Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 import pandas as pd
-from rapidfuzz import process
+from rapidfuzz import fuzz, process  # Importing valid functions for fuzzy matching
 
 # Pre-download the model
 model_name = "sentence-transformers/all-MiniLM-L6-v2"
@@ -20,18 +20,28 @@ data = df['text'].dropna().tolist()
 def autocomplete(query):
     if not query.strip():
         return []  # Return empty if query is blank
-    matches = process.extract(query, data, scorer=process.WRatio, limit=5)
+
+    # Use fuzz.partial_ratio for fuzzy matching
+    matches = process.extract(query, data, scorer=fuzz.partial_ratio, limit=5)
     return "\n".join([match[0] for match in matches])  # Return matches as a multi-line string
 
+# Semantic search function
+def semantic_search(query):
+    if not query.strip():
+        return []  # Return empty if query is blank
+    query_embedding = model.encode(query, convert_to_tensor=True)
+    results = util.semantic_search(query_embedding, embeddings, top_k=5)
+    return [data[result['corpus_id']] for result in results[0]]
+
 # Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("### Typo-Tolerant Autocomplete")
+    gr.Markdown("### Typo-Tolerant Autocomplete & Semantic Search")
 
-    # Textbox without live=True
-    query = gr.Textbox(label="Start typing for autocomplete")
+    # Real-time autocomplete
+    query = gr.Textbox(label="Start typing for autocomplete", live=True)
     autocomplete_output = gr.Textbox(label="Autocomplete Suggestions", lines=5)
 
-    # Trigger autocomplete on each input change
+    # Autocomplete on change
     query.change(fn=autocomplete, inputs=query, outputs=autocomplete_output)
 
 demo.launch()