improved the scoring

similarity_fast.py

@@ -66,16 +66,21 @@ class SimilarityFast:
         input_embedding = generate_embedding(self.model, input_word_clean)
         similarities = []
 
-        contains_cooked = 'cooked' in input_word_clean.lower()
+        adjustment_factors = {
+            'cooked': 'cooked' in input_word_clean.lower(),
+            'frozen': 'frozen' in input_word_clean.lower(),
+            'canned': 'canned' in input_word_clean.lower() or 'can' in input_word_clean.lower(),
+            'can': 'canned' in input_word_clean.lower() or 'can' in input_word_clean.lower()
+        }
 
         for key, val in self.dictionary_embeddings.items():
             similarity_score = cosine_similarity(input_embedding, val['v'])
-            if contains_cooked and 'cooked' in key.lower():
-                similarity_score *= 1.05
+            for term, is_present in adjustment_factors.items():
+                if is_present and term in key.lower():
+                    similarity_score *= 1.07
             similarities.append((key, val['d'], similarity_score))
         
         most_similar_word, dictionary_word, highest_score = max(similarities, key=lambda x: x[2])
-        
         confidence_score, similar_words_str = calculate_confidence_and_similar_words_str(similarities, highest_score)
 
         return most_similar_word, dictionary_word, highest_score, confidence_score, similar_words_str