changes made

chatbot.py

@@ -4,16 +4,16 @@ from langchain_community.utilities import SQLDatabase
 from langchain.chat_models import init_chat_model
 from langchain_community.agent_toolkits import create_sql_agent
 from langchain.callbacks.base import BaseCallbackHandler
+import time
 
 # Database connection string
 url = "postgresql://postgres.qxvpaoeakhddzabctekw:8&CiDRpTFbRRBrT@aws-0-ap-south-1.pooler.supabase.com:5432/postgres"
 
-# ✅ Initialize session state variables if they don't exist
+# Initialize session state variables if they don't exist
 if "db" not in st.session_state:
-    st.session_state.db = SQLDatabase.from_uri(url)
-
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []  # Stores past queries and responses
+    st.session_state.db = SQLDatabase.from_uri(
+        "postgresql://postgres.qxvpaoeakhddzabctekw:8&CiDRpTFbRRBrT@aws-0-ap-south-1.pooler.supabase.com:5432/postgres"
+    )
 
 if "agent_chain_output" not in st.session_state:
     st.session_state.agent_chain_output = ""
@@ -83,18 +83,6 @@ def main():
                 schema = st.session_state.db.get_table_info()
                 st.code(schema)
     
-    # Display Chat History
-    st.sidebar.subheader("📜 Chat History")
-    for entry in st.session_state.chat_history:
-        with st.sidebar.expander(f"Query: {entry['query']}"):
-            st.write(f"**Agent Response:** {entry['response']}")
-            if show_thinking and "thinking" in entry:
-                st.write(f"**Agent Thinking:**\n{entry['thinking']}")
-
-    if st.sidebar.button("🗑 Clear Chat History"):
-        st.session_state.chat_history = []
-        st.experimental_rerun()
-
     # Query input section
     query_options = ["Free-form query", "Get employee details"]
     query_type = st.radio("Query Type:", query_options)
@@ -107,7 +95,15 @@ def main():
         query = f"Give details of employee ID {employee_id}" if employee_id else ""
     
     # Process button
-    process_button = st.button("Run Query")
+    col1, col2 = st.columns([1, 5])
+    with col1:
+        process_button = st.button("Run Query", type="primary", use_container_width=True)
+    with col2:
+        if process_button:
+            clear_button = st.button("Clear Results", use_container_width=True)
+            if clear_button:
+                st.session_state.agent_chain_output = ""
+                st.experimental_rerun()
     
     # Results section
     st.header("Results")
@@ -130,6 +126,7 @@ def main():
         
         # Initialize LLM
         try:
+            # Define a fixed API key (hardcoded for simplicity)
             api_key = "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"
             
             # Create a new LLM instance
@@ -154,13 +151,6 @@ def main():
             with st.spinner("Processing your query..."):
                 result = agent_executor.invoke(query)
                 
-                # Store chat history
-                st.session_state.chat_history.append({
-                    "query": query,
-                    "response": result["output"],
-                    "thinking": stream_handler.text if show_thinking else ""
-                })
-                
                 # Display the result
                 result_container.success("Query processed successfully!")
                 result_output.markdown("### Answer")