app.py

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

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

_import_error_msgs = []
try:
    # Use CodeAgent (stable export), DuckDuckGoSearchTool, InferenceClientModel, and tool decorator
    from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel, tool
except Exception as e:
    CodeAgent = None
    DuckDuckGoSearchTool = None
    InferenceClientModel = None
    tool = None
    _import_error_msgs.append(repr(e))


# --- Utilities ---
def _clean_answer(raw: Any) -> str:
    """
    Heuristic cleaning to produce a single-line exact-match-friendly answer.

    - Keep the last non-empty line of output.
    - Remove common labels like "Answer:", "Final answer:".
    - Strip surrounding quotes and whitespace.
    - Collapse internal whitespace to single spaces.

    Args:
        raw (Any): Raw agent output to clean.

    Returns:
        str: Cleaned single-line answer string.
    """
    if raw is None:
        return ""
    text = str(raw)

    lines = [ln.strip() for ln in text.replace("\r", "").split("\n") if ln.strip() != ""]
    if not lines:
        candidate = text.strip()
    else:
        candidate = lines[-1]

    candidate = re.sub(r'^(final answer[:\-\s]*)', '', candidate, flags=re.IGNORECASE)
    candidate = re.sub(r'^(answer[:\-\s]*)', '', candidate, flags=re.IGNORECASE)
    candidate = re.sub(r'^(the answer is[:\-\s]*)', '', candidate, flags=re.IGNORECASE)

    candidate = candidate.strip().strip('\'"')
    candidate = re.sub(r'\s+', ' ', candidate)
    return candidate


# --- Safe small arithmetic evaluator tool ---
def _safe_eval_arith(expr: str) -> str:
    """
    Safely evaluate simple arithmetic expressions using ast.

    Supports: + - * / ** % unary ops and parentheses, numeric literals.
    Rejects names, attribute access, calls, comprehensions, etc.
    """
    try:
        node = ast.parse(expr, mode="eval")

        # Define allowed node types
        allowed_nodes = (
            ast.Expression, ast.BinOp, ast.UnaryOp, ast.Num, ast.Constant,
            ast.Add, ast.Sub, ast.Mult, ast.Div, ast.Pow, ast.Mod,
            ast.UAdd, ast.USub, ast.Load, ast.Tuple, ast.List, ast.Expr,
            ast.Subscript, ast.Index, ast.Slice, ast.Tuple
        )

        # Walk the AST and ensure nodes are permitted
        for n in ast.walk(node):
            if not isinstance(n, allowed_nodes):
                # numeric constants in Python 3.8+ are ast.Constant
                # allow parentheses (they are represented by grouping nodes)
                raise ValueError(f"Disallowed expression element: {type(n).__name__}")

        # Evaluate in a restricted namespace
        result = eval(compile(node, filename="<ast>", mode="eval"), {"__builtins__": {}}, {})
        return str(result)
    except Exception as e:
        return f"ERROR_EVAL: {e}"


# --- Tools (must have good docstrings for smolagents) ---
if tool is not None:
    @tool
    def download_gaia_file(task_id: str) -> str:
        """
        Download the text content of the file associated with a GAIA task ID.

        Args:
            task_id (str): The task identifier for which the file should be downloaded. This
                value comes from the GAIA questions endpoint and is used to fetch the file via
                the /files/{task_id} route.

        Returns:
            str: The textual content of the downloaded file, or an error string beginning with
                 'ERROR_DOWNLOADING_FILE:' in case of failure.
        """
        try:
            url = f"{DEFAULT_API_URL}/files/{task_id}"
            resp = requests.get(url, timeout=20)
            resp.raise_for_status()
            # Return text, decoding bytes defensively
            if isinstance(resp.content, (bytes, bytearray)):
                return resp.content.decode(resp.encoding or "utf-8", errors="replace")
            return resp.text
        except Exception as e:
            return f"ERROR_DOWNLOADING_FILE: {e}"

    @tool
    def web_search(query: str) -> str:
        """
        Execute a web search using DuckDuckGoSearchTool (wrapped) and return the combined results.

