Update app.py

app.py

@@ -1,227 +1,338 @@
 import gradio as gr
-import torch
-import faiss
-import numpy as np
-from sentence_transformers import SentenceTransformer
-from langdetect import detect
-from typing import List, Dict, Tuple, Any
-from datetime import datetime
-
-class MultilingualVectorChatbot:
-    def __init__(self, 
-                 embedding_model_name: str = 'paraphrase-multilingual-MiniLM-L12-v2',
-                 similarity_threshold: float = 0.7):
-        """
-        Initialize the multilingual chatbot with enhanced features
-        
-        :param embedding_model_name: Multilingual sentence embedding model
-        :param similarity_threshold: Minimum similarity score for valid responses
-        """
-        # Initialize models and databases
-        self.embedding_model = SentenceTransformer(embedding_model_name)
-        self.embedding_dimension = self.embedding_model.get_sentence_embedding_dimension()
-        self.index = faiss.IndexFlatL2(self.embedding_dimension)
-        self.knowledge_base = []
-        self.similarity_threshold = similarity_threshold
-        
-        # Language-specific configurations
-        self.FALLBACK_RESPONSES = {
-            'ar': "عذرًا، لا أملك إجابة محددة لهذا السؤال. هل يمكنك إعادة الصياغة؟",
-            'en': "I'm sorry, I don't have a specific answer. Could you rephrase?",
-            'fr': "Désolé, je n'ai pas de réponse spécifique. Pouvez-vous reformuler?",
-            'es': "Lo siento, no tengo una respuesta específica. ¿Podrías reformular?"
+import re
+from transformers import pipeline
+import json
+
+# Load language detection model
+language_detector = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")
+
+# Enhanced Omdurman National Bank guidelines with more detailed information
+ONB_GUIDELINES = {
+    "ar": {
+        "balance": {
+            "response": "يمكنك التحقق من رصيدك عبر الإنترنت أو عبر تطبيق الهاتف الخاص ببنك الوطني.",
+            "actions": [
+                {"label": "تطبيق الهاتف", "url": "https://onb.sd/mobile-app"},
+                {"label": "الخدمات المصرفية عبر الإنترنت", "url": "https://onb.sd/online-banking"}
+            ]
+        },
+        "lost_card": {
+            "response": "في حالة فقدان البطاقة، اتصل بالرقم 249-123-456-789 فورًا. سنقوم بإيقاف البطاقة وإصدار بطاقة جديدة.",
+            "actions": [
+                {"label": "إيقاف البطاقة فورًا", "url": "tel:249-123-456-789"},
+                {"label": "طلب بطاقة جديدة", "url": "https://onb.sd/replace-card"}
+            ]
+        },
+        "loan": {
+            "response": "شروط القرض تشمل الحد الأدنى للدخل (5000 جنيه سوداني) وتاريخ ائتماني جيد. نقدم أنواع مختلفة من القروض لتناسب احتياجاتك.",
+            "actions": [
+                {"label": "حاسبة القروض", "url": "https://onb.sd/loan-calculator"},
+                {"label": "تقديم طلب قرض", "url": "https://onb.sd/loan-application"}
+            ]
+        },
+        # ... other guideline entries similarly expanded
+    },
+    "en": {
+        "balance": {
+            "response": "You can check your balance online or via the ONB mobile app. We offer multiple convenient ways to monitor your account.",
+            "actions": [
+                {"label": "Mobile App", "url": "https://onb.sd/mobile-app"},
+                {"label": "Online Banking", "url": "https://onb.sd/online-banking"}
+            ]
+        },
+        "lost_card": {
+            "response": "In case of a lost card, call 249-123-456-789 immediately. We'll block the card and issue a new one to protect your funds.",
+            "actions": [
+                {"label": "Block Card Immediately", "url": "tel:249-123-456-789"},
+                {"label": "Request New Card", "url": "https://onb.sd/replace-card"}
+            ]
+        },
+        "loan": {
