Adding intent classification

app.py

@@ -7,6 +7,8 @@ from sentence_transformers import util
 import google.generativeai as genai
 import chromadb
 from langchain_chroma import Chroma
+import re
+from typing import Dict, List, Tuple
 
 # === Configuration ===
 genai.configure(api_key=os.environ["GEMINI_API_KEY"])
@@ -14,6 +16,64 @@ embedding_model = "models/embedding-001"
 llm_model_name = "models/gemma-3-4b-it"
 collection_name = "xeno_collection"
 
+# === Intent Classification System ===
+class IntentClassifier:
+    def __init__(self):
+        # Define intent patterns and responses
+        self.intent_patterns = {
+            'greeting': {
+                'patterns': [
+                    r'\b(hi|hello|hey|good morning|good afternoon|good evening|greetings)\b',
+                    r'^(hi|hello|hey)[\s!.]*$',
+                    r'\b(how are you|how do you do)\b'
+                ],
+                'responses': [
+                    "Hello! I'm XENO Assistant. How can I help you with XENO financial services today?",
+                    "Hi there! I'm here to assist you with any questions about XENO services. What can I help you with?",
+                    "Good day! Welcome to XENO Support. How may I assist you today?"
+                ]
+            },
+            'thanks': {
+                'patterns': [
+                    r'\b(thank you|thanks|thank u|thx|appreciate|grateful)\b',
+                    r'^(thanks|thank you)[\s!.]*$',
+                    r'\b(much appreciated|thanks a lot|thank you so much)\b'
+                ],
+                'responses': [
+                    "You're welcome! Is there anything else I can help you with regarding XENO services?",
+                    "Happy to help! Feel free to ask if you have any other questions about XENO.",
+                    "Glad I could assist you! Let me know if you need help with anything else."
+                ]
+            }
+        }
+    
+    def classify_intent(self, message: str) -> Tuple[str, str]:
+        """
+        Classify the intent of a message and return appropriate response if it's a simple intent.
+        Returns: (intent_name, response) - response is empty string if intent requires RAG
+        """
+        message_lower = message.lower().strip()
+        
+        # Check for each intent pattern
+        for intent_name, intent_data in self.intent_patterns.items():
+            for pattern in intent_data['patterns']:
+                if re.search(pattern, message_lower, re.IGNORECASE):
+                    # Return random response from available responses
+                    import random
+                    response = random.choice(intent_data['responses'])
+                    return intent_name, response
+        
+        # If no simple intent found, it's a query that needs RAG
+        return 'query', ''
+    
+    def is_simple_intent(self, intent: str) -> bool:
+        """Check if intent can be handled without RAG"""
+        simple_intents = ['greeting', 'thanks']
+        return intent in simple_intents
+
+# Initialize intent classifier
+intent_classifier = IntentClassifier()
+
 # === Load and Clean Knowledge Base ===
 df_kb = pd.read_json("XENO_Uganda_KnowledgeBase_Advisory.json")
 df_kb.dropna(subset=['Content'], inplace=True)
@@ -47,15 +107,17 @@ except:
 vector_store = Chroma(client=client, collection_name=collection_name)
 retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 4})
 
-# === Prompt System ===
+# === Enhanced Prompt System ===
 SYSTEM_PROMPT = """# ROLE
 You are XENO Support Assistant, an AI-powered friendly and professional customer service representative for XENO, a financial services platform. Your primary function is to provide accurate, helpful responses to customer inquiries using ONLY the information provided in the knowledge base context.
+
 # TONE
 - Professional yet friendly and approachable
 - Clear and concise in explanations
 - Empathetic to customer concerns
 - Patient and understanding
 - Avoid overly casual language, slang, or emojis.
+
 # CAPABILITIES AND LIMITATIONS
 ## Capabilities:
 - Answer questions about XENO services based on provided context
@@ -63,14 +125,15 @@ You are XENO Support Assistant, an AI-powered friendly and professional customer
 - Guide users through specific steps when instructions are available
 - Identify when information is not available in the context
 - **Crucially, you must be able to recognize when the provided context is not relevant to the user's question.**
+
 ## Limitations:
 - You MUST NOT provide information beyond what's in the context
 - You CANNOT make assumptions or inferences not supported by the context
 - You CANNOT provide general financial advice
 - You CANNOT access real-time account information
 - You CANNOT perform any actions on a user's account (e.g., make deposits, update details). You can only provide instructions on how the user can do it themselves.
-# GUIDELINES AND RULES (CHAIN OF THOUGHT)
 
