Update app.py

app.py

@@ -1,16 +1,37 @@
 import gradio as gr
 import joblib
 
-model = joblib.load("sentiment_model.joblib")
-tfidf = joblib.load("tfidf_vectorizer.joblib")
+# Load model and vectorizer
+model = joblib.load("sentiment_model.pkl")
+tfidf = joblib.load("tfidf_vectorizer.pkl")
 
 def predict_sentiment(text):
+    if not text.strip():
+        return "Please enter some text."
+    
+    # Transform the text using the same TF-IDF vectorizer
     vector = tfidf.transform([text])
+    
+    # Predict sentiment
     prediction = model.predict(vector)[0]
-    return "Positive ✅" if prediction == 1 else "Negative ❌"
+    
+    # Optional: make prediction readable
+    if prediction == 1:
+        label = "😊 Positive"
+    elif prediction == 0:
+        label = "😐 Neutral"
+    else:
+        label = "😠 Negative"
+    
+    return label
 
-gr.Interface(
+# Gradio Interface
+iface = gr.Interface(
     fn=predict_sentiment,
-    inputs="text",
-    outputs="text"
-).launch()
+    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="text",
+    title="Sentiment Classifier",
+    description="Predicts whether a sentence is positive, neutral, or negative using an XGBoost model."
+)
+
+iface.launch()