app.py

import os
import gradio as gr
import requests
import pandas as pd
from _types import Questions, Question, UserScore
from llama_index.core.agent.workflow import AgentWorkflow
from llama_index.core.workflow import Context
from _tools import (
    search_agent,
    fetch_file_agent,
    bytes_to_image_agent,
    document_bytes_to_text_agent,
    xlsx_to_text_agent,
    extract_text_from_image_agent,
    extract_text_from_csv_agent,
    extract_text_from_code_file_agent,
    extract_text_from_audio_file_agent,
    webpage_to_markdown_agent,
)
from llama_index.core.agent.workflow import AgentWorkflow
import asyncio
from utils import cache_answers, update_cache_answer, get_cached_answer, load_cache


# (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.")

        agent = AgentWorkflow(
            agents=[
                search_agent,
                fetch_file_agent,
                bytes_to_image_agent,
                document_bytes_to_text_agent,
                xlsx_to_text_agent,
                extract_text_from_image_agent,
                extract_text_from_csv_agent,
                extract_text_from_code_file_agent,
                extract_text_from_audio_file_agent,
                webpage_to_markdown_agent,
            ],
            root_agent="search_agent",
            verbose=True,
        )

        # context = Context(agent)
        self.agent = agent
        # self.context = context

    async def run(self, question: Question) -> str:
        question_text = question["question"]
        task_id = question["task_id"]
        file_name = question.get("file_name")
        
        """
        Run the agent with the provided question and return the answer.
        """
        print(f"Agent received question (first 50 chars): {question_text[:50]}...")
        
        prompt = f"""
        You are a general AI assistant. I will ask you a question. Think carefully and give your answer straight away as asked in the question or
        in the format below: 
        
        Answer should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
        If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. 
        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless
        specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in
        the list is a number or a string.\n
        
        Don't use any other format than the one above and limit your attempts to answer the question to 3 times.
                
        The question is: {question_text}\n
        
        If the question has a file, the file name is the task ID: {task_id}. You can use it to fetch the bytes of the file and parse
        as you want. The file name is: {file_name}.\n
        """

        try:
        # Retrieve the answer from the cache if it exists
            cached = get_cached_answer(task_id)
            if cached and cached.get("isCorrect") and cached.get("answer"):
                print(f"Returning cached correct answer for task_id {task_id}: {cached['answer']}")
                
                return str(cached["answer"])

            answer = await self.agent.run(prompt)

            print(f"Agent returning answer: {answer}")
            
            return str(answer)
        except Exception as e:
            print(f"Error running agent on question {question['task_id']}: {e}")
            return "AGENT ERROR: " + str(e)

def instantiate_agent():
    try:
        agent = BasicAgent()
        
        return agent, None
    
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        
        return None, f"Error initializing agent: {e}"

def fetch_questions(questions_url):
    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 None, "Fetched questions list is empty or invalid format."
        
        print(f"Fetched {len(questions_data)} questions.")
        
        return questions_data, None
    
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        
        return None, f"Error fetching questions: {e}"
    
    except requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}")
        print(f"Response text: {response.text[:500]}")
        
        return None, f"Error decoding server response for questions: {e}"
    
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        
        return None, f"An unexpected error occurred fetching questions: {e}"

async def run_agent_on_questions(agent: BasicAgent, questions_data: Questions):
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")

    # Inicializa o cache com todas as respostas erradas (se ainda não existir)
    cache_answers(questions_data)

    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 = await agent.run(item)
            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})
            # Update the cache with the answer
            update_cache_answer(task_id, submitted_answer, is_correct=False)
            
        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}"})
            update_cache_answer(task_id, f"AGENT ERROR: {e}", is_correct=False)

    return answers_payload, results_log

def submit_answers(submit_url, submission_data, results_log):
    print(f"Submitting {len(submission_data['answers'])} answers to: {submit_url}")
    
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data: UserScore = 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

async def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    space_id = os.getenv("SPACE_ID")
    
    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"
    

    agent, agent_error = instantiate_agent()
    
    if agent_error:
        return agent_error, None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)
    questions_data, questions_error = fetch_questions(questions_url)
    
    if questions_error:
        return questions_error, None

    answers_payload, results_log = await run_agent_on_questions(agent, questions_data)

    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)

    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)
    print(f"Cached answers: {load_cache()}")

    return submit_answers(submit_url, submission_data, results_log)


async def main():
    await run_and_submit_all(profile=None)

loop = asyncio.get_event_loop()
loop.run_until_complete(main())

# --- 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)