Update app.py

app.py

@@ -1,35 +1,14 @@
 import gradio as gr
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.naive_bayes import MultinomialNB
+from transformers import pipeline
 
-# Training data
-data = [
-    ("I love this movie!", "positive"),
-    ("This is terrible.", "negative"),
-    ("What a great experience!", "positive"),
-    ("I hate waiting in line.", "negative"),
-    ("The weather is nice today.", "positive"),
-    ("I'm so disappointed.", "negative"),
-    ("It was okay, not great.", "negative"),
-    ("Fantastic service!", "positive"),
-    ("Worst day ever.", "negative"),
-    ("Such a beautiful moment.", "positive"),
-]
+# Load the pretrained sentiment pipeline
+sentiment_pipeline = pipeline("sentiment-analysis")
 
-X = [sentence for sentence, label in data]
-y = [label for sentence, label in data]
-
-vectorizer = TfidfVectorizer()
-X_vectorized = vectorizer.fit_transform(X)
-
-model = MultinomialNB()
-model.fit(X_vectorized, y)
-
-# Prediction function
 def predict_sentiment(text):
-    vector = vectorizer.transform([text])
-    prediction = model.predict(vector)[0]
-    if prediction == "positive":
+    result = sentiment_pipeline(text)[0]
+    label = result["label"]
+    
+    if label == "POSITIVE":
         return "✅ POSITIVE 😊"
     else:
         return "❌ NEGATIVE 😠"
@@ -40,10 +19,9 @@ demo = gr.Interface(
     inputs=gr.Textbox(lines=3, placeholder="Type your sentence here..."),
     outputs="text",
     title="💬 LM Studios Sentiment Detector",
-    description="Type something and see how it *feels*. This AI knows the tone of your message.",
+    description="Now powered by a Hugging Face transformer model for smarter predictions.",
     theme="default",
-    flagging_mode="never",
-    live=False
+    flagging_mode="never"
 )
 
 demo.launch()