Update app.py

app.py

@@ -4,22 +4,22 @@ import random
 import time
 from transformers import pipeline
 
-# Load zero-shot classification model
+# Load the zero-shot classification pipeline
 classifier = pipeline("zero-shot-classification", model="valhalla/distilbart-mnli-12-3")
 
-# Random topic list
+# List of random topics for the "Surprise me" button
 random_topics = [
     "cats", "space", "chocolate", "Egypt", "Leonardo da Vinci",
     "volcanoes", "Tokyo", "honeybees", "quantum physics", "orcas"
 ]
 
-# Main function
+# Main function: get Wikipedia extract, image, and classify topic
 def get_wikipedia_facts(topic):
     if not topic.strip():
         return "Please enter a topic or use 'Surprise me!'", None, None
 
     headers = {
-        "User-Agent": "RandomFactApp/3.0"
+        "User-Agent": "RandomFactApp/3.0 (https://huggingface.co/spaces/yourname) Python requests"
     }
 
     search_url = "https://en.wikipedia.org/w/api.php"
@@ -32,16 +32,18 @@ def get_wikipedia_facts(topic):
     }
 
     try:
+        # Step 1: Search Wikipedia
         search_response = requests.get(search_url, params=search_params, headers=headers)
         time.sleep(0.3)
         search_data = search_response.json()
 
         search_hits = search_data.get("query", {}).get("search", [])
         if not search_hits:
-            return f"No results for '{topic}'.", None, None
+            return f"Sorry, no information found for '{topic}'.", None, None
 
         best_title = search_hits[0]["title"]
 
+        # Step 2: Get page extract and image
         extract_params = {
             "action": "query",
             "format": "json",
@@ -62,15 +64,21 @@ def get_wikipedia_facts(topic):
         extract_text = page.get("extract", "")
 
         if not extract_text:
-            return f"No facts found for '{topic}'.", None, None
+            return f"Sorry, no extract found for '{topic}'.", None, None
 
+        # Format extract into short facts
         sentences = [s.strip() for s in extract_text.replace("\n", " ").split(". ") if s.strip()]
+        if not sentences:
+            return f"Sorry, no facts available for '{topic}'.", None, None
+
         facts = random.sample(sentences, min(3, len(sentences)))
         facts = [fact if fact.endswith(".") else fact + "." for fact in facts]
         facts_text = "\n\n".join(f"💡 {fact}" for fact in facts)
 
+        # Get image URL
         image_url = page.get("thumbnail", {}).get("source", None)
 
+        # Zero-shot classification
         labels = ["history", "science", "technology", "art", "geography", "biology", "music", "sports", "politics"]
         classification = classifier(topic, labels)
         top_label = classification["labels"][0]
@@ -81,21 +89,24 @@ def get_wikipedia_facts(topic):
 
     except Exception as e:
         print("Error:", e)
-        return "Something went wrong. Please try again.", None, None
+        return "Oops! Something went wrong while fetching your facts.", None, None
 
-# Surprise function
+# Surprise topic function
 def surprise_topic(_):
     topic = random.choice(random_topics)
     return get_wikipedia_facts(topic)
 
-# Gradio UI
-with gr.Blocks(css="style.css") as demo:
+# Gradio UI layout
+with gr.Blocks() as demo:
     gr.Markdown("""
-# ☁️ Smart Wikipedia Fact Finder  
-Discover fun facts, a related image, and AI-generated topic category!
+# 🌍 Smart Wikipedia Fact Finder  
+Search any topic and discover:
+- 📚 Three interesting facts
+- 🖼️ A related image
+- 🧠 AI-predicted topic category
 
-Type a topic (like `quantum physics`) or click **🎲 Surprise me!**
-""")
+👉 Try something like **"koalas"**, **"quantum physics"**, or click **🎲 Surprise me!**
+    """)
 
     with gr.Row():
         with gr.Column(scale=3):
@@ -110,16 +121,16 @@ Type a topic (like `quantum physics`) or click **🎲 Surprise me!**
             facts_output = gr.Textbox(label="📚 Wikipedia Facts", lines=6)
             classification_output = gr.Textbox(label="🧠 Topic Classification")
         with gr.Column():
-            image_output = gr.Image(label="🖼️ Wikipedia Image")
+            image_output = gr.Image(label="🖼️ Related Image")
 
+    # Link functions to inputs
     topic_input.submit(get_wikipedia_facts, inputs=topic_input, outputs=[facts_output, image_output, classification_output])
     surprise_button.click(surprise_topic, inputs=None, outputs=[facts_output, image_output, classification_output])
 
+# Run the app
 if __name__ == "__main__":
     demo.launch()
 
 
-
-