Create app.py

app.py

@@ -0,0 +1,197 @@
+import gradio as gr
+import os
+import json
+from dotenv import load_dotenv
+from langchain_anthropic import ChatAnthropic
+
+load_dotenv()
+
+class LegionMariaAssistant:
+    def __init__(self):
+        # Router LLM - lightweight for section classification
+        self.router_llm = ChatAnthropic(
+            anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"),
+            model="claude-3-5-haiku-20241022",
+            temperature=0.1
+        )
+        
+        # Response LLM - for generating final answers
+        self.response_llm = ChatAnthropic(
+            anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"),
+            model="claude-3-5-haiku-20241022",
+            temperature=0.1  # Lower temperature for more consistent, direct responses
+        )
+        
+        self.data_content = {}
+        self.load_data()
+    
+    def load_data(self):
+        """Load structured JSON data"""
+        data_file = "./data.json"
+        if os.path.exists(data_file):
+            try:
+                with open(data_file, 'r', encoding='utf-8') as f:
+                    self.data_content = json.load(f)
+                
+                print("Loaded Legion Maria JSON data successfully")
+                print(f"Available sections: {list(self.data_content.keys())}")
+                
+            except Exception as e:
+                print(f"Error loading data: {str(e)}")
+                self.data_content = {}
+        else:
+            print("data.json not found")
+            self.data_content = {}
+    
+    def route_query(self, message):
+        """Router LLM decides which section of data to use"""
+        available_sections = list(self.data_content.keys())
+        
+        router_prompt = f"""You are a query router for the Legion Maria Youth Affairs system. 
+        
+Available data sections: {available_sections}
+
+Each section contains:
+- about: mission, vision, core values, organizational information
+- office: projects, community outreach, operational details
+- leadership: organizational structure, leadership team, roles
+
+User query: "{message}"
+
+Respond with ONLY the most relevant section name from the available sections. If the query spans multiple sections or is general, respond with "general".
+
+Examples:
+- "Who is the director?" -> leadership
+- "What is your mission?" -> about
+- "Tell me about your projects" -> office
+- "What do you do?" -> general
+
+Section:"""
+
+        try:
+            response = self.router_llm.invoke([{"role": "user", "content": router_prompt}])
+            section = response.content.strip().lower()
+            
+            # Validate section exists
+            if section in available_sections:
+                return section
+            elif section == "general":
+                return "general"
+            else:
+                return "about"  # Default fallback
+                
+        except Exception as e:
+            print(f"Router error: {str(e)}")
+            return "about"  # Default fallback
+    
+    def chat_response(self, message, history):
+        """Two-tier LLM system: Router + Specialist response"""
+        if not message.strip():
+            return "Please ask me something about Legion Maria Youth Affairs!"
+        
+        try:
+            if not self.data_content:
+                return "I don't have information available to answer that question."
+            
+            # Step 1: Router LLM decides which section to use
+            selected_section = self.route_query(message)
+            print(f"Router selected section: {selected_section}")
+            
+            # Step 2: Get relevant data based on routing decision
+            if selected_section == "general":
+                # Use all data for general queries
+                relevant_data = self.data_content
+            else:
+                # Use only the specific section
+                relevant_data = {selected_section: self.data_content.get(selected_section, {})}
+            
+            # Build conversation context
+            conversation_context = ""
+            if history:
+                conversation_context = "Previous conversation:\n"
+                for user_msg, assistant_msg in history[-3:]:  # Keep last 3 exchanges
+                    conversation_context += f"User: {user_msg}\nAssistant: {assistant_msg}\n\n"
+                conversation_context += "Current conversation:\n"
+            
+            # Step 3: Response LLM generates answer using only relevant data
+            response_prompt = f"""You are a concise assistant for the Legion Maria Directorate of Youth Affairs. Provide brief, direct answers.
+
+Relevant Information:
+{json.dumps(relevant_data, indent=2)}
+
+{conversation_context}User: {message}
+
+Guidelines:
+- Keep responses SHORT (1-3 sentences maximum)
+- Be direct and to the point
+- Only include essential information
+- No unnecessary elaboration
+- If information is missing, briefly say so
+
+Answer:"""
+
+            # Get response from specialist LLM
+            response = self.response_llm.invoke([{"role": "user", "content": response_prompt}])
+            return response.content
+
+        except Exception as e:
+            print(f"Error generating response: {str(e)}")
+            return "I'm sorry, I encountered an error while processing your request. Please try again."
+
+def main():
+    assistant = LegionMariaAssistant()
+    
+    # Initial greeting message
+    initial_greeting = [
+        [None, "👋 Hello! I'm your Legion Maria Youth Affairs assistant. I can help you with information about our mission, leadership, projects, and activities. What would you like to know?"]
+    ]
+    
+    # Create mobile-optimized Gradio chat interface
+    with gr.Blocks(title="Legion Maria Chat", theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# 💬 Legion Maria YA")
+        
+        # Mobile-optimized chat interface
+        chatbot = gr.Chatbot(
+            value=initial_greeting,
+            height=400,  # Reduced for mobile
+            show_label=False,
+            container=True,
+            bubble_full_width=True,  # Better for mobile
+            show_share_button=False
+        )
+        
+        # Mobile-friendly input layout
+        with gr.Row():
+            msg = gr.Textbox(
+                placeholder="Ask me anything...",
+                show_label=False,
+                scale=5,
+                container=False,
+                lines=1
+            )
+            send_btn = gr.Button("📤", variant="primary", scale=1, size="sm")
+        
+        clear = gr.Button("🔄 New Chat", variant="secondary", size="sm")
+            
+        # Chat functionality
+        def respond(message, history):
+            if message.strip():
+                bot_response = assistant.chat_response(message, history)
+                history.append([message, bot_response])
+            return history, ""
+
+        # Event handlers
+        msg.submit(respond, [msg, chatbot], [chatbot, msg])
+        send_btn.click(respond, [msg, chatbot], [chatbot, msg])
+        clear.click(lambda: initial_greeting, None, chatbot)
+
+    # Launch with better settings for chat app
+    demo.launch(
+        share=False,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_api=False
+    )
+
+if __name__ == "__main__":
+    main()