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src/text_cleaner.py

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+import re
+
+def clean_product_text(text):
+    # Remove quantities 
+    text = re.sub(r'\d+\s*(ml|g|kg|oz|l|lb|fl\.?)\b', '', text, flags=re.IGNORECASE)
+    
+    # Remove special characters 
+    text = re.sub(r'[^\w\s]', '', text)
+    
+    text = re.sub(r'\b\d+\b', '', text)
+    
+    # Remove non-product words
+    stop_phrases = ['net wt', 'net weight', 'volume', 'best before', 'expiry']
+    for phrase in stop_phrases:
+        text = text.replace(phrase, '')
+    
+    # whitespaces
+    text = re.sub(r'\s+', ' ', text).strip()
+    
+    return text
+
+def extract_keywords(text):
+    # extract brand and product details
+    words = text.split()
+    if not words:
+        return "", ""
+    
+    # assign the first word is brand 
+    brand = words[0]
+    
+    # Find longest  product name
+    product_phrase = ""
+    current_phrase = ""
+    
+    for word in words[1:]:
+        if word.isupper() or len(word) > 3:
+            current_phrase += f"{word} "
+        else:
+            if len(current_phrase) > len(product_phrase):
+                product_phrase = current_phrase
+            current_phrase = ""
+    
+    # Fial checking 
+    product_phrase = product_phrase or current_phrase or " ".join(words[1:min(4, len(words))])
+    
+    return brand, product_phrase.strip()

src/utils.py

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+import os
+
+def clean_temp_files():
+    if os.path.exists("temp_img.jpg"):
+        os.remove("temp_img.jpg")