diff --git a/Dockerfile b/Dockerfile
index 1f753b9..081ccf4 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -5,8 +5,10 @@ WORKDIR /app
 
 COPY requirements.txt .
 
+# Install system dependencies required for building Python packages
 RUN apt-get update && apt-get install -y --no-install-recommends gcc libffi-dev libssl-dev
-RUN pip install asyncio aiohttp redis[hiredis] 
+
+# Install Python dependencies including redis with hiredis support
 RUN pip install --no-cache-dir -r requirements.txt
 
 # Stage 2: Final stage
@@ -15,7 +17,10 @@ FROM python:slim
 WORKDIR /app
 
 # Copy only necessary files from the builder stage
+COPY --from=builder /usr/local/lib/python/dist-packages /usr/local/lib/python/dist-packages
+COPY --from=builder /usr/local/lib/python3/dist-packages /usr/local/lib/python3/dist-packages
 COPY --from=builder /usr/local/lib/python3.*/dist-packages /usr/local/lib/python3.*/dist-packages
+
 COPY . .
 
 EXPOSE 8080
diff --git a/nlp/normalize.py b/nlp/normalize.py
index 319cc58..e89ac0f 100644
--- a/nlp/normalize.py
+++ b/nlp/normalize.py
@@ -8,11 +8,14 @@ model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
 
 def is_russian_wording(text):
     """
-    Check if the text contains any Russian characters by checking
+    Check if the text contains more than one Russian characters by checking
     each character against the Unicode range for Cyrillic.
     """
+    counter = 0
     for char in text:
         if "\u0400" <= char <= "\u04ff":  # Unicode range for Cyrillic characters
+            counter += 1
+        if counter > 1:
             return True
     return False
 
@@ -39,17 +42,15 @@ def normalize(text):
     Normalize English text to resemble Russian characters.
     """
     # Segment the text first
-    segmented_text = segment_text(
+    t = segment_text(
         text.replace("  ", " ").replace("  ", " ").replace("  ", " ")
     )
 
-    # Normalize after segmentation
-    segmented_text = segmented_text.lower()
-
-    if is_russian_wording(segmented_text):
+    if is_russian_wording(t):
         # Normalize the text by replacing characters
         normalized_text = (
-            segmented_text.replace("e", "е")
+            t.lower()
+            .replace("e", "е")
             .replace("o", "о")
             .replace("x", "х")
             .replace("a", "а")
@@ -74,7 +75,7 @@ def normalize(text):
 
         return normalized_text
 
-    return segmented_text
+    return t
 
 
 # Example usage