app.py

import streamlit as st
from transformers import pipeline
import pandas as pd

# Page setup
st.set_page_config(page_title="🎭 Emotion Guessing Game", layout="centered")

# CSS for emoji bounce
st.markdown("""
    <style>
    .emoji-bounce {
        font-size: 60px;
        animation: bounce 1s infinite;
        text-align: center;
    }
    @keyframes bounce {
      0%, 100% { transform: translateY(0px); }
      50% { transform: translateY(-10px); }
    }
    </style>
""", unsafe_allow_html=True)

# Emoji for each emotion
emoji_dict = {
    "joy": "😄", "sadness": "😢", "anger": "😠",
    "fear": "😱", "love": "❤️", "surprise": "😲"
}

# Online sound for each emotion
sound_urls = {
    "joy": "https://www.soundjay.com/human/sounds/applause-8.mp3",
    "sadness": "https://www.soundjay.com/human/sounds/sigh-01.mp3",
    "anger": "https://www.soundjay.com/human/sounds/angry-shout-1.mp3",
    "fear": "https://www.soundjay.com/human/sounds/scream-02.mp3",
    "love": "https://www.soundjay.com/button/beep-07.mp3",
    "surprise": "https://www.soundjay.com/button/button-2.mp3"
}

# Load emotion detection model
@st.cache_resource
def load_model():
    return pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")

classifier = load_model()

# Title
st.title("🎭 Emotion Guessing Game")
st.markdown("Type a sentence and let AI guess your emotion!")

# Input
text = st.text_input("💬 How are you feeling today?")

if text:
    result = classifier(text)[0]
    emotion = result["label"]
    confidence = round(result["score"] * 100, 2)
    emoji = emoji_dict.get(emotion, "🤔")

    # Show animated emoji
    st.markdown(f"<div class='emoji-bounce'>{emoji}</div>", unsafe_allow_html=True)
    st.markdown(f"### Emotion Detected: **{emotion}** {emoji}")
    st.markdown(f"Confidence: {confidence}%")

    # 🎊 Confetti for positive emotions
    if emotion in ["joy", "love", "surprise"]:
        st.balloons()

    # 🔊 Auto play sound
    if emotion in sound_urls:
        st.audio(sound_urls[emotion], format="audio/mp3", start_time=0)

    # Show full emotion chart
    all_results = classifier(text)
    df = pd.DataFrame({
        "Emotion": [r["label"] for r in all_results],
        "Score": [round(r["score"] * 100, 2) for r in all_results]
    })
    st.subheader("📊 Emotion Confidence Scores")
    st.bar_chart(df.set_index("Emotion"))