app.py

import textwrap
import re
import os
import gradio as gr
import requests
import inspect
import datetime
from textwrap import dedent
import pandas as pd
from dotenv import load_dotenv

# Import smolagents components
from smolagents import CodeAgent, HfApiModel, DuckDuckGoSearchTool, FinalAnswerTool

# Load environment variables from .env file
load_dotenv()

# (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 ------

# Initialize the search tool
search_tool = DuckDuckGoSearchTool()


class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")

        self.store_questions_to_log_file = False

        # Create a filename with current date and time
        current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M")
        self.filename = f"questions_{current_time}.txt"

        if self.store_questions_to_log_file:
            print(f"Questions will be written to {self.filename}")
            # Clear the file if it exists or create a new one
            with open(self.filename, 'w', encoding='utf-8') as f:
                f.write('')  # Create empty file


        # Initialize the Large Language Model
        # The model is used by both agents in this simple setup
        self.model = HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct")
        # mistralai/Mixtral-8x7B-Instruct-v0.1
        #self.model = HfApiModel(model_id="mistralai/Mixtral-8x7B-Instruct-v0.1")


        # Define the Web Search Agent
        # This agent is specialised for searching the web using a specific tool
        self.web_search_agent = CodeAgent(
            model=self.model,  # Assign the model to the agent [
            tools=[DuckDuckGoSearchTool(),
                   FinalAnswerTool()],  # Provide the web search tool
            name="web_search_agent",  # Give the agent a name
            # Describe its capability [
            description="Searches the web for information.",
            verbosity_level=1,  # Set verbosity level for logging
            max_steps=5,  # Limit the steps the agent can take
        )

        # Define the Manager Agent
        # This agent manages tasks and delegates to other agents
        self.manager_agent = CodeAgent(
            model=self.model,  # Assign the model to the manager
            tools=[FinalAnswerTool()],
            managed_agents=[self.web_search_agent],  # Specify the agents this manager oversees
            name="manager_agent",  # Give the manager agent a name
            description="Manages tasks by delegating to other agents.",  # Describe its role
            additional_authorized_imports=[
                "json", "re", "pandas", "numpy", "math", "collections", "itertools", "stat", "statistics", "queue", "unicodedata", "time", "random", "datetime"],  # Allow specific imports
            verbosity_level=1,  # Set verbosity level
            max_steps=5,  # Limit the steps
        )

        print("MultiAgentSystem initialization complete.")


    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")


        # For all other questions, use the manager agent with web search
        # manager_prompt = dedent(f"""
        #     I need to answer the following question accurately:

        #     {question}

        #     Please analyze this question and determine the best approach to answer it.
        #     If needed, use web search to find relevant information.
        #     Provide a concise, accurate answer to the question.
        # """)

        manager_prompt = textwrap.dedent(f"""
    I need to answer the following question accurately:

    {question}

    Please analyze this question and determine the best approach to answer it.
    If needed, use web search to find relevant information.
    Provide a concise, accurate answer to the question.

    IMPORTANT: If you identify that specialized tools are needed that you don't have access to, respond with:
    "Missing Tool Warning: Can't process the question. Missing tool for [specify the missing capability]."

    Examples of missing capabilities to check for:
    - YouTube video analysis (if question mentions YouTube videos)
    - Image analysis (if question refers to analyzing images)
    - Audio file processing (if question refers to audio files)
    - Excel/spreadsheet analysis (if question refers to Excel files)
    - Chess position analysis (if question refers to chess positions)
    - Code execution (if question requires running Python code)
    
    Only use the "Missing Tool Warning" format if you CANNOT answer the question with your available tools.
    If you can answer the question with web search or your existing knowledge, provide the answer.
        """)

        manager_agent_response = "I apologize, but I couldn't find an answer to this question."
        source = ""
        try:
            manager_agent_response = self.manager_agent.run(manager_prompt)
            source = "manager_agent"

            # Check if the answer contains a missing tool warning
            # if "Missing Tool Warning:" in manager_agent_response:
            #    return manager_agent_response

        except Exception as e:
            print(f"Error in manager agent: {e}")
            source = f"Exception {e} "

        # Append the question to the file
        if self.store_questions_to_log_file:
            with open(self.filename, 'a', encoding='utf-8') as f:
                f.write(f"{question}\n")
                f.write(f"ANSWER by {source}: {manager_agent_response}\n")
                f.write(f"{'*'*50}\n")

        print(f"Final answer: {manager_agent_response}")
        return manager_agent_response


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}")
    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)