Create app.py

app.py

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+import gradio as gr
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+
+# 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"),
+]
+
+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":
+        return "✅ POSITIVE 😊"
+    else:
+        return "❌ NEGATIVE 😠"
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=predict_sentiment,
+    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.",
+    theme="default",
+    flagging_mode="never",
+    live=False
+)
+
+demo.launch()