Update app.py

app.py

@@ -3,16 +3,15 @@ import requests
 import random
 from transformers import pipeline
 
-# Load the zero-shot classification model
+
 classifier = pipeline("zero-shot-classification", model="valhalla/distilbart-mnli-12-3")
 
-# Simplified general labels for better accuracy
 labels = [
     "animals", "people", "places", "history", "science", "art", "technology",
     "sports", "food", "clothing", "home", "entertainment", "education", "nature", "transportation"
 ]
 
-# Preprocessing for better classification context
+
 def preprocess_topic(topic):
     topic = topic.lower().strip()
     mapping = {
@@ -35,13 +34,12 @@ def preprocess_topic(topic):
     }
     return mapping.get(topic, topic)
 
-# Random topics list
 random_topics = [
     "cats", "space", "chocolate", "Egypt", "Leonardo da Vinci",
     "volcanoes", "Tokyo", "honeybees", "quantum physics", "orcas"
 ]
 
-# Wikipedia + classification function
+
 def get_wikipedia_facts(topic):
     if not topic.strip():
         return "Please enter a topic or use 'Surprise me!'", None, None
@@ -80,7 +78,7 @@ def get_wikipedia_facts(topic):
         facts = [fact if fact.endswith(".") else fact + "." for fact in facts]
         facts_text = "\n\n".join(f"💡 {fact}" for fact in facts)
 
-        # Zero-shot classification with general labels
+       
         processed_input = preprocess_topic(topic)
         classification = classifier(processed_input, candidate_labels=labels)
         top_labels = classification["labels"][:3]
@@ -98,12 +96,12 @@ def get_wikipedia_facts(topic):
         print("Error:", e)
         return "Oops! Something went wrong while fetching your facts.", None, None
 
-# Surprise me button
+
 def surprise_topic(_):
     topic = random.choice(random_topics)
     return get_wikipedia_facts(topic)
 
-# Gradio UI
+
 with gr.Blocks() as demo:
     gr.HTML("""
     <style>