adding the graph for number of agents

app.py

@@ -42,24 +42,45 @@ def get_vanity_transactions(date):
 def fetch_and_aggregate_transactions():
     total_services = service_registry.functions.totalSupply().call()
     aggregated_transactions = []
+    weekly_agent_counts = {}
 
     for service_id in range(1, total_services + 1):
         service = service_registry.functions.getService(service_id).call()
         
         # Extract the list of agent IDs from the service data
         agent_ids = service[-1]  # Assuming the last element is the list of agent IDs
-        
+
         # Check if 25 is in the list of agent IDs
         if 25 in agent_ids:
             agent_address = service_registry.functions.getAgentInstances(service_id).call()[1][0]
             response_transfers = get_transfers("valory", agent_address)
             aggregated_transactions.extend(response_transfers["transfers"])
+        
+        # Track the weekly number of agents
+        creation_event = service_registry.events.CreateService.create_filter(
+            from_block=0, argument_filters={'serviceId': service_id, 'configHash': service[2]}
+        ).get_all_entries()
+        
+        if creation_event:
+            block_number = creation_event[0]['blockNumber']
+            block = web3.eth.get_block(block_number)
+            creation_timestamp = datetime.fromtimestamp(block['timestamp'])
+            week_str = creation_timestamp.strftime('%Y-%W')
+            
+            if week_str not in weekly_agent_counts:
+                weekly_agent_counts[week_str] = set()
+            
+            for agent_id in agent_ids:
+                weekly_agent_counts[week_str].add(agent_id)
+    
+    # Convert set to count
+    weekly_agent_counts = {week: len(agents) for week, agents in weekly_agent_counts.items()}
     
-    return aggregated_transactions
+    return aggregated_transactions, weekly_agent_counts
 
 # Function to parse the transaction data and prepare it for visualization
-def process_transactions(data):
-    transactions = data
+def process_transactions_and_agents(data):
+    transactions, weekly_agent_counts = data
 
     # Convert the data into a pandas DataFrame for easy manipulation
     rows = []
@@ -93,13 +114,14 @@ def process_transactions(data):
             "week": sending_timestamp.strftime('%Y-%W')  # Group by week
         })
     
-    df = pd.DataFrame(rows)
-    return df
+    df_transactions = pd.DataFrame(rows)
+    df_agents = pd.DataFrame(list(weekly_agent_counts.items()), columns=['week', 'agent_count'])
+    return df_transactions, df_agents
 
 # Function to create visualizations based on the metrics
 def create_visualizations():
     transactions_data = fetch_and_aggregate_transactions()
-    df = process_transactions(transactions_data)
+    df_transactions, df_agents = process_transactions_and_agents(transactions_data)
 
     # Map chain IDs to chain names
     chain_name_map = {
@@ -107,25 +129,24 @@ def create_visualizations():
         8453: "Base",
         1: "Ethereum"
     }
-    df["sending_chain"] = df["sending_chain"].map(chain_name_map)
-    df["receiving_chain"] = df["receiving_chain"].map(chain_name_map)
+    df_transactions["sending_chain"] = df_transactions["sending_chain"].map(chain_name_map)
+    df_transactions["receiving_chain"] = df_transactions["receiving_chain"].map(chain_name_map)
 
     # Ensure that chain IDs are strings for consistent grouping
-    df["sending_chain"] = df["sending_chain"].astype(str)
-    df["receiving_chain"] = df["receiving_chain"].astype(str)
-    df['date'] = pd.to_datetime(df['date'])
+    df_transactions["sending_chain"] = df_transactions["sending_chain"].astype(str)
+    df_transactions["receiving_chain"] = df_transactions["receiving_chain"].astype(str)
+    df_transactions['date'] = pd.to_datetime(df_transactions['date'])
 
     # Total transactions per chain per day
-    tx_per_chain = df.groupby(["date", "sending_chain"]).size().reset_index(name="transaction_count")
+    tx_per_chain = df_transactions.groupby(["date", "sending_chain"]).size().reset_index(name="transaction_count")
 
     # Add vanity transactions for each day
-    date_range = pd.date_range(start=df['date'].min(), end=df['date'].max())
+    date_range = pd.date_range(start=df_transactions['date'].min(), end=df_transactions['date'].max())
     vanity_transactions = pd.concat([get_vanity_transactions(date) for date in date_range])
 
