upadate app.py

app.py

@@ -1,64 +1,80 @@
-# ✅ Final Version – TweetPulse AI Sentiment Analyzer (Hugging Face Compatible)
-# 🎯 Sound system automatically disabled for online run
-# 💜 Created by Isneha Varshney
-
+# 💫 TweetPulse AI - Sentiment Analyzer (Animated Edition)
 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
-# ------------------------------------------
+
+# -------------------------------
+# Load trained model & 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")
 
+# Custom CSS for animations
+st.markdown("""
+    <style>
+    body {
+        background-color: #f8f8ff;
+    }
+    .pulse {
+        animation: pulse 1.5s infinite;
+    }
+    @keyframes pulse {
+        0% { transform: scale(1); opacity: 0.8; }
+        50% { transform: scale(1.05); opacity: 1; }
+        100% { transform: scale(1); opacity: 0.8; }
+    }
+    .flash-green {
+        background: rgba(0,255,0,0.15);
+        animation: flash-green 1.5s;
+    }
+    @keyframes flash-green {
+        from {background-color: #d4edda;}
+        to {background-color: white;}
+    }
+    .flash-red {
+        background: rgba(255,0,0,0.15);
+        animation: flash-red 1.5s;
+    }
+    @keyframes flash-red {
+        from {background-color: #f8d7da;}
+        to {background-color: white;}
+    }
+    .flash-blue {
+        background: rgba(0,0,255,0.1);
+        animation: flash-blue 1.5s;
+    }
+    @keyframes flash-blue {
+        from {background-color: #d1ecf1;}
+        to {background-color: white;}
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# -------------------------------
+# Header
+# -------------------------------
 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
+# -------------------------------
+# 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.")
@@ -67,32 +83,31 @@ if st.button("🔍 Analyze Sentiment"):
         prediction = model.predict(tweet_vector)[0]
 
         if prediction == "positive":
+            st.markdown("<div class='flash-green pulse'>", unsafe_allow_html=True)
             img = Image.open("positive.png")
-            st.image(img, width=180)
+            st.image(img, width=200)
             st.success("🎉 Sentiment Detected: **Positive 😍**")
-            play_sound_limited("positive.mp3", duration=3)
+            st.markdown("</div>", unsafe_allow_html=True)
 
         elif prediction == "negative":
+            st.markdown("<div class='flash-red pulse'>", unsafe_allow_html=True)
             img = Image.open("negative.png")
-            st.image(img, width=180)
+            st.image(img, width=200)
             st.error("💢 Sentiment Detected: **Negative 😡**")
-            play_sound_limited("negative.mp3", duration=3)
+            st.markdown("</div>", unsafe_allow_html=True)
 
         else:
+            st.markdown("<div class='flash-blue pulse'>", unsafe_allow_html=True)
             img = Image.open("neutral.png")
-            st.image(img, width=180)
+            st.image(img, width=200)
             st.info("😐 Sentiment Detected: **Neutral 😐**")
-            play_sound_limited("neutral.mp3", duration=3)
+            st.markdown("</div>", unsafe_allow_html=True)
 
-# ------------------------------------------
+# -------------------------------
 # Footer
-# ------------------------------------------
+# -------------------------------
 st.markdown("---")
-st.caption("💜 Created by **Isneha Varshney** | Powered by TweetPulse AI")
-
-
-
-
+st.caption("💜 Created by **Isneha Varshney** | Powered by TweetPulse AI 💫")