Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import gradio as gr
 import joblib
 import re
@@ -6,13 +5,15 @@ import nltk
 from nltk.corpus import stopwords
 from nltk.tokenize import word_tokenize
 
-# Load saved model and vectorizer
+# Ensure NLTK resources are downloaded
+nltk.download('punkt', quiet=True)
+nltk.download('stopwords', quiet=True)
+
+# Load model and vectorizer
 model = joblib.load("sentiment_model.pkl")
 vectorizer = joblib.load("tfidf_vectorizer.pkl")
 
-nltk.download('punkt')
-nltk.download('stopwords')
-
+# Preprocessing function
 def preprocess(text):
     text = str(text).lower()
     text = re.sub(r'[^a-z\s]', '', text)
@@ -20,12 +21,17 @@ def preprocess(text):
     tokens = [t for t in tokens if t not in stopwords.words('english')]
     return ' '.join(tokens)
 
+# Prediction function
 def predict_sentiment(text):
-    processed = preprocess(text)
-    vectorized = vectorizer.transform([processed])
-    prediction = model.predict(vectorized)[0]
-    return prediction
+    try:
+        processed = preprocess(text)
+        vectorized = vectorizer.transform([processed])
+        prediction = model.predict(vectorized)[0]
+        return prediction
+    except Exception as e:
+        return f"Error: {str(e)}"
 
+# Gradio interface
 demo = gr.Interface(
     fn=predict_sentiment,
     inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
@@ -34,4 +40,4 @@ demo = gr.Interface(
     description="Classify text as Positive, Negative, or Neutral"
 )
 
-demo.launch()
+demo.launch()