Update app.py

app.py

@@ -162,6 +162,46 @@ def get_batting_avg_column(df):
         if col in df.columns:
             return col
     return None
+# Sidebar Query Agent (LLM-based Stats Assistant)
+with st.sidebar:
+    st.markdown("---")
+    st.markdown("### 🤖 Ask CricketStatBot")
+    
+    show_input = st.button("Start Query")
+
+    if show_input:
+        user_query = st.text_input("Ask a question about batting or bowling stats:", key="agent_query")
+        
+        if user_query:
+            # Combine all dataframes into a context string
+            def df_to_text(df, name, max_rows=100):
+                return f"{name} Data:\n" + df.head(max_rows).to_csv(index=False)
+
+            context = (
+                df_to_text(batting_df, "Batting") + "\n" +
+                df_to_text(bowling_df, "Bowling") + "\n" +
+                df_to_text(odi_df, "ODI") + "\n" +
+                df_to_text(t20_df, "T20") + "\n" +
+                df_to_text(test_df, "Test") + "\n" +
+                df_to_text(odi_teams_df, "ODI Teams") + "\n" +
+                df_to_text(t20_teams_df, "T20 Teams") + "\n" +
+                df_to_text(test_teams_df, "Test Teams")
+            )
+
+            # Agent Prompt
+            agent_prompt = ChatPromptTemplate.from_messages([
+                ("system", 
+                 "You are a cricket analytics assistant. Use the below data to answer cricket-related questions in a detailed and insightful manner:\n\n{context}"),
+                ("human", "{question}")
+            ])
+            agent_chain = agent_prompt | model | out_par
+
+            with st.spinner("Analyzing your question..."):
+                agent_response = agent_chain.invoke({"context": context, "question": user_query})
+
+            st.markdown("#### 🧠 CricketStatBot Answer")
+            st.markdown(f"<div class='card'>{agent_response}</div>", unsafe_allow_html=True)
+
 
 # ---- Main Content ----
 if option == "Team Info" and team_info: