Update app.py

app.py

@@ -102,16 +102,27 @@ def retrieve_chunks(question, index, embed_model, text_chunks, k=3):
     return [text_chunks[i] for i in I[0]]
 
 # ---------------- Groq Answer Generator ----------------
-def generate_answer_with_groq(question, context):
+def generate_answer_with_groq(question, context, query_type="general"):
     url = "https://api.groq.com/openai/v1/chat/completions"
     api_key = os.getenv("GROQ_API_KEY")
     headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
-    prompt = f"Customer asked: '{question}'\n\nHere is the relevant information to help:\n{context}"
+
+    system_prompt = (
+        "You are ToyBot, a friendly WhatsApp assistant for ToyShop. "
+        "You help customers with order status, FAQs, and product return policies. "
+        "Be polite, clear, and concise."
+    )
+
+    if query_type == "faq":
+        user_prompt = f"Customer asked FAQ or product policy question:\n'{question}'\n\nRelevant info:\n{context}"
+    else:
+        user_prompt = f"Customer asked:\n'{question}'\n\nRelevant info:\n{context}"
+
     payload = {
         "model": "llama3-8b-8192",
         "messages": [
-            {"role": "system", "content": "You are ToyBot, a friendly WhatsApp assistant..."},
-            {"role": "user", "content": prompt},
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
         ],
         "temperature": 0.5,
         "max_tokens": 300,
@@ -167,10 +178,11 @@ def load_orders():
         return {}
 
 def extract_order_id(text):
-    pattern = r"\bORD\d{3,}\b"
+    pattern = r"(order_id\s+\d+)"
     match = re.search(pattern, text, re.IGNORECASE)
     if match:
-        return match.group(0).upper()
+        return match.group(1).lower()
+    return Nonetch.group(0).upper()
     return None
 
 def format_order_response(order_id, order_data):
@@ -216,9 +228,8 @@ def process_messages_loop():
     embeddings = embed_model.encode(text_chunks)
     index = faiss.IndexFlatL2(embeddings.shape[1])
     index.add(embeddings)
-    
-    orders = load_orders()  # Load orders once at start
-    
+
+    orders = load_orders()
     seen_sids = set()
 
     while True:
@@ -230,14 +241,23 @@ def process_messages_loop():
         message = fetch_latest_incoming_message(twilio_client, conversation_sid)
         if message and message["sid"] not in seen_sids:
             seen_sids.add(message["sid"])
-            question = message["body"]
+            question = message["body"].strip()
 
+            # Check for order ID in question
             order_id = extract_order_id(question)
             if order_id and order_id in orders:
                 answer = format_order_response(order_id, orders[order_id])
             else:
+                # Retrieve relevant KB chunks
                 chunks = retrieve_chunks(question, index, embed_model, text_chunks)
-                answer = generate_answer_with_groq(question, "\n\n".join(chunks))
+                context = "\n\n".join(chunks).strip()
+
+                if context:
+                    # Treat as FAQ or policy query
+                    answer = generate_answer_with_groq(question, context, query_type="faq")
+                else:
+                    # Fallback: general query without context
+                    answer = generate_answer_with_groq(question, "", query_type="general")
 
             send_twilio_message(twilio_client, conversation_sid, answer)