app.py

from zoneinfo import ZoneInfo
from google.adk.agents import Agent,BaseAgent,LlmAgent
from google.adk.tools import google_search
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
import google.genai.types as types
import requests
from google.adk.events import Event, EventActions
from google.adk.agents.invocation_context import InvocationContext
from typing import AsyncGenerator, Dict, Any
from google.genai import types as genai_types 
from google.adk.tools import ToolContext, FunctionTool
import logging
from google.adk.tools import built_in_code_execution
from google.adk.tools import agent_tool
import asyncio

logging.basicConfig(level=logging.ERROR)

url = 'https://agents-course-unit4-scoring.hf.space/questions'
headers = {'accept': 'application/json'}
response = requests.get(url, headers=headers)

def answer_questions():
    """Fetches questions from the scoring API."""
    url = 'https://agents-course-unit4-scoring.hf.space/questions'
    headers = {'accept': 'application/json'}
    response = requests.get(url, headers=headers)
    response.raise_for_status()  # Raise an exception for bad status codes
    prompts = []
    for i in response.json():
        task_id = i['task_id']
        if i['file_name']:
            url_file = f"https://agents-course-unit4-scoring.hf.space/files/{i['task_id']}"
            question = i['question']
            prompt = f"{task_id}:{question} and the file is {url_file}, give the final answer only"
        else:
            question = i['question']
            prompt = f"{task_id}:{question} give the final answer only"
        prompts.append(prompt)
    return prompts

def submit_questions(answers: list[Dict[str, Any]]) -> Dict[str, Any]:
    """Submits the collected answers to the scoring API."""
    url = 'https://agents-course-unit4-scoring.hf.space/submit'
    payload = {
        "username": "ashishja",
        "agent_code": "https://huggingface.co/spaces/ashishja/Agents_Course_Final_Assignment_Ashish",
        "answers": answers
    }
    headers = {'accept': 'application/json', "Content-Type": "application/json"}
    response = requests.post(url, headers=headers, json=payload)
    import json
    print("Submitting the following payload:")
    print(json.dumps(payload, indent=2))
    if response.status_code == 200:
        print("Submission successful!")
        return response.json()
    else:
        print(f"Submission failed with status {response.status_code}: {response.text}")
        response.raise_for_status()

responses_api = FunctionTool(func=answer_questions)
submit_api = FunctionTool(func=submit_questions)

# class QuestionAnswerer(LlmAgent):
#     async def _run_async_impl(self, ctx: InvocationContext) -> AsyncGenerator[Event, None]:
#         questions_to_answer = ctx.session_service.get('fetched_questions', [])
#         for q in questions_to_answer:
#             answer = await self._llm(messages=[types.ChatMessage(role="user", parts=[types.Part(text=q)])])
#             yield Event(author=self.name, content=answer.content)
#
# qa = QuestionAnswerer(name = 'qa_1', model="gemini-1.5-flash-latest", description="Question Answerer")

APP_NAME="final_assignment_agent"
USER_ID="user1234"
SESSION_ID="5678"

code_agent = LlmAgent(
    name='codegaiaAgent',
    model="gemini-2.0-flash",
    description=(
        "You are a smart agent that can write and execute code to answer questions. Use this for questions involving code files (.py) or data files (.csv, .xlsx, .json, .txt)."
    ),
    instruction=(
        "If the question contains a file with .py, get the code file and, depending on the question and the file provided, execute the code and provide the final answer. "
        "If the question contains a spreadsheet file like .xlsx or .csv, get the file, use pandas to analyze it, and provide the final answer. "
        "If the question contains a file with .txt or .json, get the file and use code to parse it and answer the question. "
        "Always use the code execution tool to run your code and provide only the final answer."
    ),
    tools=[built_in_code_execution],
)

search_agent = LlmAgent(
    name='searchgaiaAgent',
    model="gemini-2.0-flash",
    description=(
        "You are a smart agent that can search the web to answer questions."
    ),
    instruction=(
        "Get the URL associated with the question, perform a web search, consolidate the information, and answer the provided question."
    ),
    tools=[google_search],
)

image_agent = LlmAgent(
    name='imagegaiaAgent',
    model="gemini-2.0-flash",
    description=(
        "You are a smart agent that can analyze an image file and answer any questions related to it."
    ),
    instruction=(
        "Get the image file from the link provided in the prompt. Use your multimodal capabilities to understand the image and answer the question."
    ),
)

youtube_agent = LlmAgent(
    name='youtubegaiaAgent',
    model="gemini-2.0-flash",
    description=(
        "You are a smart agent that can watch a YouTube video and answer any questions related to it."
    ),
    instruction=(
        "Get the YouTube link from the prompt. Use your multimodal capabilities to watch the video and answer the provided question."
    ),
)

root_agent = LlmAgent(
    name='basegaiaAgent',
    model="gemini-2.0-flash",
    description=(
        "You are a master agent that orchestrates sub-agents to answer various types of questions."
    ),
    instruction=(
        "You are a helpful orchestrator agent. Your primary goal is to answer a series of questions and submit them. "
        "First, invoke your tool 'answer_questions' to retrieve the list of questions. "
        "Once you receive the list, iterate through each question. For each one, delegate to the most appropriate sub-agent (code, search, youtube, image) based on its description and the question's content (e.g., file type). "
        "After getting the answer from the sub-agent, format it into a dictionary with 'task_id' and 'submitted_answer' keys. The task_id is at the beginning of each question string, separated by a colon. "
        "Collect all these answer dictionaries into a single list. "
        "Finally, pass this complete list of dictionaries to the 'submit_questions' tool to submit all answers at once."
    ),
    tools=[
        responses_api,
        submit_api,
        agent_tool.AgentTool(agent=code_agent),
        agent_tool.AgentTool(agent=search_agent),
        agent_tool.AgentTool(agent=youtube_agent),
        agent_tool.AgentTool(agent=image_agent)
    ],
)

session_service = InMemorySessionService()
runner = Runner(agent=root_agent, app_name=APP_NAME, session_service=session_service)

async def process_questions_and_answer():
    """
    Orchestrates the entire process of fetching questions, answering them
    using the agent, and submitting the final answers.
    """
    session = await session_service.create_session(
        app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID
    )
    print(f"===== Application Startup at {session.create_time} =====")
    print(f"Session created: {session.session_id}")

    # Initial prompt to kick off the agent's task
    initial_prompt = "Please get all the questions, answer each one by delegating to the correct tool or sub-agent, format the answers, and then submit the final list."

    print("\nSending initial prompt to the agent...")
    print(f"Prompt: '{initial_prompt}'")

    # Run the agent and stream events
    async for event in runner.run_async(
        session_id=session.session_id,
        content=types.Content(role="user", parts=[types.Part(text=initial_prompt)]),
    ):
        if event.action == EventActions.AGENT_RESPONSE and event.author == root_agent.name:
            if event.content and event.content.parts:
                print(f"\nFinal Agent Response: {event.content.parts[0].text}")
        elif event.action == EventActions.TOOL_OUTPUT:
             if event.content and event.content.parts and event.content.parts[0].tool_output:
                tool_output = event.content.parts[0].tool_output
                print(f"\n<-- Tool Output from {tool_output.tool_name}:")
                for key, value in tool_output.data.items():
                    print(f"    {key}: {value}")

    print("\n===== Task Complete =====")

async def main():
    """Main entry point for the application."""
    await process_questions_and_answer()

if __name__ == "__main__":
    asyncio.run(main())