import gradio as gr
from transformers import pipeline
import torch

MODEL_ID = "samandar1105/sentiment-classifier"

LABEL_INFO = {
    "positive": {"emoji": "😊", "description": "Positive Sentiment", "color": "#22c55e"},
    "negative": {"emoji": "😞", "description": "Negative Sentiment", "color": "#ef4444"},
}

EXAMPLES = [
    ["This movie was absolutely breathtaking. One of the best I have ever seen."],
    ["Terrible experience. The product broke after one day."],
    ["I cannot recommend this highly enough — outstanding quality!"],
    ["What a disappointment. The movie was boring."],
]

classifier = pipeline(
    "text-classification",
    model=MODEL_ID,
    top_k=None
)

def analyze_sentiment(text):
    if not text or len(text.strip()) < 5:
        return "<p>Please enter more text.</p>", {}

    # Get all class scores
    result = classifier(text[:512])

    # Handle different output formats
    scores = result[0] if isinstance(result[0], list) else result

    # Sort by confidence
    scores = sorted(scores, key=lambda x: x["score"], reverse=True)

    top = scores[0]
    info = LABEL_INFO[top["label"]]

    html = f"""
    <div style="padding:20px;border-radius:12px;
                background:{info['color']}18;
                border:2px solid {info['color']};
                text-align:center;">
        <div style="font-size:48px;">{info['emoji']}</div>
        <div style="font-size:24px;font-weight:bold;color:{info['color']};">
            {info['description']}
        </div>
        <div style="font-size:18px;">
            Confidence: <strong>{top['score']*100:.1f}%</strong>
        </div>
    </div>
    """

    conf = {
        f"{LABEL_INFO[s['label']]['emoji']} {s['label'].capitalize()}": round(s["score"], 4)
        for s in scores
    }

    return html, conf

with gr.Blocks(title="Sentiment Analyzer", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🎭 Sentiment Analyzer\nPaste any English text to detect **positive 😊** or **negative 😞** sentiment.")
    with gr.Row():
        with gr.Column(scale=3):
            input_text = gr.Textbox(label="📝 Your Text", placeholder="Type or paste text here...", lines=6)
            with gr.Row():
                analyze_btn = gr.Button("🔍 Analyze Sentiment", variant="primary", scale=3)
                gr.ClearButton([input_text], scale=1)
        with gr.Column(scale=2):
            result_html = gr.HTML(label="Result")
            confidence = gr.Label(label="📊 Confidence Scores", num_top_classes=2)
    gr.Examples(examples=EXAMPLES, inputs=input_text, label="💡 Click an example")
    analyze_btn.click(fn=analyze_sentiment, inputs=[input_text], outputs=[result_html, confidence])
    input_text.submit(fn=analyze_sentiment, inputs=[input_text], outputs=[result_html, confidence])

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