app.py

import os
import requests
from smolagents import Agent
from audio_transcriber import AudioTranscriptionTool
from image_analyzer import ImageAnalysisTool
from wikipedia_searcher import WikipediaSearcher
from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

# Tools
audio_tool = AudioTranscriptionTool()
image_tool = ImageAnalysisTool()
wiki_tool = WikipediaSearcher()

# Static system prompt
def build_prompt(question: str) -> str:
    return f"""You are an agent solving the GAIA benchmark and you are required to provide exact answers.
Rules to follow:
1. Return only the exact requested answer: no explanation and no reasoning.
2. For yes/no questions, return exactly "Yes" or "No".
3. For dates, use the exact format requested.
4. For numbers, use the exact number, no other format.
5. For names, use the exact name as found in sources.
6. If the question has an associated file, download the file first using the task ID.
Examples of good responses:
- "42"
- "Yes"
- "October 5, 2001"
- "Buenos Aires"
Never include phrases like "the answer is..." or "Based on my research".
Only return the exact answer.

QUESTION:
{question}
"""

# Main agent function
class GAIAAgent:
    def __init__(self):
        self.llm = OpenAI(model="gpt-4-turbo", temperature=0)

    def __call__(self, task: dict) -> str:
        question = task.get("question", "")
        attachment_url = task.get("attachment", "")

        # Handle audio
        if attachment_url.endswith((".mp3", ".wav")):
            transcript = audio_tool.forward(attachment_url)
            question += f"\n\nTranscript of attached audio:\n{transcript}"

        # Handle image
        elif attachment_url.endswith((".jpg", ".jpeg", ".png")):
            return image_tool.forward(attachment_url, question)

        # Handle Python file
        elif attachment_url.endswith(".py"):
            try:
                code_text = requests.get(attachment_url).text
                question += f"\n\nAttached Python file content:\n{code_text}"
            except Exception as e:
                return f"Error retrieving Python file: {e}"

        # Wikipedia queries (if task type or instruction indicates)
        if "wikipedia" in question.lower():
            return wiki_tool.search(question)

        # Build prompt
        prompt = build_prompt(question)

        # Run model
        response = self.llm.chat.completions.create(
            messages=[{"role": "system", "content": prompt}],
            model="gpt-4-turbo"
        )

        return response.choices[0].message.content.strip()


def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = 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"

    try:
        agent = MyAgent()
    except Exception as e:
        print(f"Error initializing 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}")

    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

    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    for item in questions_data:
        task_id = item.get("task_id")
        if not task_id:
            continue
        try:
            submitted_answer = agent(item)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({
                "Task ID": task_id,
                "Question": item.get("question", ""),
                "Submitted Answer": submitted_answer
            })
        except Exception as e:
            error_msg = f"AGENT ERROR: {e}"
            results_log.append({
                "Task ID": task_id,
                "Question": item.get("question", ""),
                "Submitted Answer": error_msg
            })

    if not answers_payload:
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    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.')}"
        )
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        try:
            detail = e.response.json().get("detail", e.response.text)
        except Exception:
            detail = e.response.text[:500]
        return f"Submission Failed: {detail}", pd.DataFrame(results_log)
    except requests.exceptions.Timeout:
        return "Submission Failed: The request timed out.", pd.DataFrame(results_log)
    except Exception as e:
        return f"An unexpected error occurred during submission: {e}", pd.DataFrame(results_log)

# Gradio UI setup
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
        **Instructions:**
        1. Clone this space, modify code to define your agent's logic, tools, and packages.
        2. Log in to your Hugging Face account using the button below.
        3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see your score.
        **Note:** Submitting can take some time.
    """)

    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 found: {space_host}")
        print(f"   Runtime URL should be: https://{space_host}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id:
        print(f"✅ SPACE_ID found: {space_id}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?).")

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