Update app.py

app.py

@@ -34,16 +34,20 @@ autocomplete_embeddings = model.encode(autocomplete_data, convert_to_tensor=True
 def find_synonym(word, top_n=1):
     query_embedding = model.encode(word, convert_to_tensor=True)
     results = util.semantic_search(query_embedding, autocomplete_embeddings, top_k=top_n)
-    return [autocomplete_data[result['corpus_id']] for result in results[0]]
+    
+    if results[0]:
+        return [autocomplete_data[result['corpus_id']] for result in results[0] if result['score'] > 0.6]  # Only return synonyms with good confidence
+    return [word]  # Return original word if no synonym is found
 
 # Function to correct spellings
 def correct_spelling(word):
-    matches = process.extract(word, autocomplete_data, scorer=fuzz.ratio, limit=1)
-    if matches:  # Ensure a match exists
-        best_match = matches[0][0]  # Extract the best match string
-        score = matches[0][1]  # Extract the match score
-        return best_match if score > 80 else word  # Only correct if confidence > 80%
-    return word  # Return the original word if no match found
+    matches = process.extract(word, autocomplete_data, scorer=fuzz.partial_ratio, limit=3)
+    
+    if matches:
+        best_match, score, _ = matches[0]  # Extract the best match string & score
+        if score > 70:  # Lowered threshold to allow for more typo tolerance
+            return best_match
+    return word  # Return the original word if no good match is found
 
 # Autocomplete function with tracking
 def autocomplete(query):