Add pagination to treasury app

prediction_market_agent/agents/microchain_agent/nft_treasury_game/app_nft_treasury_game.py

@@ -37,7 +37,10 @@
     ReceiveMessage,
     SendPaidMessageToAnotherAgent,
 )
-from prediction_market_agent.db.agent_communication import fetch_unseen_transactions
+from prediction_market_agent.db.agent_communication import (
+    fetch_count_unprocessed_transactions,
+    fetch_unseen_transactions,
+)
 from prediction_market_agent.db.long_term_memory_table_handler import (
     LongTermMemories,
     LongTermMemoryTableHandler,
@@ -178,12 +181,44 @@ def customized_chat_message(
             st.markdown(parsed_function_output_body)
 
 
-@st.fragment(run_every=timedelta(seconds=5))
 def show_function_calls_part(nft_agent: type[DeployableAgentNFTGameAbstract]) -> None:
     st.markdown(f"""### Agent's actions""")
 
+    n_total_messages = long_term_memory_table_handler(nft_agent.identifier).count()
+    messages_per_page = 50
+    if "page_number" not in st.session_state:
+        st.session_state.page_number = 0
+
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        if st.button("Previous page", disabled=st.session_state.page_number == 0):
+            st.session_state.page_number -= 1
+    with col2:
+        if st.button(
+            "Next page",
+            disabled=st.session_state.page_number
+            == n_total_messages // messages_per_page,
+        ):
+            st.session_state.page_number += 1
+    with col3:
+        st.write(f"Current page {st.session_state.page_number + 1}")
+
+    show_function_calls_part_messages(
+        nft_agent, messages_per_page, st.session_state.page_number
+    )
+
+
+@st.fragment(run_every=timedelta(seconds=10))
+def show_function_calls_part_messages(
+    nft_agent: type[DeployableAgentNFTGameAbstract],
+    messages_per_page: int,
+    page_number: int,
+) -> None:
     with st.spinner("Loading agent's actions..."):
-        calls = long_term_memory_table_handler(nft_agent.identifier).search()
+        calls = long_term_memory_table_handler(nft_agent.identifier).search(
+            offset=page_number * messages_per_page,
+            limit=messages_per_page,
+        )
 
     if not calls:
         st.markdown("No actions yet.")
@@ -205,7 +240,7 @@ def show_function_calls_part(nft_agent: type[DeployableAgentNFTGameAbstract]) ->
             customized_chat_message(function_call, function_output)
 
 
-@st.fragment(run_every=timedelta(seconds=5))
+@st.fragment(run_every=timedelta(seconds=10))
 def show_about_agent_part(nft_agent: type[DeployableAgentNFTGameAbstract]) -> None:
     system_prompt = (
         system_prompt_from_db.prompt
@@ -240,23 +275,28 @@ def show_about_agent_part(nft_agent: type[DeployableAgentNFTGameAbstract]) -> No
     )
     st.markdown("---")
     with st.popover("Show unprocessed incoming messages"):
-        messages = fetch_unseen_transactions(nft_agent.wallet_address)
+        show_n = 10
+        n_messages = fetch_count_unprocessed_transactions(nft_agent.wallet_address)
+        messages = fetch_unseen_transactions(nft_agent.wallet_address, n=show_n)
 
         if not messages:
             st.info("No unprocessed messages")
         else:
             for message in messages:
                 st.markdown(
                     f"""
-                    **From:** {message.sender}
+                    **From:** {message.sender}  
                     **Message:** {unzip_message_else_do_nothing(message.message.hex())}  
                     **Value:** {wei_to_xdai(message.value)} xDai
                     """
                 )
                 st.divider()
 
+            if n_messages > show_n:
+                st.write(f"... and another {n_messages - show_n} unprocessed messages.")
+
 
