Update app.py

app.py

@@ -1,7 +1,11 @@
 import gradio as gr
 import logging
+from transformers import pipeline  # For NLP
 from difflib import get_close_matches  # For fuzzy matching
 
+# Load Arabic NLP model for intent classification
+intent_classifier = pipeline("text-classification", model="aubmindlab/bert-base-arabertv02")
+
 # Omdurman National Bank-specific guidelines
 ONB_GUIDELINES = {
     "balance": "يمكنك التحقق من رصيدك عبر الإنترنت أو عبر تطبيق الهاتف الخاص ببنك أم درمان الوطني.",
@@ -15,22 +19,28 @@ ONB_GUIDELINES = {
     "contact": "الاتصال بنا على الرقم 249-123-456-789 أو عبر البريد الإلكتروني [email protected]."
 }
 
-# Map keywords to responses
-KEYWORD_TO_RESPONSE = {
-    "رصيد": "balance",
-    "بطاقة": "lost_card",
-    "قرض": "loan",
-    "تحويل": "transfer",
-    "حساب": "new_account",
-    "فائدة": "interest_rates",
-    "فرع": "branches",
-    "ساعات": "working_hours",
-    "اتصال": "contact"
+# Map intents to responses
+INTENT_TO_RESPONSE = {
+    "balance": "balance",
+    "lost_card": "lost_card",
+    "loan": "loan",
+    "transfer": "transfer",
+    "new_account": "new_account",
+    "interest_rates": "interest_rates",
+    "branches": "branches",
+    "working_hours": "working_hours",
+    "contact": "contact"
 }
 
 # Set up logging for analytics
 logging.basicConfig(filename='chatbot_queries.log', level=logging.INFO)
 
+def classify_intent(message: str):
+    # Use NLP model to classify the user's intent
+    result = intent_classifier(message)
+    intent = result[0]['label']
+    return INTENT_TO_RESPONSE.get(intent, "unknown")
+
 def respond(message: str, history: list):
     # Log the query
     logging.info(f"Query: {message}")
@@ -39,27 +49,19 @@ def respond(message: str, history: list):
     if "القائمة" in message or "menu" in message.lower():
         return "main_menu"
     
-    # Find all matching keywords using fuzzy matching
-    matches = []
-    for keyword in KEYWORD_TO_RESPONSE.keys():
-        if keyword in message:
-            matches.append(keyword)
-        else:
-            # Use fuzzy matching for similar words
-            close_matches = get_close_matches(keyword, message.split(), n=1, cutoff=0.6)
-            if close_matches:
-                matches.append(keyword)
+    # Classify the user's intent using NLP
+    intent = classify_intent(message)
     
-    # If multiple matches are found, return all responses
-    if matches:
-        responses = []
-        for match in matches:
-            key = KEYWORD_TO_RESPONSE.get(match)
-            if key:
-                responses.append(ONB_GUIDELINES.get(key))
-        return "\n\n".join(responses)
+    # If intent is recognized, return the corresponding response
+    if intent != "unknown":
+        return ONB_GUIDELINES.get(intent, "عذرًا، لم يتم التعرف على الخيار المحدد.")
+    
+    # Fallback to keyword matching if NLP doesn't recognize the intent
+    for keyword, key in INTENT_TO_RESPONSE.items():
+        if keyword in message:
+            return ONB_GUIDELINES.get(key, "عذرًا، لم يتم التعرف على الخيار المحدد.")
     
-    # Default response if no keyword is matched
+    # Default response if no intent or keyword is matched
     return "عذرًا، لم أفهم سؤالك. الرجاء إعادة الصياغة أو كتابة 'القائمة' للعودة إلى القائمة الرئيسية."
 
 # Main menu with submenus