Update utils/gemini_explainer.py

utils/gemini_explainer.py

@@ -188,6 +188,61 @@ Keep it under 120 words."""
             return self._generate_fallback_insights(
                 total_spending, total_rewards, optimization_score
             )
+
+    def chat_response(self, message: str, user_context: dict, chat_history: list) -> str:
+        """
+        Generate conversational response using Gemini
+        
+        Args:
+            message: User's question
+            user_context: User profile data (cards, spending, etc.)
+            chat_history: Previous conversation turns
+        
+        Returns:
+            str: Gemini's response
+        """
+        if not self.enabled:
+            return "Gemini AI is currently unavailable. Please check your API configuration."
+        
+        try:
+            # Build context from user data
+            context_str = f"""
+    You are a helpful credit card rewards expert assistant. You're chatting with a user who has the following profile:
+    
+    **User Profile:**
+    - Cards in wallet: {', '.join(user_context.get('cards', ['Unknown']))}
+    - Monthly spending: ${user_context.get('monthly_spending', 0):.2f}
+    - Top spending category: {user_context.get('top_category', 'Unknown')}
+    - Total rewards earned: ${user_context.get('total_rewards', 0):.2f}
+    - Optimization score: {user_context.get('optimization_score', 0)}/100
+    
+    **Your role:**
+    - Answer questions about credit cards, rewards, and optimization strategies
+    - Be conversational, friendly, and concise (2-3 paragraphs max)
+    - Reference the user's specific cards and spending when relevant
+    - Provide actionable advice
+    - If asked about a specific card, explain its benefits and best use cases
+    
+    **Conversation history:**
+    """
+            
+            # Add recent chat history (last 3 turns)
+            for user_msg, bot_msg in chat_history[-3:]:
+                context_str += f"\nUser: {user_msg}\nAssistant: {bot_msg}\n"
+            
+            context_str += f"\n**Current question:** {message}\n\nProvide a helpful, personalized response:"
+            
+            # Generate response
+            response = self.model.generate_content(context_str)
+            
+            if response and response.text:
+                return response.text.strip()
+            else:
+                return "I'm having trouble generating a response. Could you rephrase your question?"
+                
+        except Exception as e:
+            print(f"Gemini chat error: {e}")
+            return "I encountered an error processing your question. Please try asking in a different way."
     
     def _generate_fallback_insights(
         self,