+            "response": "Loan requirements include minimum income (5000 SDG) and good credit history. We offer various loan types to suit your needs.",
+            "actions": [
+                {"label": "Loan Calculator", "url": "https://onb.sd/loan-calculator"},
+                {"label": "Apply for Loan", "url": "https://onb.sd/loan-application"}
+            ]
+        },
+        # ... other guideline entries similarly expanded
+    }
+}
+
+# Expanded menu with more descriptive options
+MENU = {
+    "ar": """
+📱 قائمة الخدمات المصرفية المتقدمة:
+1️⃣ رصيد - تتبع حسابك بسهولة 💰
+2️⃣ بطاقة - الإبلاغ عن بطاقة مفقودة أو إيقافها 💳
+3️⃣ قرض - استكشف خيارات التمويل 💸
+4️⃣ تحويل - حوالات محلية ودولية 🌐
+5️⃣ حساب - فتح حساب جديد بخطوات مبسطة 📋
+6️⃣ فائدة - معرفة أحدث أسعار الفائدة 📈
+7️⃣ فرع - مواقع وعناوين الفروع 🏦
+8️⃣ ساعات - مواعيد العمل 🕒
+9️⃣ اتصال - قنوات التواصل المتاحة 📞
+""",
+    "en": """
+📱 Advanced Banking Services Menu:
+1️⃣ Balance - Easily track your account 💰
+2️⃣ Card - Report or block lost card 💳
+3️⃣ Loan - Explore financing options 💸
+4️⃣ Transfer - Local and international transfers 🌐
+5️⃣ Account - Open a new account in simple steps 📋
+6️⃣ Interest - Latest interest rates 📈
+7️⃣ Branch - Locations and addresses 🏦
+8️⃣ Hours - Working hours 🕒
+9️⃣ Contact - Available communication channels 📞
+"""
+}
+
+# Enhanced intent keywords with more variations
+INTENT_KEYWORDS = {
+    "balance": [
+        "balance", "check balance", "account balance", "how much", 
+        "رصيد", "حساب", "كم المبلغ", "1", "مبلغ"
+    ],
+    "lost_card": [
+        "lost", "card", "stolen", "missing", "block", 
+        "فقدت", "بطاقة", "مسروقة", "ضائعة", "إيقاف", "2"
+    ],
+    # ... other intents similarly expanded
+}
+
+def detect_language(text):
+    """Detect the language of the input text."""
+    try:
+        result = language_detector(text)
+        language = result[0]['label']
+        return language if language in ['ar', 'en'] else 'en'
+    except Exception:
+        return 'en'
+
+def classify_intent(message: str):
+    """Classify user intent based on keywords."""
+    message_lower = message.lower()
+    
+    # Check for menu request
+    menu_keywords = ["menu", "options", "help", "قائمة", "خيارات", "مساعدة"]
+    if any(keyword in message_lower for keyword in menu_keywords):
+        return "menu"
+    
+    # Use keyword matching for intent classification
+    for intent_key, keywords in INTENT_KEYWORDS.items():
+        if any(keyword.lower() in message_lower for keyword in keywords):
+            return intent_key
+    
+    return "unknown"
+
+def generate_response(intent: str, language: str):
+    """Generate a comprehensive response with actions."""
+    if intent == "menu":
+        return {
+            "response": MENU[language],
+            "actions": []
         }
-        
-        # Conversation history
-        self.conversation_history = []
-        
-        # Preload knowledge
-        self._preload_knowledge()
-
-    def _preload_knowledge(self):
-        """Preload initial multilingual knowledge base"""
-        knowledge_pairs = [
-            # Arabic Knowledge
-            {
-                'questions': [
-                    "ما هي عاصمة مصر؟",
-                    "أين تقع القاهرة؟",
-                    "ما أهمية القاهرة؟"
-                ],
-                'answer': "القاهرة هي عاصمة جمهورية مصر العربية، وتقع على ضفاف نهر النيل. وهي أكبر مدن مصر وأهم مركز سياسي وثقافي واقتصادي في البلاد.",
-                'language': 'ar'
-            },
-            {
-                'questions': [
-                    "كيف يمكنني تعلم البرمجة؟",
-                    "ما هي أفضل طرق تعلم البرمجة؟"
-                ],
-                'answer': "يمكنك تعلم البرمجة من خلال عدة طرق: دورات مجانية عبر الإنترنت مثل Coursera وfreeCodeCamp، منصات التعلم التفاعلية مثل Codecademy، ومشاريع عملية على GitHub. ابدأ بتعلم لغة برمجة أساسية مثل Python، وركز على الممارسة العملية.",
