Upload analytics_pipeline.py

agents/analytics_pipeline.py

@@ -1,7 +1,7 @@
+
 from google.adk.agents import LlmAgent
 from tools import csv_parser, plot_generator, forecaster
 
-# Define the agents using raw function references as tools
 trend_detector_agent = LlmAgent(
     name="trend_detector_agent",
     model="gemini-2.5-pro-exp-03-25",
@@ -10,10 +10,7 @@ trend_detector_agent = LlmAgent(
         Analyze the input table. Identify major trends, seasonal patterns,
         and anomalies (spikes or drops). Return a concise summary.
     """,
-    tools=[
-        csv_parser.parse_csv_tool,
-        plot_generator.plot_sales_tool
-    ]
+    tools=[csv_parser.parse_csv_tool, plot_generator.plot_sales_tool]
 )
 
 forecast_agent = LlmAgent(
@@ -24,9 +21,7 @@ forecast_agent = LlmAgent(
         Forecast next 3 months of sales based on historical patterns.
         Use the forecast tool to generate a visual chart.
     """,
-    tools=[
-        forecaster.forecast_tool
-    ]
+    tools=[forecaster.forecast_tool]
 )
 
 strategy_agent = LlmAgent(
@@ -39,7 +34,6 @@ strategy_agent = LlmAgent(
     """
 )
 
-# Parent coordinator that orchestrates all sub-agents
 analytics_coordinator = LlmAgent(
     name="analytics_coordinator",
     model="gemini-2.5-pro-exp-03-25",
@@ -51,9 +45,5 @@ analytics_coordinator = LlmAgent(
         3. Recommend business strategies using strategy_agent
         Return a full dashboard-style summary.
     """,
-    sub_agents=[
-        trend_detector_agent,
-        forecast_agent,
-        strategy_agent
-    ]
+    sub_agents=[trend_detector_agent, forecast_agent, strategy_agent]
 )