app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd

# 🔹 NOVO: imports do smolagents
from smolagents import CodeAgent, InferenceClientModel

# --- Constants ---
# (mantido como no template)
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


# =========================================================
#  Helper: limpeza de resposta para EXACT MATCH
# =========================================================
def clean_answer(text: str) -> str:
    """
    Limpa a saída do modelo para ficar mais adequada ao EXACT MATCH.
    Remove prefixos como 'Answer:', 'Final answer:' etc.,
    aspas externas e ponto final solto.
    """
    if text is None:
        return ""

    ans = str(text).strip()

    # remove prefixos comuns
    prefixes = [
        "answer:", "resposta:", "final answer:", "final:", "ans:", "a:",
        "the answer is", "the final answer is",
    ]
    lower = ans.lower()
    for p in prefixes:
        if lower.startswith(p):
            ans = ans[len(p):].strip()
            break

    # remove ponto final se não parecer número decimal
    if ans.endswith(".") and not ans.replace(".", "", 1).isdigit():
        ans = ans[:-1].strip()

    # remove aspas externas
    if len(ans) > 1 and ans[0] == ans[-1] and ans[0] in ["'", '"']:
        ans = ans[1:-1].strip()

    return ans


# =========================================================
#  Basic Agent Definition – AGORA usando smolagents
# =========================================================

SYSTEM_PROMPT = (
    "You are an exam-taking assistant.\n"
    "For each question, reply with ONLY the final answer, with no explanation, "
    "no reasoning, no extra words, no quotes, and no labels like 'Final answer'.\n"
    "If the answer is a number, output just the number. "
    "If it is a word or short phrase, output just that.\n"
    "Your output will be compared to the ground truth using EXACT MATCH."
)


class BasicAgent:
    """
    Agente simples baseado em smolagents:
    - Usa InferenceClientModel (Inference API da Hugging Face)
    - Não utiliza tools adicionais
    - Retorna uma string já limpa para EXACT MATCH
    """

    def __init__(self):
        print("Initializing smolagents BasicAgent...")

        # Modelo remoto via Inference API (utiliza HF_TOKEN configurado no Space)
        self.model = InferenceClientModel()

        # CodeAgent sem ferramentas (agente simples)
        self.agent = CodeAgent(
            model=self.model,
            tools=[],            # agente simples: sem tools
            max_steps=1,         # sem tools, 1 passo é suficiente
            system_prompt=SYSTEM_PROMPT,
        )

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 80 chars): {question[:80]}...")
        try:
            raw_answer = self.agent.run(question)
            fixed_answer = clean_answer(raw_answer)
            print(f"Agent returning cleaned answer: {fixed_answer}")
            return fixed_answer
        except Exception as e:
            print(f"Error inside BasicAgent.__call__: {e}")
            # Em caso de erro, devolve string vazia (melhor do que quebrar tudo)
            return ""


# =========================================================
#  Runner + submit (mantido do template, só usando BasicAgent novo)
# =========================================================
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 (agora nosso agente smolagents)
    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
    # (useful for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Agent code URL: {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 (smolagents)")
    gr.Markdown(
        """
        **Instructions:**
        1.  This space uses a simple agent built with `smolagents` + `InferenceClientModel`.
        2.  Log in to your Hugging Face account using the button below.
        3.  Click **'Run Evaluation & Submit All Answers'** to fetch questions,
            run the agent, submit answers, and see your score.
        ---
        **Notes:**
        - The correction on the server uses EXACT MATCH, so the agent is prompted
          to output only the final answer (sem 'FINAL ANSWER', sem explicações).
        - This template is intentionally simples; você pode adicionar tools,
          melhorar o prompt, etc., se quiser subir seu score.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(
        label="Run Status / Submission Result", lines=5, interactive=False
    )
    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)