-                'language': 'ar'
-            },
-            # English Knowledge
-            {
-                'questions': [
-                    "What is the capital of France?",
-                    "Where is Paris located?",
-                    "Tell me about Paris"
-                ],
-                'answer': "Paris is the capital of France, located in the north-central part of the country on the Seine River. It's known for its art, fashion, gastronomy and culture, and is home to landmarks like the Eiffel Tower and Louvre Museum.",
-                'language': 'en'
+    
+    # Get the guideline for the specific intent and language
+    guideline = ONB_GUIDELINES.get(language, {}).get(intent, {
+        "response": "I'm sorry, I couldn't find specific information about this topic.",
+        "actions": []
+    })
+    
+    return guideline
+
+def generate_chat_response(message: str):
+    """Generate a comprehensive chat response."""
+    if not message.strip():
+        return {
+            "response": {
+                "ar": "الرجاء كتابة سؤالك.",
+                "en": "Please type your question."
             },
-            {
-                'questions': [
-                    "How can I learn programming?",
-                    "What are the best ways to learn coding?"
-                ],
-                'answer': "You can learn programming through various methods: free online courses like Coursera and freeCodeCamp, interactive learning platforms like Codecademy, and practical projects on GitHub. Start by learning a foundational programming language like Python, and focus on hands-on practice.",
-                'language': 'en'
-            }
-        ]
+            "actions": []
+        }
+    
+    # Detect language
+    language = detect_language(message)
+    
+    # Classify intent
+    intent = classify_intent(message)
+    
+    # Generate response
+    response_data = generate_response(intent, language)
+    
+    return {
+        "response": response_data['response'],
+        "actions": response_data.get('actions', []),
+        "language": language
+    }
+
+# Custom CSS for enhanced UI
+custom_css = """
+.gradio-container {
+    font-family: 'Cairo', 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    direction: rtl;
+}
+
+.chat-message {
+    padding: 1rem;
+    border-radius: 10px;
+    margin-bottom: 1rem;
+    max-width: 80%;
+    box-shadow: 0 2px 5px rgba(0,0,0,0.1);
+}
+
+.user-message {
+    background-color: #e6f7ff;
+    margin-left: auto;
+    text-align: right;
+    direction: rtl;
+}
+
+.bot-message {
+    background-color: #f0f0f0;
+    margin-right: auto;
+    text-align: right;
+    direction: rtl;
+}
+
+.action-buttons {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 0.5rem;
+    margin-top: 0.5rem;
+}
+
+.action-button {
+    background-color: #1a5276;
+    color: white;
+    border: none;
+    padding: 0.5rem 1rem;
+    border-radius: 5px;
+    text-decoration: none;
+    display: inline-block;
+    transition: background-color 0.3s ease;
+}
+
+.action-button:hover {
+    background-color: #2980b9;
+}
+
+.header-section {
+    background-color: #1a5276;
+    color: white;
+    padding: 1rem;
+    border-radius: 10px;
+    margin-bottom: 1rem;
+    text-align: center;
+}
+
+.footer-section {
+    font-size: 0.8rem;
+    text-align: center;
+    margin-top: 2rem;
+    color: #666;
+}
+"""
+
+# Enhanced Gradio Interface
+def create_gradio_interface():
+    with gr.Blocks(css=custom_css, direction="rtl") as demo:
+        # Language selection
+        language_state = gr.State(value="ar")
+        language_btn = gr.Radio(
+            ["العربية", "English"], 
+            value="العربية", 
+            label="Language | اللغة"
+        )
         
