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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
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
]
# Gradio UI
demo = gr.Interface(
fn=analyze_sentiment,
inputs=gr.Textbox(lines=3, placeholder="Type a sentence here in one of 10 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 10+ languages (English, French, German, Spanish, Italian, Dutch, "
"Portuguese, Russian, Arabic, Japanese). Detects fine-grained emotions "
"with 5 levels:\n\n"
"😡 Very Negative | ☹️ Negative | 😐 Neutral | 🙂 Positive | 🤩 Very Positive"
),
)
if __name__ == "__main__":
demo.launch()
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