     # Combine actual and vanity transactions
     tx_per_chain = pd.concat([tx_per_chain, vanity_transactions]).groupby(["date", "sending_chain"]).sum().reset_index()
-    # Total transactions per chain per day
-    #tx_per_chain = df.groupby(["date", "sending_chain"]).size().reset_index(name="transaction_count")
+    
     fig_tx_chain = px.bar(
         tx_per_chain,
         x="date",
@@ -149,11 +170,12 @@ def create_visualizations():
         height=700,
     )
     fig_tx_chain.update_xaxes(tickformat="%Y-%m-%d")
+
     # Identify swap transactions
-    df["is_swap"] = df.apply(lambda x: x["sending_token_symbol"] != x["receiving_token_symbol"], axis=1)
+    df_transactions["is_swap"] = df_transactions.apply(lambda x: x["sending_token_symbol"] != x["receiving_token_symbol"], axis=1)
 
     # Total swaps per chain per day
-    swaps_per_chain = df[df["is_swap"]].groupby(["date", "sending_chain"]).size().reset_index(name="swap_count")
+    swaps_per_chain = df_transactions[df_transactions["is_swap"]].groupby(["date", "sending_chain"]).size().reset_index(name="swap_count")
     fig_swaps_chain = px.bar(
         swaps_per_chain,
         x="date",
@@ -179,10 +201,10 @@ def create_visualizations():
     fig_swaps_chain.update_xaxes(tickformat="%Y-%m-%d")
 
     # Identify bridge transactions
-    df["is_bridge"] = df.apply(lambda x: x["sending_chain"] != x["receiving_chain"], axis=1)
+    df_transactions["is_bridge"] = df_transactions.apply(lambda x: x["sending_chain"] != x["receiving_chain"], axis=1)
 
     # Total bridges per chain per day
-    bridges_per_chain = df[df["is_bridge"]].groupby(["date", "sending_chain"]).size().reset_index(name="bridge_count")
+    bridges_per_chain = df_transactions[df_transactions["is_bridge"]].groupby(["date", "sending_chain"]).size().reset_index(name="bridge_count")
     fig_bridges_chain = px.bar(
         bridges_per_chain,
         x="date",
@@ -206,8 +228,9 @@ def create_visualizations():
         height=700,
     )
     fig_bridges_chain.update_xaxes(tickformat="%Y-%m-%d")
+
     # Investment per agent per day
-    investment_per_agent = df.groupby(["date", "from_address", "sending_chain"])["sending_amount_usd"].sum().reset_index()
+    investment_per_agent = df_transactions.groupby(["date", "from_address", "sending_chain"])["sending_amount_usd"].sum().reset_index()
     fig_investment_agent = px.bar(
         investment_per_agent,
         x="date",
@@ -236,7 +259,25 @@ def create_visualizations():
         height=700,
     )
     fig_investment_agent.update_xaxes(tickformat="%Y-%m-%d")
-    return fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent
+
+    # Number of agents per week
+    fig_agents_weekly = px.bar(
+        df_agents,
+        x="week",
+        y="agent_count",
+        title="Number of Agents Week Over Week",
+        labels={"week": "Week", "agent_count": "Number of Agents"},
+        color_discrete_sequence=["purple"]
+    )
+    fig_agents_weekly.update_layout(
+        xaxis_title=None,
+        yaxis=dict(tickmode='linear', tick0=0, dtick=1),
+        xaxis=dict(tickmode='array', tickvals=df_agents['week'], tickangle=45),
+        bargap=0.8,
+        height=700,
+    )
+
+    return fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent, fig_agents_weekly
 
 # Gradio interface
 def dashboard():
@@ -245,20 +286,24 @@ def dashboard():
 
         # Fetch and display visualizations
         with gr.Tab("Transactions"):
-            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent = create_visualizations()
+            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent, fig_agents_weekly = create_visualizations()
             gr.Plot(fig_tx_chain)
         
         with gr.Tab("Swaps"):
-            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent = create_visualizations()
+            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent, fig_agents_weekly = create_visualizations()
             gr.Plot(fig_swaps_chain)
         
         with gr.Tab("Bridges"):
-            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent = create_visualizations()
+            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent, fig_agents_weekly = create_visualizations()
             gr.Plot(fig_bridges_chain)
 
         with gr.Tab("Investment"):
-            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent = create_visualizations()
+            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent, fig_agents_weekly = create_visualizations()
             gr.Plot(fig_investment_agent)
+        
+        with gr.Tab("Agents Week Over Week"):
+            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent, fig_agents_weekly = create_visualizations()
+            gr.Plot(fig_agents_weekly)
 
     return demo