app.py

import gradio as gr
from indic_transliteration import sanscript
from indic_transliteration.sanscript import transliterate
from transformers import pipeline

# 1. Emotion और Sentiment models (public, authentication-free)
emotion_model = pipeline(
    "text-classification",
    model="j-hartmann/emotion-english-distilroberta-base",
    return_all_scores=True
)

sentiment_model = pipeline(
    "text-classification",
    model="nlptown/bert-base-multilingual-uncased-sentiment",
    return_all_scores=True
)

# 2. Hinglish -> Hindi transliteration
def hinglish_to_hindi(text: str) -> str:
    return transliterate(text, sanscript.ITRANS, sanscript.DEVANAGARI)

# 3. Basic Hindi normalization
def normalize_hindi(text: str) -> str:
    corrections = {
        "उदस्": "उदास",
        "प्यर्": "प्यार",
        "भाइयोन्": "भाइयों",
        "कित्न": "कितना",
        "टुझे": "तुझे"
    }
    for wrong, right in corrections.items():
        text = text.replace(wrong, right)
    return text

# 4. Emoji mapping for quick visual
EMOJI_MAP = {
    "anger":"😡","disgust":"🤢","fear":"😱","joy":"😄",
    "neutral":"😐","sadness":"😢","surprise":"😲"
}

SENTIMENT_MAP = {
    "1 star":"😞 Negative",
    "2 stars":"😟 Negative",
    "3 stars":"😐 Neutral",
    "4 stars":"🙂 Positive",
    "5 stars":"😃 Positive"
}

# 5. Complete pipeline
def analyze_text(hinglish_text: str):
    # Transliterate + normalize
    hindi_text = normalize_hindi(hinglish_to_hindi(hinglish_text))
    
    # Emotion prediction
    emotions = emotion_model(hindi_text)[0]
    top_emotion = max(emotions, key=lambda x: x['score'])
    emotion_label = top_emotion['label']
    emotion_score = top_emotion['score']
    emoji = EMOJI_MAP.get(emotion_label.lower(), "❓")
    
    # Sentiment prediction
    sentiments = sentiment_model(hindi_text)[0]
    top_sentiment = max(sentiments, key=lambda x: x['score'])
    sentiment_label = top_sentiment['label']
    sentiment_score = top_sentiment['score']
    sentiment_display = SENTIMENT_MAP.get(sentiment_label, sentiment_label)
    
    # Return readable summary
    return f"हिसाब से भावनाएँ: {emotion_label} {emoji} ({emotion_score:.2f})\nसेंटिमेंट: {sentiment_display} ({sentiment_score:.2f})"

# 6. Gradio interface
iface = gr.Interface(
    fn=analyze_text,
    inputs="text",
    outputs="text",
    title="Hinglish → Hindi Emotion & Sentiment Detector",
    description="Hinglish या Hindi text डालें, परिणाम emoji और readable format में मिलेगा।"
)

iface.launch()