app.py

import gradio as gr
from transformers import pipeline

# Load emotion detection model (RoBERTa)
classifier = pipeline(
    "text-classification",
    model="j-hartmann/emotion-english-distilroberta-base",
    top_k=None
)

# Prediction function
def detect_emotion(text):
    results = classifier(text)[0]

    # Get highest score emotion
    best = max(results, key=lambda x: x['score'])
    label = best['label']
    score = round(best['score'], 3)

    # Emoji mapping
    emoji_map = {
        "joy": "😊",
        "anger": "😡",
        "sadness": "😢",
        "fear": "😨",
        "love": "❤️",
        "surprise": "😲"
    }

    emoji = emoji_map.get(label.lower(), "")

    return f"{emoji} Emotion: {label} (Confidence: {score})"

# Gradio UI
app = gr.Interface(
    fn=detect_emotion,
    inputs=gr.Textbox(lines=3, placeholder="Enter text..."),
    outputs="text",
    title="RoBERTa Emotion Detection",
    description="Detect emotions like joy, anger, sadness, fear, love, surprise"
)

app.launch()