Update app.py

app.py

@@ -2,20 +2,26 @@ import gradio as gr
 from datasets import load_dataset
 from sentence_transformers import SentenceTransformer, util
 import torch
-from itertools import chain
 
 # Load dataset
 dataset = load_dataset("asuender/motivational-quotes", "quotes_extended", split="train")
+
+# Parse data
 quotes = [item["quote"] for item in dataset]
 authors = [item["author"] for item in dataset]
-tags_list = [item["tags"] for item in dataset]
+
+# Since tags are stored as a **comma-separated string**, convert to list
+tags_list = [item["tags"].split(", ") if item["tags"] else [] for item in dataset]
+
+# Get unique tag values
+from itertools import chain
 all_tags = sorted(set(chain.from_iterable(tags_list)))
 
-# Load model
+# Load embedding model
 model = SentenceTransformer("all-MiniLM-L6-v2")
 quote_embeddings = model.encode(quotes, convert_to_tensor=True)
 
-# Define recommendation logic
+# Recommendation function
 def recommend_quote(mood_input, selected_tag):
     filtered = [(q, a, i) for i, (q, a, t) in enumerate(zip(quotes, authors, tags_list)) if selected_tag in t]
     if not filtered:
@@ -36,7 +42,7 @@ def recommend_quote(mood_input, selected_tag):
 
     return result.strip()
 
-# Gradio UI
+# Gradio app
 iface = gr.Interface(
     fn=recommend_quote,
     inputs=[