from pathlib import Path
import sys
from types import SimpleNamespace

import gradio as gr

try:
    import spaces
except ImportError:
    def _gpu_decorator(fn=None, *args, **kwargs):
        def decorator(func):
            return func

        if fn is None:
            return decorator
        if callable(fn):
            return fn
        return decorator

    spaces = SimpleNamespace(GPU=_gpu_decorator)

sys.path.insert(0, str(Path(__file__).resolve().parent / "src"))

from multilingual_sentiment_analysis.infer import predict, predict_batch


@spaces.GPU
def analyze_single(text: str):
    if not text or not text.strip():
        return "⚠️ Please enter some text", ""
    try:
        result = predict(text.strip())
        emoji = {"positive": "🟢", "neutral": "🟡", "negative": "🔴"}.get(result["label"], "⚪")
        return f"{emoji} {result['label'].upper()}", f"{result['confidence'] * 100:.1f}%"
    except Exception as error:
        return f"❌ Error: {error}", ""


def analyze_batch(batch_text: str):
    if not batch_text or not batch_text.strip():
        return "⚠️ Please enter texts (one per line)", []
    texts = [line.strip() for line in batch_text.splitlines() if line.strip()]
    if not texts:
        return "⚠️ Please enter at least one text", []
    try:
        results = predict_batch(texts)
        emojis = {"positive": "🟢", "neutral": "🟡", "negative": "🔴"}
        rows = [
            [text[:70] + "..." if len(text) > 70 else text,
             f"{emojis.get(result['label'], '⚪')} {result['label'].upper()}",
             f"{result['confidence'] * 100:.1f}%"]
            for text, result in zip(texts, results)
        ]
        sentiments = [result["label"] for result in results]
        summary = (
            f"✅ Analyzed {len(texts)} texts | 😊 Positive: {sentiments.count('positive')} | "
            f"😐 Neutral: {sentiments.count('neutral')} | 😞 Negative: {sentiments.count('negative')}"
        )
        return summary, rows
    except Exception as error:
        return f"❌ Error: {error}", []


custom_theme = gr.themes.Base(primary_hue="cyan", secondary_hue="slate").set(
    body_background_fill="#000000", body_text_color="#00FFFF",
    button_primary_background_fill="#00FFFF", button_primary_text_color="#000000",
    button_primary_background_fill_hover="#00DDDD", block_title_text_color="#00FFFF",
    block_label_text_color="#00FFFF", input_background_fill="#111111",
    input_border_color="#00FFFF", input_placeholder_color="#666666", border_color_primary="#00FFFF",
)

with gr.Blocks(title="🌍 Multilingual Sentiment Analysis", theme=custom_theme) as demo:
    gr.Markdown("# 🌍 Multilingual Sentiment Analysis")
    gr.Markdown("Analyze sentiment using a fine-tuned XLM-RoBERTa model.")
    with gr.Tabs():
        with gr.TabItem("📝 Single Text"):
            with gr.Row():
                with gr.Column(scale=3):
                    text_input = gr.Textbox(label="Enter text to analyze", placeholder="Type something to analyze...", lines=4)
                with gr.Column(scale=1):
                    analyze_btn = gr.Button("🔍 Analyze", size="lg", variant="primary")
            with gr.Row():
                sentiment_output = gr.Textbox(label="Sentiment", interactive=False)
                confidence_output = gr.Textbox(label="Confidence", interactive=False)
            analyze_btn.click(analyze_single, inputs=text_input, outputs=[sentiment_output, confidence_output])
        with gr.TabItem("📚 Batch Analysis"):
            batch_input = gr.Textbox(label="Enter multiple texts (one per line)", placeholder="Text 1...\nText 2...", lines=8)
            batch_btn = gr.Button("🚀 Batch Analyze", size="lg", variant="primary")
            batch_summary = gr.Textbox(label="Summary", interactive=False)
            batch_results = gr.Dataframe(headers=["Text", "Sentiment", "Confidence"], label="Results", interactive=False)
            batch_btn.click(analyze_batch, inputs=batch_input, outputs=[batch_summary, batch_results])
    gr.Markdown("---\nBuilt with ❤️ using Gradio • XLM-RoBERTa")


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