Update app.py

app.py

@@ -84,9 +84,9 @@ def submit_questions(answers: list[Dict[str, Any]]) -> Dict[str, Any]:
         print(f"Response Body: {e.response.text if e.response else 'No response'}")
         raise
 
-# Wrap API functions in ADK Tools
-responses_api = FunctionTool(func=answer_questions, description="Fetches all questions from the remote server.")
-submit_api = FunctionTool(func=submit_questions, description="Submits the final list of answers to the remote server for scoring.")
+# Wrap API functions in ADK Tools - The description is taken from the function's docstring.
+responses_api = FunctionTool(func=answer_questions)
+submit_api = FunctionTool(func=submit_questions)
 
 # --- Agent Definitions ---
 
@@ -94,14 +94,14 @@ APP_NAME = "gaia_challenge_agent"
 USER_ID = "test_user"
 SESSION_ID = "main_session"
 
-# A specialized agent for tasks requiring code execution or data analysis
+# A specialized agent for tasks requiring code generation
 code_agent = LlmAgent(
     name='CodeAgent',
-    model="gemini-1.5-pro-latest", # Using Pro for complex code generation
-    description="Executes code and analyzes data files (.csv, .xlsx, .json, .py) to answer a question. Responds with only the final, exact answer.",
+    model="gemini-2.0-flash-lite", # Using Pro for complex code generation
+    description="Generates Python code to analyze data files (.csv, .xlsx, .json, .py) to answer a question. Responds with only the final, exact answer.",
     instruction=(
-        "You are an expert in data analysis and code execution. Given a question and a file URL, "
-        "write Python code to find the answer. "
+        "You are an expert in data analysis and code generation. Given a question and a file URL, "
+        "write Python code to find the answer. You cannot execute the code. "
         "Use pandas for data files. Fetch remote files using requests. "
         "Your final output must be only the answer to the question, with no extra text or explanation."
     ),
@@ -111,7 +111,7 @@ code_agent = LlmAgent(
 # A specialized agent for web searches
 search_agent = LlmAgent(
     name='SearchAgent',
-    model="gemini-1.5-flash-latest", # Flash is efficient for search-and-answer
+    model="gemini-2.0-flash-lite", # Flash is efficient for search-and-answer
     description="Searches the web to answer questions about current events, facts, or general knowledge. Responds with only the final, exact answer.",
     instruction=(
         "You are an expert web researcher. You will be given a question. "
@@ -125,7 +125,7 @@ search_agent = LlmAgent(
 # A specialized agent for image analysis
 image_agent = LlmAgent(
     name='ImageAgent',
-    model="gemini-1.5-flash-latest", # Flash model has vision capabilities
+    model="gemini-2.0-flash-lite", # Flash model has vision capabilities
     description="Analyzes an image to answer a question about its content. Responds with only the final, exact answer.",
     instruction=(
         "You are an expert image analyst. You will be given a question and a URL to an image. "
@@ -137,7 +137,7 @@ image_agent = LlmAgent(
 # A specialized agent for video analysis
 youtube_agent = LlmAgent(
     name='YouTubeAgent',
-    model="gemini-1.5-flash-latest", # Flash model has vision capabilities
+    model="gemini-2.0-flash-lite", # Flash model has vision capabilities
     description="Watches a YouTube video to answer a question about its content. Responds with only the final, exact answer.",
     instruction=(
         "You are an expert video analyst. You will be given a question and a URL to a YouTube video. "
@@ -149,7 +149,7 @@ youtube_agent = LlmAgent(
 # The main orchestrator agent
 root_agent = LlmAgent(
     name='OrchestratorAgent',
-    model="gemini-1.5-pro-latest", # Pro for robust orchestration
+    model="gemini-2.0-flash-lite", # Pro for robust orchestration
     description="Manages a team of specialized agents to answer a list of questions and submits them for scoring.",
     instruction=(
         "You are the project manager. Your goal is to answer a series of questions and submit them. "
@@ -180,7 +180,6 @@ async def run_agent_process():
     session = await session_service.create_session(
         app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID
     )
-    # Corrected the print statement below
     print(f"===== Agent Process Started for session: {session.session_id} =====")
     
     initial_prompt = "Get all the questions, answer each one using your specialized agents, and submit the final list of answers for scoring."