Update app.py

app.py

@@ -3,42 +3,74 @@ import requests
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# Initialize the LLM
 client = InferenceClient("meta-llama/Llama-3.3-70B-Instruct", token=os.getenv("HF_TOKEN"))
 
 def search_books(query):
-    """Simple Web Search for books."""
     try:
         url = f"https://openlibrary.org/search.json?q={query}&limit=3"
         data = requests.get(url, timeout=5).json().get("docs", [])
-        if not data: return "No books found."
+        if not data: return "No catalog records found."
         return "\n".join([f"{d.get('title')} by {d.get('author_name', ['Unknown'])[0]}" for d in data])
     except:
-        return "Search error."
+        return "Library catalog is currently unreachable."
 
-def chat_fn(message, history):
-    # 1. Get live data
+def librarian_bot(message, history, session_state):
+    """
+    session_state tracks: 
+    - 'verified': bool
+    - 'age': int
+    """
+    # 1. Initialize State
+    if session_state is None:
+        session_state = {"verified": False, "age": None}
+
+    # 2. ETHICAL GATE: Check Age
+    if not session_state["verified"]:
+        if message.isdigit():
+            age = int(message)
+            if age < 13:
+                reply = "I'm sorry, but I can only assist users aged 13 and older for safety reasons. Please ask a parent for help."
+                return reply, session_state
+            else:
+                session_state["age"] = age
+                session_state["verified"] = True
+                return f"Age {age} verified. Welcome! How can I help you in the library today?", session_state
+        else:
+            return "Hello! I am your AI Librarian. To comply with safety guidelines, please enter your age as a number to continue.", session_state
+
+    # 3. CORE LOGIC: RAG + LLM (Only reached if verified)
     context = search_books(message)
     
-    # 2. Build the API prompt
+    # Adjusting prompt based on age (Dynamic Ethics)
+    tone = "simple and educational" if session_state["age"] < 18 else "professional and academic"
+    
     messages = [
-        {"role": "system", "content": "You are a librarian. Use the context to suggest books."},
-        {"role": "user", "content": f"Context: {context}\nUser Query: {message}"}
+        {"role": "system", "content": f"You are a librarian for a {tone} audience. Use the context to answer."},
+        {"role": "user", "content": f"Context: {context}\nQuestion: {message}"}
     ]
-    
-    # 3. Get LLM response
+
     try:
-        response = client.chat_completion(messages=messages, max_tokens=200)
-        return response.choices[0].message.content
+        response = client.chat_completion(messages=messages, max_tokens=1000)
+        return response.choices[0].message.content, session_state
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"Error connecting to the stacks: {str(e)}", session_state
+
+# --- UI with State Management ---
+with gr.Blocks() as demo:
+    gr.Markdown("# 📚 Ethical AI Librarian")
+    
+    # Session state persists for this user's tab
+    state = gr.State(None)
+    
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Enter age to start, then ask about books...")
+    
+    # We use a wrapper to handle the state update
+    def user_interact(user_input, chat_history, current_state):
+        bot_reply, new_state = librarian_bot(user_input, chat_history, current_state)
+        chat_history.append((user_input, bot_reply))
+        return "", chat_history, new_state
 
-# The most basic Gradio Interface - No custom state, no complex types.
-demo = gr.ChatInterface(
-    fn=chat_fn,
-    title="AI Librarian (Minimalist Build)",
-    description="Ask me about any book. I'll search the web and answer."
-)
+    msg.submit(user_interact, [msg, chatbot, state], [msg, chatbot, state])
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()