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sentiment-analysis
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"""
Sentiment Analysis — Positive / Negative / Neutral
Course: 100 Deep Learning ch4
"""

import gradio as gr
from transformers import pipeline

# Multi-language sentiment model
classifier = pipeline(
    "sentiment-analysis",
    model="lxyuan/distilbert-base-multilingual-cased-sentiments-student",
)

LABEL_MAP = {
    "positive": "Positive",
    "negative": "Negative",
    "neutral": "Neutral",
}
EMOJI_MAP = {
    "positive": "😊",
    "negative": "😞",
    "neutral": "😐",
}


def analyze(text: str):
    if not text.strip():
        return {}, ""

    results = classifier(text, top_k=3)
    label_dict = {
        f"{EMOJI_MAP.get(r['label'], '')} {LABEL_MAP.get(r['label'], r['label'])}": round(r["score"], 4)
        for r in results
    }

    top = results[0]
    emoji = EMOJI_MAP.get(top["label"], "")
    summary = (
        f"**Prediction: {emoji} {LABEL_MAP.get(top['label'], top['label'])}** "
        f"({top['score']:.1%} confidence)\n\n"
        f"| Label | Score |\n|---|---|\n"
    )
    for r in results:
        bar_len = int(r["score"] * 30)
        bar = "█" * bar_len + "░" * (30 - bar_len)
        summary += f"| {LABEL_MAP.get(r['label'], r['label'])} | {bar} {r['score']:.1%} |\n"

    return label_dict, summary


with gr.Blocks(title="Sentiment Analysis") as demo:
    gr.Markdown(
        "# Sentiment Analysis\n"
        "Enter text in any language to analyze its sentiment.\n"
        "Uses a multilingual DistilBERT model.\n"
        "*Course: 100 Deep Learning ch4 — RNN & Sequence Models*"
    )

    with gr.Row():
        with gr.Column():
            text_input = gr.Textbox(
                label="Input Text",
                placeholder="Type or paste text here...",
                lines=4,
            )
            btn = gr.Button("Analyze Sentiment", variant="primary")
        with gr.Column():
            label_out = gr.Label(num_top_classes=3, label="Sentiment Scores")

    detail_md = gr.Markdown()

    btn.click(analyze, [text_input], [label_out, detail_md])
    text_input.submit(analyze, [text_input], [label_out, detail_md])

    gr.Examples(
        examples=[
            "I absolutely love this product! It exceeded all my expectations.",
            "The movie was terrible. Worst 2 hours of my life.",
            "The weather today is partly cloudy with temperatures around 72°F.",
            "这个餐厅的食物非常好吃，服务也很棒！",
            "I'm not sure how I feel about the new update. It has some good features but also some bugs.",
            "Das Essen war hervorragend und die Bedienung sehr freundlich.",
        ],
        inputs=[text_input],
    )

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