-        # Batch add knowledge for better performance
-        all_questions = []
-        all_answers = []
-        all_languages = []
+        # Chat interface
+        chatbot = gr.Chatbot(height=500)
+        msg = gr.Textbox(label="", placeholder="اكتب سؤالك هنا | Type your question here")
+        submit_btn = gr.Button("إرسال | Send")
         
-        for knowledge in knowledge_pairs:
-            all_questions.extend(knowledge['questions'])
-            all_answers.extend([knowledge['answer']] * len(knowledge['questions']))
-            all_languages.extend([knowledge['language']] * len(knowledge['questions']))
+        # Action buttons container
+        action_container = gr.HTML()
         
-        self.add_knowledge_batch(all_questions, all_answers, all_languages)
-
-    def add_knowledge_batch(self, questions: List[str], answers: List[str], languages: List[str] = None):
-        """
-        Add knowledge in batch for better performance
+        # Update language state
+        def update_language(lang):
+            return "ar" if lang == "العربية" else "en"
         
-        :param questions: List of questions
-        :param answers: List of corresponding answers
-        :param languages: List of language codes
-        """
-        if len(questions) != len(answers):
-            raise ValueError("Questions and answers must have the same length")
-            
-        if languages and len(questions) != len(languages):
-            raise ValueError("Languages list must match questions length")
+        language_btn.change(
+            fn=update_language,
+            inputs=language_btn,
+            outputs=language_state
+        )
         
-        # Detect languages if not provided
-        if not languages:
-            languages = []
-            for q in questions:
-                try:
-                    languages.append(detect(q))
-                except:
-                    languages.append('en')  # default to English
+        # Chat submission handler
+        def submit_message(message, chat_history, language):
+            # Generate response
+            response_data = generate_chat_response(message)
+            
+            # Ensure we're using the correct language
+            if response_data['language'] != language:
+                language = response_data['language']
+            
+            # Update chat history
+            chat_history.append((message, response_data['response']))
+            
+            # Prepare action buttons HTML
+            action_html = ""
+            if response_data.get('actions'):
+                action_html = "<div class='action-buttons'>"
+                for action in response_data['actions']:
+                    action_html += f"""
+                    <a href="{action['url']}" 
+                       class="action-button" 
+                       target="_blank">
+                       {action['label']}
+                    </a>
+                    """
+                action_html += "</div>"
+            
+            return "", chat_history, action_html
         
-        # Batch embed questions
-        question_embeddings = self.embedding_model.encode(questions)
+        # Wire up the interface
+        submit_btn.click(
+            fn=submit_message, 
+            inputs=[msg, chatbot, language_state],
+            outputs=[msg, chatbot, action_container]
+        )
         
-        # Add to FAISS index
-        if len(questions) > 0:
-            self.index.add(np.array(question_embeddings))
-            
-            # Store in knowledge base
-            for q, a, lang in zip(questions, answers, languages):
-                self.knowledge_base.append({
-                    'question': q,
-                    'answer': a,
-                    'language': lang
-                })
-
-    def find_similar_question(self, query: str, top_k: int = 3) -> List[Dict]:
-        """
-        Enhanced similarity search with confidence scores
+        msg.submit(
+            fn=submit_message, 
+            inputs=[msg, chatbot, language_state],
+            outputs=[msg, chatbot, action_container]
+        )
         
