Update app.py

app.py

@@ -7,7 +7,7 @@ from nltk.tokenize import word_tokenize
 from nltk.corpus import stopwords
 from nltk.stem import WordNetLemmatizer
 
-# Download NLTK resources (optional if already available)
+# Download NLTK resources (if not already available)
 nltk.download('punkt_tab')
 nltk.download('stopwords')
 nltk.download('wordnet')
@@ -32,8 +32,8 @@ model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-unc
 model.load_state_dict(torch.load("sent_model.pth", map_location=torch.device("cpu")))
 model.eval()
 
-# Label mapping
-idx2label = {0: "negative", 1: "neutral", 2: "positive"}  # Update if needed
+# ✅ Correct label mapping based on LabelEncoder
+idx2label = {0: "negative", 1: "neutral", 2: "positive"}
 
 # Prediction function
 def predict(text):