# ✅ Final Version – TweetPulse AI Sentiment Analyzer (Hugging Face Compatible)
# 🎯 Sound system automatically disabled for online run
# 💜 Created by Isneha Varshney

import streamlit as st
import joblib
from PIL import Image
import threading
import time
import os

# Try importing playsound (works only on local system)
try:
    from playsound import playsound
    SOUND_ENABLED = True
except Exception:
    SOUND_ENABLED = False

# ------------------------------------------
# Load trained model and TF-IDF vectorizer
# ------------------------------------------
model = joblib.load("sentiment_model.pkl")
vectorizer = joblib.load("tfidf_vectorizer.pkl")

# ------------------------------------------
# Function: Play sound for few seconds (only for local)
# ------------------------------------------
def play_sound_limited(sound_file, duration=3):
    if not SOUND_ENABLED:
        return
    def play():
        try:
            playsound(sound_file)
        except Exception:
            pass
    t = threading.Thread(target=play)
    t.start()
    time.sleep(duration)
    os.system("taskkill /IM wmplayer.exe /F >nul 2>&1")

# ------------------------------------------
# Streamlit Page Setup
# ------------------------------------------
st.set_page_config(page_title="TweetPulse AI 💬", page_icon="💫", layout="centered")

st.markdown("""
    <h1 style='text-align:center; color:#6a0dad;'>💫 TweetPulse AI - Sentiment Analyzer 💫</h1>
    <h4 style='text-align:center; color:gray;'>Analyze tweet emotions instantly ⚡</h4>
""", unsafe_allow_html=True)

# Stylish Input Box
tweet = st.text_area(
    "✍️ Type your tweet below:",
    placeholder="e.g. I absolutely loved this movie! 🎬",
    height=120,
    help="Type any sentence or tweet to analyze its emotion."
)

# ------------------------------------------
# Predict Sentiment
# ------------------------------------------
if st.button("🔍 Analyze Sentiment"):
    if tweet.strip() == "":
        st.warning("⚠️ Please type something to analyze.")
    else:
        tweet_vector = vectorizer.transform([tweet])
        prediction = model.predict(tweet_vector)[0]

        if prediction == "positive":
            img = Image.open("positive.png")
            st.image(img, width=180)
            st.success("🎉 Sentiment Detected: **Positive 😍**")
            play_sound_limited("positive.mp3", duration=3)

        elif prediction == "negative":
            img = Image.open("negative.png")
            st.image(img, width=180)
            st.error("💢 Sentiment Detected: **Negative 😡**")
            play_sound_limited("negative.mp3", duration=3)

        else:
            img = Image.open("neutral.png")
            st.image(img, width=180)
            st.info("😐 Sentiment Detected: **Neutral 😐**")
            play_sound_limited("neutral.mp3", duration=3)

# ------------------------------------------
# Footer
# ------------------------------------------
st.markdown("---")
st.caption("💜 Created by **Isneha Varshney** | Powered by TweetPulse AI")