app.py

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

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

# --- Agent Definition ---
def build_agent():
    """Build and return the smolagents CodeAgent with Groq backend."""
    from smolagents import CodeAgent, LiteLLMModel, DuckDuckGoSearchTool, WikipediaSearchTool, VisitWebpageTool, tool

    @tool
    def download_task_file(task_id: str) -> str:
        """Download a file associated with a GAIA task and return its local path.
        Use this when a question mentions or implies there is an attached file.

        Args:
            task_id: The task ID whose file should be downloaded.

        Returns:
            The local file path where the file was saved, or an error message.
        """
        url = f"{DEFAULT_API_URL}/files/{task_id}"
        try:
            resp = requests.get(url, timeout=30)
            if resp.status_code == 404:
                return "No file found for this task."
            resp.raise_for_status()

            # Try to determine file extension from Content-Disposition or Content-Type
            content_disp = resp.headers.get("content-disposition", "")
            if "filename=" in content_disp:
                filename = content_disp.split("filename=")[-1].strip().strip('"')
            else:
                ct = resp.headers.get("content-type", "")
                ext_map = {
                    "image/png": ".png", "image/jpeg": ".jpg", "image/gif": ".gif",
                    "application/pdf": ".pdf", "text/plain": ".txt",
                    "text/csv": ".csv", "application/json": ".json",
                    "audio/mpeg": ".mp3", "audio/wav": ".wav",
                    "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet": ".xlsx",
                }
                ext = next((v for k, v in ext_map.items() if k in ct), ".bin")
                filename = f"task_{task_id}{ext}"

            path = f"/tmp/{filename}"
            with open(path, "wb") as f:
                f.write(resp.content)
            return path
        except Exception as e:
            return f"Error downloading file: {e}"

    openai_api_key = os.getenv("OPENAI_API_KEY")
    if not openai_api_key:
        raise ValueError("OPENAI_API_KEY environment variable not set. Add it as a Secret in your HF Space settings.")

    model = LiteLLMModel(
        model_id="openai/gpt-4o",
        api_key=openai_api_key,
        temperature=0.0,
    )

    agent = CodeAgent(
        tools=[
            DuckDuckGoSearchTool(),
            WikipediaSearchTool(),
            VisitWebpageTool(),
            download_task_file,
        ],
        model=model,
        additional_authorized_imports=[
            "requests", "json", "re", "math", "datetime",
            "csv", "io", "os", "pathlib",
            "PIL", "PIL.Image",
            "pandas", "openpyxl",
        ],
        max_steps=15,
    )

    return agent


class BasicAgent:
    def __init__(self):
        print("Initializing agent (loading smolagents + Groq)...")
        self._agent = build_agent()
        print("Agent ready.")

    def __call__(self, question: str) -> str:
        print(f"Question: {question[:100]}...")
        system_note = (
            "You are a precise research assistant solving GAIA benchmark questions. "
            "Your answers are graded by EXACT STRING MATCH, so formatting is critical.\n\n"
            "Rules:\n"
            "- Reply with ONLY the answer, nothing else. No explanation, no 'FINAL ANSWER:' prefix.\n"
            "- Numbers: use digits (e.g. 42, 3.14). No units unless the question asks for them.\n"
            "- Lists: comma-separated on one line unless the question specifies otherwise.\n"
            "- Names/strings: exact spelling, match the question's expected format.\n"
            "- If a file is attached to the question, use the download_task_file tool first.\n"
            "- Search the web and visit pages to verify facts before answering.\n"
            "- Think step by step, but output ONLY the final answer."
        )
        full_prompt = f"{system_note}\n\nQuestion: {question}"
        try:
            result = self._agent.run(full_prompt)
            answer = str(result).strip()
            print(f"Answer: {answer[:100]}")
            return answer
        except Exception as e:
            print(f"Agent error: {e}")
            return f"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.
    """
    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 = BasicAgent()
    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(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:
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # 3. Run 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:
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Submit
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    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.")
        return final_status, pd.DataFrame(results_log)
    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 Exception:
            error_detail += f" Response: {e.response.text[:500]}"
        print(f"Submission Failed: {error_detail}")
        return f"Submission Failed: {error_detail}", pd.DataFrame(results_log)
    except Exception as e:
        print(f"Unexpected error during submission: {e}")
        return f"An unexpected error occurred during submission: {e}", pd.DataFrame(results_log)


# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# GAIA Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Make sure `GROQ_API_KEY` is set as a Secret in your HF Space settings.
        2. Log in with your Hugging Face account below.
        3. Click **Run Evaluation & Submit All Answers** — the agent will answer all 20 GAIA questions and submit.

        ---
        *Note: This can take several minutes as the agent processes each question.*
        """
    )

    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_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
    else:
        print("ℹ️  SPACE_HOST not found (running locally?).")

    if space_id_startup:
        print(f"✅ SPACE_ID found: {space_id_startup}")
    else:
        print("ℹ️  SPACE_ID not found (running locally?).")

    print("-" * (60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface...")
    demo.launch(debug=True, share=False)