Update services/utils.py

services/utils.py

@@ -12,7 +12,7 @@ nltk.download('wordnet')
 stop_words = set(stopwords.words('english'))
 lemmatizer = WordNetLemmatizer()
 model = SentenceTransformer('all-mpnet-base-v2')
-
+clip_model = SentenceTransformer('clip-ViT-B-32')
 
 def clean_text(text):
     # Lowercase
@@ -28,14 +28,35 @@ def clean_text(text):
     cleaned_text = ' '.join(words)
     return cleaned_text
 
-
-def encode_and_normalize(text):
-    vector = model.encode(text)
-    normalized_vector = vector / np.linalg.norm(vector)
-    return normalized_vector
-
 def extract_order_id_from_query(text):
     match = re.search(r'\bB-\d+\b', text)
     if match:
         return match.group(0)
     return None
+
+def generate_text_embedding(text):
+    try:
+        text_embedding = clip_model.encode(text, convert_to_tensor=True)
+        return text_embedding.cpu().numpy() 
+    except Exception as e:
+        print(f"Error processing text '{text}': {e}")
+        return np.zeros((512,))
+    
+def generate_image_embedding(image_path):
+    try:
+        image = Image.open(image_path)
+        image = image.convert('RGB')
+        image_embedding = clip_model.encode(image, convert_to_tensor=True)
+        return image_embedding.cpu().numpy()  # Convert to numpy array for easy storage
+    except Exception as e:
+        print(f"Error processing image from {image_path}: {e}")
+        return np.zeros((512,)) 
+
+def clear_chat():
+    return []
+
+
+def undo_last_message(chatbot):
+    if chatbot:
+        chatbot.pop()
+    return chatbot