import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Multilingual model (10+ languages)
MODEL = "nlptown/bert-base-multilingual-uncased-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
sentiment_model = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# Map stars (1–5) to emotion labels with emojis
STAR_EMOJIS = {
    1: "😡 Very Negative",
    2: "☹️ Negative",
    3: "😐 Neutral",
    4: "🙂 Positive",
    5: "🤩 Very Positive"
}

def analyze_sentiment(text):
    result = sentiment_model(text)[0]
    stars = int(result["label"][0])   # "1 star" → 1
    sentiment = STAR_EMOJIS.get(stars, result["label"])
    confidence = f"{result['score']:.2f}"
    return [[sentiment, confidence]]

# Example texts in different languages including Yoruba
examples = [
    ["I absolutely love this new phone, the camera is stunning!"],       # English
    ["Je déteste quand cette application plante sans cesse."],           # French
    ["Das Essen in diesem Restaurant war fantastisch!"],                 # German
    ["Este producto es muy malo y no funciona."],                        # Spanish
    ["Questo film è stato noioso e troppo lungo."],                      # Italian
    ["Eu gostei muito do serviço, foi excelente!"],                      # Portuguese
    ["Эта книга ужасна, я еле её дочитал."],                             # Russian
    ["هذا الهاتف رائع للغاية، أنا سعيد جدًا به."],                        # Arabic
    ["この映画は本当に面白かった！"],                                       # Japanese
    ["De app werkt prima, maar kan beter."],                             # Dutch
    ["Mo nifẹ́ fíìmù yìí gan-an!"],                                      # Yoruba Positive
    ["Mo kọ́ láti rí ìrírí tó dáa nínú iṣẹ́ yìí."],                     # Yoruba Neutral
    ["Mo bínú gan-an sí ìṣẹ̀lẹ̀ náà."],                                   # Yoruba Negative
]

# Gradio UI
demo = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(lines=3, placeholder="Type a sentence here in one of 11+ languages..."),
    outputs=gr.Dataframe(
        headers=["Emotion (1–5 Stars)", "Confidence"],
        row_count=1,
        col_count=(2, "fixed"),
    ),
    examples=examples,
    title="🌍 Multilingual Emotion & Sentiment Analyzer",
    description=(
        "Supports 11+ languages (English, French, German, Spanish, Italian, Dutch, "
        "Portuguese, Russian, Arabic, Japanese, Yoruba). Detects fine-grained emotions "
        "with 5 levels:\n\n"
        "😡 Very Negative | ☹️ Negative | 😐 Neutral | 🙂 Positive | 🤩 Very Positive"
    ),
)

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