "hate",
    "dislike",
    "worst",
    "poor",
    "ugly",
    "wrong",
    "disappointing",
    "disappointed",
    "failure",
    "fail",
    "failed",
    "negative",
    "angry",
    "boring",
    "problem",
    "problems",
    "difficult",
    "useless",
    "slow",
    "broken"
}


# ============================================================
# SENTIMENT ANALYZER
# ============================================================
# ZeroGPU requires a @spaces.GPU function to be registered
# with the Gradio event system.
#
# This application does NOT actually perform GPU computation.
# The decorator is only required because this Space is using
# ZeroGPU hardware.
# ============================================================

@spaces.GPU(duration=1)
def sentiment_analyzer(text):

    # --------------------------------------------------------
    # Check empty input
    # --------------------------------------------------------

    if not text or not text.strip():
        return "⚠️ Please enter a sentence."


    # --------------------------------------------------------
    # Convert text to lowercase
    # --------------------------------------------------------

    text = text.lower()


    # --------------------------------------------------------
    # Extract words
    # --------------------------------------------------------

    words = re.findall(r"\b\w+\b", text)


    # --------------------------------------------------------
    # Find positive words
    # --------------------------------------------------------

    positive_matches = [
        word
        for word in words
        if word in POSITIVE_WORDS
    ]


    # --------------------------------------------------------
    # Find negative words
    # --------------------------------------------------------

    negative_matches = [
        word
        for word in words
        if word in NEGATIVE_WORDS
    ]


    # --------------------------------------------------------
    # Count sentiment words
    # --------------------------------------------------------

    positive_count = len(positive_matches)
    negative_count = len(negative_matches)


    # --------------------------------------------------------
    # Sentiment decision
    # --------------------------------------------------------

    if positive_count > negative_count:

        sentiment = "😊 Positive"

    elif negative_count > positive_count:

        sentiment = "😞 Negative"

    else:

        sentiment = "😐 Neutral"


    # --------------------------------------------------------
    # Create result
    # --------------------------------------------------------

    result = f"""
Sentiment: {sentiment}

Positive words detected: {positive_count}
Negative words detected: {negative_count}

Positive matches:
{", ".join(positive_matches) if positive_matches else "None"}

Negative matches:
{", ".join(negative_matches) if negative_matches else "None"}
"""

    return result


# ============================================================
# GRADIO INTERFACE
# ============================================================

with gr.Blocks(
    title="Rule-Based Sentiment Analysis"
) as app:

    # --------------------------------------------------------
    # Header
    # --------------------------------------------------------

    gr.Markdown(
        """
        # 📝 Rule-Based Sentiment Analysis

        Analyze the sentiment of a sentence using
        **manually defined rules and keywords**.

        ### Sentiment Categories

        😊 **Positive**

        😞 **Negative**

        😐 **Neutral**

        ---

        **How it works**

        The application checks the words in your sentence
        against predefined positive and negative word lists.

        **No AI model • No API • No Machine Learning**
        """
    )


    # --------------------------------------------------------
    # Text input
    # --------------------------------------------------------

    text_input = gr.Textbox(
        label="Enter your sentence",
        placeholder="Example: I really love this application!",
        lines=4
    )


    # --------------------------------------------------------
    # Analyze button
    # --------------------------------------------------------

    analyze_button = gr.Button(
        "Analyze Sentiment",
        variant="primary"
    )


    # --------------------------------------------------------
    # Output
    # --------------------------------------------------------

    output = gr.Textbox(
        label="Analysis Result",
        lines=10,
        interactive=False
    )


    # --------------------------------------------------------
    # Button event
    # --------------------------------------------------------

    analyze_button.click(
        fn=sentiment_analyzer,
        inputs=text_input,
        outputs=output
    )


    # --------------------------------------------------------
    # Clear button
    # --------------------------------------------------------

    gr.ClearButton(
        components=[
            text_input,
            output
        ],
        value="Clear"
    )


# ============================================================
# LAUNCH
# ============================================================

if __name__ == "__main__":
    app.launch()