Update app.py

app.py

@@ -8,7 +8,7 @@ session = ort.InferenceSession(MODEL_FILE)
 tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
 
 labels = [
-    "general", "pharma", "legal", "technical", "UI", "medicine", "it", "marketing", 
+    "general", "pharma", "legal", "technical", "UI", "user interface", "medicine", "information technology", "IT", "marketing", 
     "e-commerce", "programming", "website", "html", "keywords", "food commerce", 
     "personal development", "literature", "poetry", "physics", "chemistry", "biology", 
     "theater", "finance", "sports", "education", "politics", "economics", "art", 
@@ -24,7 +24,7 @@ context_pipeline = pipeline("zero-shot-classification", model="facebook/bart-lar
 def detect_context(input_text):
     result = context_pipeline(input_text, candidate_labels=labels)
     contexts = [label for label, score in zip(result["labels"], result["scores"]) if score > 0.1]
-    print contexts
+    print(contexts)
     return contexts if contexts else ["general"]
 
 def translate_text(input_text, context):