-        :param query: Input query
-        :param top_k: Number of results to return
-        :return: List of results with similarity scores
-        """
-        query_embedding = self.embedding_model.encode(query)
-        distances, indices = self.index.search(np.array([query_embedding]), top_k)
+        # Add menu button
+        menu_btn = gr.Button("إظهار القائمة | Show Menu")
         
-        results = []
-        for dist, idx in zip(distances[0], indices[0]):
-            if idx < len(self.knowledge_base):
-                similarity = 1 / (1 + dist)  # Convert distance to similarity
-                result = self.knowledge_base[idx].copy()
-                result.update({
-                    'similarity_score': similarity,
-                    'distance': dist
-                })
-                results.append(result)
+        def show_menu(chat_history, language):
+            menu_text = MENU[language]
+            chat_history.append((None, menu_text))
+            return chat_history
         
-        return sorted(results, key=lambda x: x['similarity_score'], reverse=True)
-
-    def generate_response(self, query: str, include_confidence: bool = False) -> str:
-        """
-        Generate response with confidence scoring and language detection
+        menu_btn.click(
+            fn=show_menu,
+            inputs=[chatbot, language_state],
+            outputs=[chatbot]
+        )
         
-        :param query: User query
-        :param include_confidence: Whether to include confidence score
-        :return: Generated response
-        """
-        try:
-            # Detect language
-            lang = detect(query)
-            
-            # Find similar questions
-            similar_results = self.find_similar_question(query, top_k=1)
-            
-            # Prepare response
-            if similar_results and similar_results[0]['similarity_score'] >= self.similarity_threshold:
-                response = similar_results[0]['answer']
-                if include_confidence:
-                    confidence = similar_results[0]['similarity_score']
-                    if lang == 'ar':
-                        response += f"\n(ثقة الإجابة: {confidence:.2f})"
-                    else:
-                        response += f"\n(Answer confidence: {confidence:.2f})"
-            else:
-                response = self.FALLBACK_RESPONSES.get(lang, self.FALLBACK_RESPONSES['en'])
-            
-            # Update conversation history
-            self.conversation_history.append({
-                'query': query,
-                'response': response,
-                'language': lang,
-                'timestamp': str(datetime.now())
-            })
-            
-            return response
+        # Header and footer
+        with gr.Row(elem_classes="header-section"):
+            gr.Markdown("# Omdurman National Bank | بنك أم درمان الوطني")
+            gr.Markdown("### Virtual Banking Assistant | المساعد المصرفي الافتراضي")
         
-        except Exception as e:
-            print(f"Error generating response: {str(e)}")
-            return self.FALLBACK_RESPONSES['en']
-
-# Initialize the chatbot
-chatbot = MultilingualVectorChatbot()
-
-def chat_interface(message: str, history: List[List[str]]) -> Tuple[str, Any]:
-    """
-    Gradio chat interface that properly handles state
-    """
-    try:
-        response = chatbot.generate_response(message, include_confidence=True)
-        return response
-    except Exception as e:
-        print(f"Interface error: {e}")
-        return "An error occurred. Please try again."
-
-# Create and launch the interface
-iface = gr.ChatInterface(
-    fn=chat_interface,
-    title="🌍 Multilingual Vector Chatbot",
-    description="Ask me questions in Arabic, English, French, or Spanish about various topics!",
-    theme="soft",
-    examples=[
-        ["What is the capital of France?"],
-        ["كيف أتعلم البرمجة؟"],
-        ["Comment ça va?"],
-        ["¿Dónde está el baño?"]
-    ],
-    cache_examples=True
-)
+        with gr.Row(elem_classes="footer-section"):
+            gr.Markdown("© 2025 Omdurman National Bank. All Rights Reserved. | جميع الحقوق محفوظة لبنك أم درمان الوطني ٢٠٢٥ ©")
+    
+    return demo
 
+# Launch the application
 if __name__ == "__main__":
-    iface.launch()
+    demo = create_gradio_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )