updated agent code

app.py

@@ -1,196 +1,245 @@
-import os
-import gradio as gr
+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
-import inspect
-import pandas as pd
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
+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": "your_agent_code",
+    "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:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
-        try:
-            submitted_answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
-        except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-        """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+
+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
+# #     asyncio.run(main())