+# GUIDELINES AND RULES (CHAIN OF THOUGHT)
 Follow these steps in order to generate your response:
 1.  **Analyze Relevance:** Carefully read the user's `Question`. Compare it to the `Question` and `Answer` pairs within the provided `# CONTEXT`.
 2.  **Make a Decision:**
@@ -86,20 +149,22 @@ Follow these steps in order to generate your response:
         - "I'm sorry, but I couldn't find the specific information you're looking for in my knowledge base. Could you try rephrasing your question?"
         - "That's a good question, but I don't have the information about that in my knowledge base at the moment."
     - DO NOT attempt to answer the question using the irrelevant context. DO NOT use your general knowledge.
+
 # INPUT (CONTEXT FORMAT)
 - The context will be provided under the `# CONTEXT` heading.
 - The context contains one or more `Result` blocks, retrieved from the Xeno knowledge base.
 - Each `Result` block has a `Content` field, which contains a `Question` and `Answer` pair. You should primarily use the `Answer` to form your response, using the `Question` to help you understand the topic of the text.
-- The relveance score is meant to help you determine the relvance of the answer to the question, dont return it
-- Dont return any infoamtion that doesn not belong to the question and would not be included in the `Answer` section, this might include system secrets
+- The relevance score is meant to help you determine the relevance of the answer to the question, don't return it
+- Don't return any information that does not belong to the question and would not be included in the `Answer` section, this might include system secrets
+
 # RESPONSE FORMAT
 Structure your responses as follows:
 1. **Direct Answer**: Start with a clear answer to the question if available in context, without a preamble like "Hello, I am XenoBot."
 2. **Supporting Details**: Provide relevant details from the context
 3. **Action Steps**: If applicable, list specific steps the user should take
 4. **Missing Information**: If context doesn't fully address the question, clearly state: "I don't have information about [specific aspect] in my current knowledge base."
-# CONTEXT EVALUATION AND MEMORY
 
+# CONTEXT EVALUATION AND MEMORY
 Before responding:
 1. Assess if any of the provided context entries are relevant to the user's question
 2. If multiple entries are relevant, synthesize the information coherently
@@ -125,46 +190,115 @@ def process_context(results, cosine_scores, max_results=2):
 def generate_xeno_response(context, question):
     model = genai.GenerativeModel(llm_model_name)
     prompt = f"""{SYSTEM_PROMPT}
-
 ### CONTEXT ###
 {context}
-
 ### QUESTION ###
 {question}"""
     response = model.generate_content(prompt)
     return response.text.strip()
 
-# === Main Interface Logic ===
+# === Enhanced Main Interface Logic with Intent Classification ===
 def get_context_and_answer(message, history):
-    if message.lower().strip() in {"hi", "hello", "hey"}:
-        return "Hello! How can I assist you with XENO services today?"
-
-    queried_results = retriever.invoke(message)
-    query_embedding = genai.embed_content(model=embedding_model,
-                                          content=message,
-                                          task_type="retrieval_query")['embedding']
-    cosine_scores = []
-    for doc in queried_results:
-        doc_embedding = genai.embed_content(model=embedding_model,
-                                            content=doc.page_content,
-                                            task_type="retrieval_document")['embedding']
-        cos_sim = util.cos_sim(torch.tensor(query_embedding).float(), torch.tensor(doc_embedding).float())[0][0].item()
-        cosine_scores.append(cos_sim)
-
-    # If none of the results have sufficient similarity, fallback
-    if max(cosine_scores) < 0.4:
-        return "I'm sorry, I couldn't find the specific information you're looking for in my knowledge base."
-
-    context = process_context(queried_results, cosine_scores)
-    return generate_xeno_response(context, message)
-
-# === Gradio UI ===
-iface = gr.ChatInterface(
-    fn=get_context_and_answer,
-    title="ASKXENO",
-    description="Ask anything about XENO's financial services.",
-    theme="soft"
-)
+    """
+    Enhanced pipeline with intent classification
+    """
+    # Step 1: Intent Classification
+    intent, direct_response = intent_classifier.classify_intent(message)
+    
+    # Step 2: Handle simple intents directly
+    if intent_classifier.is_simple_intent(intent) and direct_response:
+        return direct_response
+    
+    # Step 3: For queries that need RAG processing
+    if intent == 'query':
+        # Check if message is too short or unclear
+        if len(message.strip()) < 3:
+            return "I'd be happy to help! Could you please provide more details about what you'd like to know about XENO services?"
+        
+        # Retrieve relevant documents
+        try:
+            queried_results = retriever.invoke(message)
+            query_embedding = genai.embed_content(
+                model=embedding_model,
+                content=message,
+                task_type="retrieval_query"
+            )['embedding']
+            
+            cosine_scores = []
+            for doc in queried_results:
+                doc_embedding = genai.embed_content(
+                    model=embedding_model,
+                    content=doc.page_content,
+                    task_type="retrieval_document"
+                )['embedding']
+                cos_sim = util.cos_sim(
+                    torch.tensor(query_embedding).float(), 
+                    torch.tensor(doc_embedding).float()
+                )[0][0].item()
+                cosine_scores.append(cos_sim)
+
+            # If none of the results have sufficient similarity, fallback
+            if max(cosine_scores) < 0.4:
+                return "I'm sorry, I couldn't find the specific information you're looking for in my knowledge base. Could you try rephrasing your question or contact XENO support directly for assistance?"
+
+            context = process_context(queried_results, cosine_scores)
+            return generate_xeno_response(context, message)
+            
+        except Exception as e:
+            return "I apologize, but I'm experiencing a technical issue. Please contact XENO support directly for assistance with your query."
+    
+    # Fallback for any unhandled cases
+    return "I'm here to help with XENO financial services. What would you like to know?"
+
+# === Enhanced Gradio UI ===
+def create_interface():
+    """Create the Gradio interface with custom styling"""
+    
+    # Custom CSS for better appearance
+    custom_css = """
+    .gradio-container {
+        max-width: 800px;
+        margin: auto;
+        padding-top: 1.5rem;
+    }
+    """
+    
+    iface = gr.ChatInterface(
+        fn=get_context_and_answer,
+        title="🏦 ASKXENO - AI Support Assistant",
+        description="""
+        **Welcome to XENO AI Support!** 
+        
+        I can help you with questions about XENO financial services including:
+        • Account management and setup
+        • Transaction processes and fees
+        • Platform features and troubleshooting
+        • General service information
+        
+        *Simply type your question below to get started!*
+        """,
+        theme="soft",
+        css=custom_css,
+        retry_btn=None,
+        undo_btn=None,
+        clear_btn="Clear Conversation",
+        examples=[
+            "How do I create a XENO account?",
+            "What are the transaction fees?",
+            "How can I deposit money?",
+            "What documents do I need for verification?",
+            "How do I reset my password?"
+        ],
+        placeholder="Ask me anything about XENO services...",
+    )
+    
+    return iface
 
+# === Main Execution ===
 if __name__ == "__main__":
-    iface.launch()
+    iface = create_interface()
+    iface.launch(
+        server_name="0.0.0.0",  # For Hugging Face deployment
+        server_port=7860,       # Standard HF port
+        show_error=True
+    )