app.py

"""GAIA Benchmark Evaluation Runner — smolagents CodeAgent"""
import os
import re
import gradio as gr
import requests
import pandas as pd
from smolagents import (
    CodeAgent,
    DuckDuckGoSearchTool,
)

# Import the correct model class (name changed across versions)
try:
    from smolagents import InferenceClientModel as ModelClass
except ImportError:
    try:
        from smolagents import HfApiModel as ModelClass
    except ImportError:
        from smolagents import ApiModel as ModelClass
import yaml

from tools.final_answer import FinalAnswerTool
from tools.visit_webpage import VisitWebpageTool
from tools.web_search import DuckDuckGoSearchTool as CustomSearchTool

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


def build_agent():
    """Build a smolagents CodeAgent equipped for GAIA benchmark tasks."""

    # Model — Use the HF Inference API
    # Try multiple models in order of preference
    model_id = os.getenv(
        "MODEL_ID",
        "Qwen/Qwen2.5-Coder-32B-Instruct"
    )
    model = ModelClass(
        max_tokens=4096,
        temperature=0.1,
        model_id=model_id,
        custom_role_conversions=None,
    )

    # Tools
    final_answer = FinalAnswerTool()
    visit_webpage = VisitWebpageTool()
    search_tool = CustomSearchTool(max_results=5)

    # Load prompt templates
    with open("prompts.yaml", "r") as stream:
        prompt_templates = yaml.safe_load(stream)

    agent = CodeAgent(
        model=model,
        tools=[search_tool, visit_webpage, final_answer],
        max_steps=12,
        verbosity_level=1,
        name="gaia_agent",
        description="An agent designed for GAIA benchmark question answering.",
        prompt_templates=prompt_templates,
    )
    return agent


def extract_answer(raw_answer) -> str:
    """Aggressively clean agent output to extract only the final answer value."""
    if raw_answer is None:
        return ""
    answer = str(raw_answer).strip()

    # If the answer contains final_answer("..."), extract the argument
    fa_match = re.search(r'final_answer\(["\'](.+?)["\']\)', answer, re.DOTALL)
    if fa_match:
        answer = fa_match.group(1).strip()

    # Remove code blocks (```py ... ```)
    answer = re.sub(r'```[\s\S]*?```', '', answer).strip()

    # Remove <end_code> tags and surrounding artifacts
    answer = re.sub(r'<end_code>.*', '', answer, flags=re.DOTALL).strip()

    # Remove Calling tools: [...] JSON metadata
    answer = re.sub(r'Calling tools:.*', '', answer, flags=re.DOTALL).strip()

    # Remove "Using the `final_answer` tool:" and similar
    answer = re.sub(r'Using the `final_answer` tool:.*', '', answer, flags=re.DOTALL).strip()

    # Remove Thought: / Code: sections if they leaked through
    answer = re.sub(r'^Thought:.*?(?=\S)', '', answer, flags=re.DOTALL).strip()

    # Remove common prefixes
    prefixes = [
        "FINAL ANSWER:", "Final Answer:", "final answer:",
        "The final answer is:", "The final answer is ",
        "The answer is:", "The answer is ",
        "Answer:", "Final answer:",
    ]
    for prefix in prefixes:
        if answer.lower().startswith(prefix.lower()):
            answer = answer[len(prefix):].strip()

    # Remove surrounding quotes if present
    if len(answer) >= 2:
        if (answer[0] == '"' and answer[-1] == '"') or \
           (answer[0] == "'" and answer[-1] == "'"):
            answer = answer[1:-1].strip()

    # Remove trailing periods (unless it's a decimal number)
    if answer.endswith('.') and not re.match(r'^\d+\.$', answer):
        answer = answer[:-1].strip()

    return answer


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the agent 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"

    # 1. Instantiate Agent
    try:
        agent = build_agent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    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: {e}")
        return f"Error decoding server response: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred: {e}", None

    # 3. Run Agent on each question
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    for i, item in enumerate(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:
            print(f"\n{'='*60}")
            print(f"Question {i+1}/{len(questions_data)} (task_id: {task_id})")
            print(f"Q: {question_text[:100]}...")
            raw_answer = agent.run(question_text, reset=True)
            submitted_answer = extract_answer(raw_answer)
            print(f"A: {submitted_answer}")
            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=120)
        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 ---
with gr.Blocks() as demo:
    gr.Markdown("# GAIA Benchmark Agent Evaluation")
    gr.Markdown(
        """
        **Instructions:**
        1. Log in to your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to fetch questions,
           run the agent, submit answers, and see the score.

        ---
        **Note:** This may take several minutes as the agent processes all questions.
        """
    )

    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)
    space_host = os.getenv("SPACE_HOST")
    space_id = os.getenv("SPACE_ID")
    if space_host:
        print(f"✅ SPACE_HOST: {space_host}")
    else:
        print("ℹ️  SPACE_HOST not found (running locally?).")
    if space_id:
        print(f"✅ SPACE_ID: {space_id}")
    else:
        print("ℹ️  SPACE_ID not found (running locally?).")
    print("-" * (60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface for GAIA Evaluation...")
    demo.launch(debug=True, share=False, server_name="0.0.0.0", server_port=7860, ssr_mode=False)