Create app.py

app.py

@@ -0,0 +1,106 @@
+import os
+import requests
+import numpy as np
+import faiss
+import gradio as gr
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import InferenceClient
+
+# 1. INITIALIZE MODELS
+# Embedder runs on CPU; Llama 3.3 runs via the API
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+client = InferenceClient("meta-llama/Llama-3.3-70B-Instruct", token=os.getenv("HF_TOKEN"))
+
+def fetch_and_index(query):
+    """Fetches live book data and indexes it in FAISS."""
+    try:
+        url = f"https://openlibrary.org/search.json?q={query}&limit=8"
+        data = requests.get(url, timeout=5).json().get("docs", [])
+        
+        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'))
+        
+        return index, catalog
+    except Exception:
+        return None, None
+
+def librarian_logic(message, history, user_state):
+    # Initialize State
+    if user_state is None:
+        user_state = {"step": "ASK_AGE", "age": None, "location": None}
+
+    # --- PHASE 1: SAFETY GATE ---
+    if user_state["step"] == "ASK_AGE":
+        if message.isdigit():
+            user_state["age"] = int(message)
+            user_state["step"] = "ASK_LOCATION"
+            reply = "Understood. For regional safety compliance, what is your general location (City/Country)?"
+            history.append((message, reply))
+            return history, user_state, ""
+        
+        reply = "Welcome to the AI Library! Before we start, for safety and compliance: How old are you?"
+        history.append((message, reply))
+        return history, user_state, ""
+
+    if user_state["step"] == "ASK_LOCATION":
+        user_state["location"] = message
+        user_state["step"] = "SEARCH_READY"
+        reply = f"System verified for {user_state['location']}. I am now your active Librarian. What books are you looking for?"
+        history.append((message, reply))
+        return history, user_state, ""
+
+    # --- PHASE 2: SEARCH ACTION ---
+    index, catalog = fetch_and_index(message)
+    
+    if not index:
+        reply = "I couldn't find any live records for that. Try another title or author?"
+        history.append((message, reply))
+        return history, user_state, ""
+
+    # Semantic Retrieval
+    query_vec = embedder.encode([message])
+    _, I = index.search(np.array(query_vec).astype('float32'), k=min(3, len(catalog)))
+    results = [catalog[i] for i in I[0]]
+
+    # Agent Synthesis
+    safety_rule = "The user is a child. Strictly recommend age-appropriate titles." if user_state["age"] < 13 else ""
+    prompt = (
+        f"Context: {results}\n"
+        f"User (Age {user_state['age']}, Loc {user_state['location']}) asks: {message}\n"
+        f"{safety_rule}\n"
+        "Present the Title and Author of these matches clearly and briefly."
+    )
+
+    response = client.chat_completion(
+        [{"role": "system", "content": "You are a professional librarian agent."}, 
+         {"role": "user", "content": prompt}], 
+        max_tokens=250
+    ).choices[0].message.content
+
+    history.append((message, response))
+    return history, user_state, ""
+
+# --- UI SETUP ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 📚 AI Librarian Agent\n*Live Web Search + Semantic FAISS Ranking*")
+    
+    user_state = gr.State()
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Your Message", placeholder="Type age first, then chat...")
+    clear = gr.Button("Reset Session")
+
+    msg.submit(librarian_logic, [msg, chatbot, user_state], [chatbot, user_state, msg])
+    clear.click(lambda: (None, None, ""), None, [chatbot, user_state, msg])
+
+if __name__ == "__main__":
+    demo.launch()