Update app.py

app.py

@@ -8,60 +8,30 @@ 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
+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 built_in_code_execution
 from google.adk.tools import agent_tool
-
-
-
-
-
-
-
+import asyncio
 
 logging.basicConfig(level=logging.ERROR)
-#from google.adk.tools import agent_tool
+
 url = 'https://agents-course-unit4-scoring.hf.space/questions'
 headers = {'accept': 'application/json'}
 response = requests.get(url, headers=headers)
 
-# class responses_api(BaseAgent):
-#     async def _run_async_impl(self, ctx: InvocationContext)-> AsyncGenerator[Event, None]:
-#         # This method is called when the agent is run
-#         # You can implement your logic here
-#         # For example, you can call an external API or perform some calculations
-#         # and return the result
-#         url = 'https://agents-course-unit4-scoring.hf.space/questions'
-#         headers = {'accept': 'application/json'}
-#         response = requests.get(url, headers=headers)
-#         for i in response.json():
-#             if i['file_name'] != '':
-#                 url_file = f"https://agents-course-unit4-scoring.hf.space/files/{i['task_id']}"
-#                 question = i['question']
-#                 prompt = f"{question} and the file is {url_file}, give the final answer only"
-#             else:
-#                 question = i['question']
-#                 prompt = f"{question} give the final answer only"
-#             existing_responses = ctx.session.state.get("user:responses", [])
-#             existing_responses.append(prompt)
-#             ctx.session_state["user:responses"] = existing_responses
-
-#             # Optionally, yield a single event to indicate completion or provide some output
-#             yield Event(author=self.name, content=types.Content(parts=[types.Part(text=f"Fetched {len(questions_data)} questions.")]))
-
-
-
 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'] != '':
+        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"
@@ -70,27 +40,28 @@ def answer_questions():
             prompt = f"{task_id}:{question} give the final answer only"
         prompts.append(prompt)
     return prompts
-#responses_api = responses_api(name= 'responses_api_1')
-from typing import Dict, Any
-def submit_questions(answers: list[str]) -> Dict[str, Any]:
+
+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}
+        "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)
+    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)
+responses_api = FunctionTool(func=answer_questions)
 submit_api = FunctionTool(func=submit_questions)
 
 # class QuestionAnswerer(LlmAgent):
@@ -99,147 +70,126 @@ submit_api = FunctionTool(func=submit_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")
 
-# qa = QuestionAnswerer(name = 'qa_1', model="gemini-2.0-flash", description="Question Answerer")
-
-
-
-
-
-
-
-
-APP_NAME="weather_sentiment_agent"
+APP_NAME="final_assignment_agent"
 USER_ID="user1234"
-SESSION_ID="1234"
-
+SESSION_ID="5678"
 
 code_agent = LlmAgent(
     name='codegaiaAgent',
-    model="gemini-2.5-pro-preview-05-06",
+    model="gemini-2.0-flash",
     description=(
-        "You are a smart agent that can write and execute code and answer any questions provided access the given files and answer"
+        "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 and .csv among others, get the file and depending on the question and the file provided, execute the code and provide the final answer. "
-"use code like import pandas as pd , file = pd.read_csv('file.csv') and then use the file to answer the question. "
-"if the question contains a file with .txt ,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 file with .json ,Get the code file and depending on the question and the file provided, execute the code and provide the final answer. "
-"If you are writing code or if you get a code file, use the code execution tool to run the code and provide the final answer. "
-)
-
-,
-    # tools=[built_in_code_execution], 
-    # Add the responses_api agent as a tool
-    # sub_agents=[responses_api]
+    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-2.5-pro-preview-05-06",
+    model="gemini-2.0-flash",
     description=(
-        "You are a smart agent that can search the web and answer any questions provided access the given files and answer"
+        "You are a smart agent that can search the web to answer questions."
     ),
-    instruction = (
-    "Get the url associated perform a search and consolidate the information provided and answer the provided question "
-)
-
-,
-    tools=[google_search], 
-    # Add the responses_api agent as a tool
-    # sub_agents=[responses_api]
+    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-2.5-pro-preview-05-06",
+    model="gemini-2.0-flash",
     description=(
-        "You are a smart agent that can when given a image file and answer any questions related to it"
+        "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."
     ),
-    instruction = (
-    "Get the image file from the link associated in the prompt use Gemini to watch the video and answer the provided question ")
-
-,
-    # tools=[google_search], 
-    # # Add the responses_api agent as a tool
-    # sub_agents=[responses_api]
 )
 
-
 youtube_agent = LlmAgent(
     name='youtubegaiaAgent',
-    model="gemini-2.5-pro-preview-05-06",
+    model="gemini-2.0-flash",
     description=(
-        "You are a smart agent that can when given a youtube link watch it and answer any questions related to it"
+        "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."
     ),
-    instruction = (
-    "Get the youtube link associated use Gemini to watch the video and answer the provided question ")
-
-,
-    tools=[google_search], 
-    # Add the responses_api agent as a tool
-    # sub_agents=[responses_api]
 )
 
 root_agent = LlmAgent(
     name='basegaiaAgent',
-    model="gemini-2.5-pro-preview-05-06",
+    model="gemini-2.0-flash",
     description=(
-        "You are a smart agent that can answer any questions provided access the given files and answer"
+        "You are a master agent that orchestrates sub-agents to answer various types of questions."
     ),
-    instruction = (
-    "You are a helpful agent. When the user asks to get the questions or makes a similar request, "
-    "invoke your tool 'responses_api' to retrieve the questions. "
-    "Once you receive the list of questions, loop over each question and provide a concise answer for each based on the question and any provided file. "
-    "For every answer, return a dictionary with the keys task_id and submitted_answer, for example: "
-    "{'task_id': 'the-task-id', 'submitted_answer': 'your answer'}. "
-    "Collect all such dictionaries in a list (do not include any backslashes), and pass this list to the 'submit_api' tool to submit the answers."
-)
-
-,
-    tools=[responses_api,submit_api,agent_tool.AgentTool(agent = code_agent),\
-           agent_tool.AgentTool(agent = search_agent), agent_tool.AgentTool(youtube_agent), agent_tool.AgentTool(image_agent)], 
-    # Add the responses_api agent as a tool
-    # sub_agents=[responses_api]
+    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)
+    ],
 )
 
-# root_agent = LlmAgent(
-#     name='gaiaAgent',
-#     model="gemini-2.5-pro-preview-05-06",
-#     description=(
-#         "You are a smart agent that can answer any questions provided access the given files and answer"
-#     ),
-#     instruction = (
-#     "You are a helpful agent. When the user asks to get the questions or makes a similar request, "
-#     "invoke base agent. "
-#     "Once you the answers check if are in correct format. "
-#     #"Collect all such dictionaries in a list (do not include any backslashes), and pass this list to the 'submit_api' tool to submit the answers."
-# )
-
-# ,
-#     #tools=[submit_api], 
-#     # Add the responses_api agent as a tool
-#     sub_agents=[base_agent]
-# )
-
 session_service = InMemorySessionService()
-session = session_service.create_session(app_name=APP_NAME, \
-                                        user_id=USER_ID,\
-                                        session_id=SESSION_ID)
-import asyncio
-runner = Runner(agent=root_agent, app_name=APP_NAME, session_service= session_service)
-def send_query_to_agent(root_agent, query, session):
-    session = session
-    content = types.Content(role='user', parts=[types.Part(text=query)])
-
-
-
+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__":
-    import asyncio
-    asyncio.run(main())
+    asyncio.run(main())