app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from langdetect import detect
from googletrans import Translator

# Multilingual sentiment model
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)

translator = Translator()

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

# Suggested actions in English
ACTIONS = {
    1: "Take a break, reflect on the situation, or seek support.",
    2: "Consider what’s bothering you and try to address it calmly.",
    3: "Maintain balance; you’re feeling neutral, continue as usual.",
    4: "Share your positive experience and stay motivated!",
    5: "Celebrate and spread your joy; keep up the enthusiasm!"
}

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

    # Detect language
    try:
        lang = detect(text)
    except:
        lang = "en"

    # Translate action to detected language
    action_en = ACTIONS.get(stars, "")
    if lang != "en":
        try:
            action_translated = translator.translate(action_en, dest=lang).text
        except:
            action_translated = action_en
    else:
        action_translated = action_en

    return [[sentiment, confidence, action_translated]]

# Example texts including Yoruba
examples = [
    ["I absolutely love this new phone, the camera is stunning!"],       # English
    ["Mo nifẹ́ fíìmù yìí gan-an!"],                                      # Yoruba Positive
    ["Mo bínú gan-an sí ìṣẹ̀lẹ̀ náà."],                                   # Yoruba Negative
    ["Je déteste quand cette application plante sans cesse."],           # French
]

# Gradio UI
demo = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(lines=3, placeholder="Type a sentence in any supported language..."),
    outputs=gr.Dataframe(
        headers=["Emotion (1–5 Stars)", "Confidence", "What to do"],
        row_count=1,
        col_count=(3, "fixed"),
    ),
    examples=examples,
    title="🌍 Multilingual Emotion & Action Analyzer",
    description=(
        "Supports multiple languages including English, Yoruba, French, German, Spanish, etc. "
        "Detects emotion (1–5 stars) and provides suggested actions in the same language as input."
    ),
)

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