Update app.py

app.py

@@ -102,27 +102,16 @@ 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, query_type="general"):
+def generate_answer_with_groq(question, context):
     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"}
-
-    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}"
-
+    prompt = f"Customer asked: '{question}'\n\nHere is the relevant information to help:\n{context}"
     payload = {
         "model": "llama3-8b-8192",
         "messages": [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_prompt},
+            {"role": "system", "content": "You are ToyBot, a friendly WhatsApp assistant specialized in toy shop customer service."},
+            {"role": "user", "content": prompt},
         ],
         "temperature": 0.5,
         "max_tokens": 300,
@@ -178,11 +167,11 @@ def load_orders():
         return {}
 
 def extract_order_id(text):
-    pattern = r"(order_id\s+\d+)"
+    # Extract pattern: "order_id <digits>"
+    pattern = r"order_id\s+(\d+)"
     match = re.search(pattern, text, re.IGNORECASE)
     if match:
-        return match.group(1).lower()
-    return Nonetch.group(0).upper()
+        return match.group(1)  # just digits as string
     return None
 
 def format_order_response(order_id, order_data):
@@ -228,8 +217,9 @@ def process_messages_loop():
     embeddings = embed_model.encode(text_chunks)
     index = faiss.IndexFlatL2(embeddings.shape[1])
     index.add(embeddings)
-
-    orders = load_orders()
+    
+    orders = load_orders()  # Load orders once at start
+    
     seen_sids = set()
 
     while True:
@@ -241,23 +231,14 @@ 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"].strip()
+            question = message["body"]
 
-            # 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)
-                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")
+                answer = generate_answer_with_groq(question, "\n\n".join(chunks))
 
             send_twilio_message(twilio_client, conversation_sid, answer)