-@st.fragment(run_every=timedelta(seconds=5))
+@st.fragment(run_every=timedelta(seconds=10))
 def show_treasury_part() -> None:
     treasury_xdai_balance = get_balances(TREASURY_SAFE_ADDRESS).xdai
     st.markdown(

prediction_market_agent/db/agent_communication.py

@@ -12,13 +12,14 @@
 
 def fetch_unseen_transactions(
     consumer_address: ChecksumAddress,
+    n: int | None = None,
 ) -> list[MessageContainer]:
     agent_comm_contract = AgentCommunicationContract()
 
     count_unseen_messages = fetch_count_unprocessed_transactions(consumer_address)
 
     message_containers = par_map(
-        items=list(range(count_unseen_messages)),
+        items=list(range(n or count_unseen_messages)),
         func=lambda idx: agent_comm_contract.get_at_index(
             agent_address=consumer_address, idx=idx
         ),

prediction_market_agent/db/long_term_memory_table_handler.py

@@ -2,6 +2,7 @@
 import typing as t
 
 from prediction_market_agent_tooling.tools.utils import DatetimeUTC, utcnow
+from sqlalchemy.sql.elements import ColumnElement
 from sqlmodel import col
 
 from prediction_market_agent.agents.identifiers import AgentIdentifier
@@ -44,25 +45,36 @@ def save_answer_with_scenario(
     ) -> None:
         return self.save_history([answer_with_scenario.model_dump()])
 
-    def search(
-        self,
-        from_: DatetimeUTC | None = None,
-        to_: DatetimeUTC | None = None,
-        limit: int | None = None,
-    ) -> list[LongTermMemories]:
-        """Searches the LongTermMemoryTableHandler for entries within a specified datetime range that match
-        self.task_description."""
+    def _get_query_filters(
+        self, from_: DatetimeUTC | None, to_: DatetimeUTC | None
+    ) -> list[ColumnElement[bool]]:
         query_filters = [
             col(LongTermMemories.task_description) == self.task_description
         ]
         if from_ is not None:
             query_filters.append(col(LongTermMemories.datetime_) >= from_)
         if to_ is not None:
             query_filters.append(col(LongTermMemories.datetime_) <= to_)
+        return query_filters
 
+    def search(
+        self,
+        from_: DatetimeUTC | None = None,
+        to_: DatetimeUTC | None = None,
+        offset: int = 0,
+        limit: int | None = None,
+    ) -> list[LongTermMemories]:
+        """Searches the LongTermMemoryTableHandler for entries within a specified datetime range that match
+        self.task_description."""
+        query_filters = self._get_query_filters(from_, to_)
         return self.sql_handler.get_with_filter_and_order(
             query_filters=query_filters,
             order_by_column_name=LongTermMemories.datetime_.key,  # type: ignore[attr-defined]
             order_desc=True,
+            offset=offset,
             limit=limit,
         )
+
+    def count(self) -> int:
+        query_filters = self._get_query_filters(None, None)
+        return self.sql_handler.count(query_filters=query_filters)

prediction_market_agent/db/sql_handler.py

@@ -34,6 +34,7 @@ def get_with_filter_and_order(
         query_filters: t.Sequence[ColumnElement[bool] | BinaryExpression[bool]] = (),
         order_by_column_name: str | None = None,
         order_desc: bool = True,
+        offset: int = 0,
         limit: int | None = None,
     ) -> list[SQLModelType]:
         with self.db_manager.get_session() as session:
@@ -47,7 +48,19 @@ def get_with_filter_and_order(
                     if order_desc
                     else asc(order_by_column_name)
                 )
+            if offset:
+                query = query.offset(offset)
             if limit:
                 query = query.limit(limit)
             results = query.all()
         return results
+
+    def count(
+        self,
+        query_filters: t.Sequence[ColumnElement[bool] | BinaryExpression[bool]] = (),
+    ) -> int:
+        with self.db_manager.get_session() as session:
+            query = session.query(self.table)
+            for exp in query_filters:
+                query = query.where(exp)
+            return query.count()