Update app.py

app.py

@@ -7,8 +7,8 @@ from sentence_transformers import SentenceTransformer
 from huggingface_hub import InferenceClient
 
 # 1. INITIALIZE MODELS
-# Embedder (CPU) and Llama 3.3 (API)
 embedder = SentenceTransformer('all-MiniLM-L6-v2')
+# Ensure you have HF_TOKEN in your Space Secrets!
 client = InferenceClient("meta-llama/Llama-3.3-70B-Instruct", token=os.getenv("HF_TOKEN"))
 
 def fetch_and_index(query):
@@ -20,13 +20,11 @@ def fetch_and_index(query):
         
         if not data: return None, None
 
-        # Extract strictly Title and Author
         catalog = [
             f"{d.get('title')} by {', '.join(d.get('author_name', ['Unknown Author']))}" 
             for d in data
         ]
         
-        # Build Vector Index
         embeddings = embedder.encode(catalog)
         index = faiss.IndexFlatL2(embeddings.shape[1])
         index.add(np.array(embeddings).astype('float32'))
@@ -36,7 +34,6 @@ def fetch_and_index(query):
         return None, None
 
 def librarian_logic(message, history, user_state):
-    # Initialize State if empty
     if user_state is None:
         user_state = {"step": "ASK_AGE", "age": None, "location": None}
 
@@ -45,32 +42,28 @@ def librarian_logic(message, history, user_state):
         if message.isdigit():
             user_state["age"] = int(message)
             user_state["step"] = "ASK_LOCATION"
-            reply = "Got it. For regional safety compliance, what is your general location (City/Country)?"
+            reply = "Got it. For safety compliance, what is your general location (City/Country)?"
             return history + [[message, reply]], user_state, ""
-        
-        reply = "Welcome! I am your AI Librarian. To begin safely, how old are you?"
-        return history + [[message, reply]], user_state, ""
+        return history + [[message, "How old are you?"]], user_state, ""
 
     if user_state["step"] == "ASK_LOCATION":
         user_state["location"] = message
         user_state["step"] = "SEARCH_READY"
-        reply = f"Verification complete for {user_state['location']}. How can I help you find a book today?"
+        reply = f"System verified for {user_state['location']}. What book can I find for you?"
         return history + [[message, reply]], user_state, ""
 
-    # --- PHASE 2: SEARCH & LLM ACTION ---
+    # --- PHASE 2: SEARCH & LLM ---
     index, catalog = fetch_and_index(message)
-    context = "\n- ".join(catalog[:3]) if catalog else "No books found in live search."
+    context = "\n- ".join(catalog[:3]) if catalog else "No books found."
 
-    # Constructing the DICT for the model (The fix for your error)
-    safety_context = "User is under 13; ensure recommendations are child-safe." if user_state["age"] < 13 else ""
-    
+    # THE FIX: This specific list-of-dicts format
     messages = [
-        {"role": "system", "content": "You are a professional librarian. Use the context provided to recommend titles and authors."},
-        {"role": "user", "content": f"Context: {context}\nSafety: {safety_context}\nQuery: {message}"}
+        {"role": "system", "content": "You are a professional librarian. Respond only with book titles and authors based on the provided context."},
+        {"role": "user", "content": f"User Age: {user_state['age']}. Context: {context}. Query: {message}"}
     ]
 
     response = ""
-    # Important: Named argument 'messages' is used here
+    # Explicitly use messages=messages
     for msg in client.chat_completion(
         messages=messages, 
         max_tokens=300, 
@@ -81,15 +74,14 @@ def librarian_logic(message, history, user_state):
             response += token
             yield history + [[message, response]], user_state, ""
 
-# --- GRADIO UI ---
+# --- UI SETUP ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 📚 AI Librarian Agent\n*Live Web Search + Semantic FAISS Ranking*")
+    gr.Markdown("# 📚 AI Librarian Agent\n*Search for books by title or author.*")
     
     user_state = gr.State()
     chatbot = gr.Chatbot()
-    msg = gr.Textbox(label="Your Input", placeholder="Enter age first...")
+    msg = gr.Textbox(label="Your Input", placeholder="Type age first...")
     
-    # Logic link
     msg.submit(librarian_logic, [msg, chatbot, user_state], [chatbot, user_state, msg])
 
 if __name__ == "__main__":