Update app.py

app.py

@@ -88,7 +88,7 @@ def load_live_alerts():
             approx_value_at_win = tickets_sold * ticket_price
 
             content = (
-                f"Raffle {num} | Prize: {['prize_name']} | Value: £{val:.2f}"
+                f"Raffle {num} | Prize: {row['prize_name']} | Value: £{val:.2f}"
                 f"Single ticket price: £{ticket_price:.2f} | Won @ {date}"
                 f"Won at tickets sold:"
                 f"qty: {tickets_sold}"
@@ -103,7 +103,7 @@ def load_live_alerts():
                 "value": val,
                 "tickets_sold": tickets_sold,
                 "percent_path": pct,
-                "ticket_price": float(row["ticket_price"]),
+                "ticket_price": ticket_price,
                 "approx_value_at_win": approx_value_at_win,
                 "timestamp": ts_iso,
                 "source": "recent and live"
@@ -113,8 +113,8 @@ def load_live_alerts():
 
 # ─── 3) Retriever ───────────────────────────────────────────────────────────────
 
-db         = build_chroma_db()
-live_docs  = load_live_alerts()
+db        = build_chroma_db()
+live_docs = load_live_alerts()
 
 def combined_docs(q: str):
     hist = db.similarity_search(q, k=8)
@@ -129,13 +129,10 @@ You are **Rafael The Raffler**, a calm friendly expert in instant-win raffle ana
 If asked “what do you do?”, give a bullet list of your strengths (raffle timing, value insights, patterns).
 Reasoning Rules:
 1. **Interpreting “When”:** Whenever the user asks “When…?”, interpret that as “At what tickets-sold count and percent did the prize win occur?”  Do *not* give calendar dates or times.
-
 --- Conversation So Far ---
 {chat_history}
-
 --- Raffle Data ---
 {context}
-
 --- Question ---
 {question}
 """
@@ -156,23 +153,57 @@ def filter_docs(inputs):
         docs = [d for d in docs if d.metadata["value"] > thr]
     return {"documents":docs, "question":q}
 
-# ─── 5) RAG + ChatGroq Chain ────────────────────────────────────────────────────
+# ─── Follow-up Question Rewriting ───────────────────────────────────────────────
+
+# This template will turn "How many big prizes...?" into
+# "In raffle 86, how many big prizes in total were won?"
+question_rewrite_template = PromptTemplate(
+    input_variables=["chat_history","question"],
+    template="""
+Rewrite the following user query to be a fully self-contained question, given the conversation so far.
+
+Conversation:
+{chat_history}
+
+Follow-up:
+{question}
+
+Rewritten standalone question:"""
+)
+
+rewrite_chain = (
+    # bundle history + raw question
+    RunnableLambda(lambda q: {
+        "chat_history": memory.load_memory_variables({})["chat_history"],
+        "question": q
+    })
+    # build the rewrite prompt
+    | RunnableLambda(lambda inp: question_rewrite_template.format(**inp))
+    # call the LLM to rewrite
+    | ChatGroq(api_key=GROQ_API_KEY, model="llama3-8b-8192")
+    | StrOutputParser()
+)
+
+# ─── 5) RAG + ChatGroq Chain (with rewrite) ────────────────────────────────────
 
 retrieval_chain = (
-    RunnableMap({
-        "documents": lambda x: combined_docs(x if isinstance(x,str) else x["question"]),
-        "question":  lambda x: x if isinstance(x,str) else x["question"]
+    # 1) Rewrite the question first
+    rewrite_chain
+    # 2) Retrieve docs against the rewritten question
+    | RunnableMap({
+        "documents": lambda rewritten_q: combined_docs(rewritten_q),
+        "question":  lambda rewritten_q: rewritten_q
     })
     | RunnableLambda(filter_docs)
+    # 3) Build final inputs and truncate history
     | RunnableLambda(lambda d: {
-    # only keep the last 4 lines of chat_history
         "chat_history": "\n".join(
-            memory.load_memory_variables({})["chat_history"]
-                .splitlines()[-4:]
+            memory.load_memory_variables({})["chat_history"].splitlines()[-4:]
         ),
         "context": "\n".join(doc.page_content for doc in d["documents"]),
         "question": d["question"]
     })
+    # 4) Format final prompt and call LLM
     | RunnableLambda(lambda inp: prompt.format(**inp))
     | ChatGroq(api_key=GROQ_API_KEY, model="llama3-8b-8192")
     | StrOutputParser()