app.py

import os
import tempfile
import time
import gradio as gr
import requests
import inspect
import pandas as pd
import spaces

from smolagents import (
    ActionStep,
    CodeAgent,
    LiteLLMModel,
    WebSearchTool,
    VisitWebpageTool,
    WikipediaSearchTool,
)

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


@spaces.GPU
def _zerogpu_startup_check():
    # This Space runs on ZeroGPU hardware but the agent below only makes
    # network calls (Groq API, web search) and never touches CUDA.
    # ZeroGPU requires at least one @spaces.GPU function to be declared,
    # so this no-op satisfies that check without spending any GPU quota
    # (it is never actually invoked).
    return None

# GAIA benchmark expects a terse, exact-match final answer.
GAIA_ANSWER_FORMAT_INSTRUCTIONS = """You are a general AI assistant. I will ask you a question.
Report your thoughts, and finish your work by calling final_answer() with your answer.
Your final 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 commas to write it, and don't use units such as $ or % unless specified otherwise.
If you are asked for a string, don't use articles or abbreviations (e.g. for cities), and write digits in plain text unless specified otherwise.
If you are asked for a comma separated list, apply the above rules to each element depending on whether it's a number or a string.
"""

class RateLimiter:
    """
    Enforces a minimum delay between successive agent LLM calls (one call per
    step) as a step_callback, to stay under the model provider's
    requests-per-minute limit. Groq's free tier for llama-3.3-70b-versatile
    caps at 30 RPM, so the default here (2.5s) targets that with a small
    margin; override via RATE_LIMIT_SECONDS_BETWEEN_CALLS for other tiers.
    Note this only protects against per-minute limits - free tiers also
    often cap total requests/day, which this can't work around.
    """
    def __init__(self, min_seconds_between_calls: float = 2.5):
        self.min_seconds_between_calls = min_seconds_between_calls
        self._last_call_at: float | None = None

    def __call__(self, memory_step: ActionStep, agent: CodeAgent) -> None:
        now = time.monotonic()
        if self._last_call_at is not None:
            wait = self.min_seconds_between_calls - (now - self._last_call_at)
            if wait > 0:
                print(f"Rate limiter: sleeping {wait:.1f}s to stay under the RPM limit")
                time.sleep(wait)
        self._last_call_at = time.monotonic()


# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        model_id = os.getenv("AGENT_MODEL_ID", "groq/llama-3.3-70b-versatile")
        api_key = os.getenv("GROQ_API_KEY")
        if not api_key:
            print("Warning: GROQ_API_KEY is not set - the agent will fail to call the model.")

        self.model = LiteLLMModel(model_id=model_id, api_key=api_key, temperature=0)
        self.agent = CodeAgent(
            model=self.model,
            tools=[WebSearchTool(), VisitWebpageTool(), WikipediaSearchTool()],
            add_base_tools=True,  # adds DuckDuckGo search + Whisper audio transcriber
            additional_authorized_imports=[
                "pandas", "numpy", "math", "re", "json", "itertools",
                "collections", "statistics", "datetime", "io", "openpyxl", "PIL",
            ],
            max_steps=12,
            step_callbacks=[RateLimiter(float(os.getenv("RATE_LIMIT_SECONDS_BETWEEN_CALLS", "2.5")))],
        )
        print("BasicAgent initialized.")

    def __call__(self, question: str, file_path: str | None = None) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        task = GAIA_ANSWER_FORMAT_INSTRUCTIONS + f"\nQuestion: {question}"
        if file_path:
            task += (
                f"\n\nA file for this question was downloaded locally to: {file_path}\n"
                "Open/read it with Python (pandas, openpyxl, PIL, etc. as appropriate) to answer the question."
            )
        try:
            answer = self.agent.run(task)
        except Exception as e:
            print(f"Agent run failed: {e}")
            return f"AGENT ERROR: {e}"
        answer = str(answer).strip()
        print(f"Agent returning answer: {answer}")
        return 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}")
    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...")
    with tempfile.TemporaryDirectory() as tmp_dir:
        for item in questions_data:
            task_id = item.get("task_id")
            question_text = item.get("question")
            file_name = item.get("file_name")
            if not task_id or question_text is None:
                print(f"Skipping item with missing task_id or question: {item}")
                continue

            file_path = None
            if file_name:
                try:
                    file_response = requests.get(f"{api_url}/files/{task_id}", timeout=30)
                    file_response.raise_for_status()
                    file_path = os.path.join(tmp_dir, file_name)
                    with open(file_path, "wb") as f:
                        f.write(file_response.content)
                except requests.exceptions.RequestException as e:
                    print(f"Could not download attached file for task {task_id}: {e}")
                    file_path = None

            try:
                submitted_answer = agent(question_text, file_path=file_path)
                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.

        **Setup:** This agent calls Groq (Llama 3.3 70B) via `smolagents`. Get a free key at https://console.groq.com/keys and set it as the `GROQ_API_KEY` secret in this Space's settings before running.
        """
    )

    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)