Update agent.py

agent.py

@@ -1,235 +1,240 @@
-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
-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
-
-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():
-    url = 'https://agents-course-unit4-scoring.hf.space/questions'
-    headers = {'accept': 'application/json'}
-    response = requests.get(url, headers=headers)
-    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
-#responses_api = responses_api(name= 'responses_api_1')
-from typing import Dict, Any
-def submit_questions(answers: list[str]) -> Dict[str, Any]:
-    url = 'https://agents-course-unit4-scoring.hf.space/submit'
-    payload = {
-    "username": "ashishja",
-    "agent_code": "https://huggingface.co/spaces/ashishja/Agents_Course_Final_Assignment_Ashish/tree/main",
-    "answers": answers}
-    headers = {'accept': 'application/json', "Content-Type": "application/json"}
-    response = requests.post(url, headers=headers, json =payload)
-    import json
-    print(json.dumps(payload, indent=2))
-    if response.status_code == 200:
-        return response.json()
-    else:
-        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-2.0-flash", description="Question Answerer")
-
-
-
-
-
-
-
-
-APP_NAME="weather_sentiment_agent"
-USER_ID="user1234"
-SESSION_ID="1234"
-
-
-code_agent = LlmAgent(
-    name='codegaiaAgent',
-    model="gemini-2.5-pro-preview-05-06",
-    description=(
-        "You are a smart agent that can write and execute code and answer any questions provided access the given files and answer"
-    ),
-    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]
-)
-
-
-search_agent = LlmAgent(
-    name='searchgaiaAgent',
-    model="gemini-2.5-pro-preview-05-06",
-    description=(
-        "You are a smart agent that can search the web and answer any questions provided access the given files and answer"
-    ),
-    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]
-)
-
-image_agent = LlmAgent(
-    name='imagegaiaAgent',
-    model="gemini-2.5-pro-preview-05-06",
-    description=(
-        "You are a smart agent that can when given a image file and answer any questions related to it"
-    ),
-    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",
-    description=(
-        "You are a smart agent that can when given a youtube link watch it and answer any questions related to it"
-    ),
-    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",
-    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 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]
-)
-
-# 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)
-
-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)])
-
-# # async def main():
-# #     await process_questions_and_answer()
-
-# # if __name__ == "__main__":
-# #     import asyncio
+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
+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
+
+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():
+    """Fetch questions from the GAIA API and return them in a structured format"""
+    url = 'https://agents-course-unit4-scoring.hf.space/questions'
+    headers = {'accept': 'application/json'}
+    response = requests.get(url, headers=headers)
+
+    if response.status_code != 200:
+        return f"Error fetching questions: {response.status_code}"
+
+    questions_data = response.json()
+    return questions_data
+#responses_api = responses_api(name= 'responses_api_1')
+from typing import Dict, Any
+def submit_questions(answers: list[str]) -> Dict[str, Any]:
+    url = 'https://agents-course-unit4-scoring.hf.space/submit'
+    payload = {
+    "username": "ashishja",
+    "agent_code": "https://huggingface.co/spaces/ashishja/Agents_Course_Final_Assignment_Ashish/tree/main",
+    "answers": answers}
+    headers = {'accept': 'application/json', "Content-Type": "application/json"}
+    response = requests.post(url, headers=headers, json =payload)
+    import json
+    print(json.dumps(payload, indent=2))
+    if response.status_code == 200:
+        return response.json()
+    else:
+        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-2.0-flash", description="Question Answerer")
+
+
+
+
+
+
+
+
+APP_NAME="weather_sentiment_agent"
+USER_ID="user1234"
+SESSION_ID="1234"
+
+
+code_agent = LlmAgent(
+    name='codegaiaAgent',
+    model="gemini-2.5-pro-preview-05-06",
+    description=(
+        "You are a smart agent that can write and execute code and answer any questions provided access the given files and answer"
+    ),
+    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]
+)
+
+
+search_agent = LlmAgent(
+    name='searchgaiaAgent',
+    model="gemini-2.5-pro-preview-05-06",
+    description=(
+        "You are a smart agent that can search the web and answer any questions provided access the given files and answer"
+    ),
+    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]
+)
+
+image_agent = LlmAgent(
+    name='imagegaiaAgent',
+    model="gemini-2.5-pro-preview-05-06",
+    description=(
+        "You are a smart agent that can when given a image file and answer any questions related to it"
+    ),
+    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",
+    description=(
+        "You are a smart agent that can when given a youtube link watch it and answer any questions related to it"
+    ),
+    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",
+    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 your tool 'responses_api' to retrieve the questions data. "
+    "The questions data will be a list of dictionaries, each containing 'task_id', 'question', and 'file_name' fields. "
+    "For each question in the data: "
+    "1. If file_name is not empty, the file can be accessed at https://agents-course-unit4-scoring.hf.space/files/{task_id} "
+    "2. Use appropriate sub-agents based on question type (code_agent for coding, search_agent for web search, etc.) "
+    "3. Provide a concise, direct answer for each question "
+    "4. Return a dictionary with keys 'task_id' and 'submitted_answer' for each answer "
+    "5. Collect all dictionaries in a list and pass to 'submit_api' tool to submit the answers. "
+    "Always provide direct, factual answers without prefixes like 'The answer is:' or 'Final answer:'"
+)
+
+,
+    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]
+)
+
+# 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()
+# Create the default session synchronously
+try:
+    session = session_service.create_session(app_name=APP_NAME,
+                                            user_id=USER_ID,
+                                            session_id=SESSION_ID)
+    print(f"✅ Default session created: {SESSION_ID}")
+except Exception as e:
+    print(f"⚠️ Error creating default session: {e}")
+    session = None
+
+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)])
+
+# # async def main():
+# #     await process_questions_and_answer()
+
+# # if __name__ == "__main__":
+# #     import asyncio
 # #     asyncio.run(main())