        Args:
            query (str): A natural-language query describing the information to find.

        Returns:
            str: Search results or a short error string beginning with 'ERROR_SEARCH:'.
        """
        try:
            # Construct a minimal wrapper call to DuckDuckGoSearchTool
            # The actual DuckDuckGoSearchTool object will be created in agent init
            return DuckDuckGoSearchTool()(query)
        except Exception as e:
            return f"ERROR_SEARCH: {e}"

    @tool
    def simple_calc(expression: str) -> str:
        """
        Compute a simple arithmetic expression safely.

        Args:
            expression (str): A mathematical expression like '2 + 3 * (4 - 1)'.

        Returns:
            str: The numeric result as a string, or an error string beginning with 'ERROR_EVAL:'.
        """
        return _safe_eval_arith(expression)
else:
    # If smolagents.tool not available, define fallback functions that raise helpful errors
    def download_gaia_file(task_id: str) -> str:
        raise RuntimeError("smolagents.tool decorator unavailable. Install smolagents and redeploy. Import errors: " + "; ".join(_import_error_msgs))
    def web_search(query: str) -> str:
        raise RuntimeError("smolagents.tool decorator unavailable. Install smolagents and redeploy. Import errors: " + "; ".join(_import_error_msgs))
    def simple_calc(expression: str) -> str:
        raise RuntimeError("smolagents.tool decorator unavailable. Install smolagents and redeploy. Import errors: " + "; ".join(_import_error_msgs))


# --- Leaderboard-grade Agent (CodeAgent) ---
class BasicAgent:
    def __init__(self):
        if CodeAgent is None or InferenceClientModel is None or DuckDuckGoSearchTool is None:
            raise RuntimeError(
                "smolagents imports failed. Ensure 'smolagents' is in requirements.txt and redeploy. "
                "Import details: " + "; ".join(_import_error_msgs)
            )

        print("Initializing GAIA leaderboard-grade agent (CodeAgent)...")

        model_id = os.getenv("HF_MODEL_ID", "Qwen/Qwen2.5-72B-Instruct")
        try:
            self.model = InferenceClientModel(
                model_id=model_id,
                temperature=0.0
            )
        except Exception as e:
            raise RuntimeError(f"Failed to init InferenceClientModel({model_id}): {e}")

        # Instantiate the real search tool object and put our tools in list
        try:
            ddg = DuckDuckGoSearchTool()
            self.tools = [ddg, download_gaia_file, simple_calc]
        except Exception as e:
            raise RuntimeError(f"Failed to init tools: {e}")

        # Instructions to bias towards exact final-answer-only outputs
        self.system_instructions = (
            "You are solving GAIA benchmark questions. Use available tools when needed. "
            "If a file is referenced, download and read it. Do NOT reveal your chain-of-thought or reasoning. "
            "The final output MUST be exactly the answer only (one short line). No extra commentary, no 'FINAL ANSWER'."
        )

        # Initialize CodeAgent; argument signatures may vary across versions, handle common cases
        try:
            self.agent = CodeAgent(
                tools=self.tools,
                model=self.model
            )
        except TypeError:
            self.agent = CodeAgent(self.model, self.tools)

    def __call__(self, question: str) -> str:
        """
        Run the CodeAgent on the provided question and return a cleaned single-line answer.
        """
        try:
            prompt = f"{self.system_instructions}\n\nQUESTION:\n{question}\n\nAnswer:"
            # Some smolagents versions accept dict input; try string then dict
            try:
                raw = self.agent.run(prompt)
            except TypeError:
                raw = self.agent.run({"input": prompt})
            cleaned = _clean_answer(raw)
            return cleaned
        except Exception as e:
            tb = traceback.format_exc()
            print("Agent runtime error:", e, tb)
            return f"AGENT_ERROR: {e}"

            

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)