app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from openai import OpenAI

client = OpenAI(api_key="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")

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

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self):
        self.api_key = os.getenv("OPENAI_API_KEY")
        if not self.api_key:
            raise ValueError("OpenAI API key not found. Please set OPENAI_API_KEY as environment variable.")

        # Optional: Log OpenAI constructor arguments
        print("🔍 OpenAI init params:", list(inspect.signature(OpenAI.__init__).parameters.keys()))

        # ✅ Ensure only valid args passed
        self.client = OpenAI(api_key=self.api_key)
        print("✅ OpenAI Agent initialized (v1+ syntax).")

    def __call__(self, question: str) -> str:
        print(f"❓ Question received: {question[:50]}...")
        try:
            response = self.client.chat.completions.create(
                model="gpt-3.5-turbo",
                messages=[
                    {"role": "system", "content": "You are a helpful assistant that answers GAIA benchmark questions."},
                    {"role": "user", "content": question}
                ],
                max_tokens=300,
                temperature=0.7
            )
            answer = response.choices[0].message.content.strip()
            print(f"✅ Answer: {answer}")
            return answer
        except Exception as e:
            print(f"❌ Error calling OpenAI API: {e}")
            return f"ERROR: {e}"


def run_and_submit_all(profile: gr.OAuthProfile | None):
    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"

    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)

    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 for questions: {e}", None
    except Exception as e:
        print(f"Unexpected error fetching questions: {e}")
        return f"Unexpected 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")
        question_text = item.get("question")
        if not task_id or question_text is None:
            print(f"Skipping invalid item: {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)

    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:
        try:
            error_detail = f"{e.response.status_code} - {e.response.json().get('detail', e.response.text)}"
        except Exception:
            error_detail = f"{e.response.status_code} - {e.response.text}"
        return f"Submission Failed: {error_detail}", pd.DataFrame(results_log)
    except requests.exceptions.Timeout:
        return "Submission Failed: The request timed out.", pd.DataFrame(results_log)
    except requests.exceptions.RequestException as e:
        return f"Submission Failed: Network error - {e}", pd.DataFrame(results_log)
    except Exception as e:
        return f"Unexpected error during submission: {e}", pd.DataFrame(results_log)


# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
        **Instructions:**
        1. Clone this space and modify the code to define your own agent.
        2. Log in with your Hugging Face account.
        3. Click 'Run Evaluation & Submit All Answers' to start.
    """)

    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}")
        print(f"Runtime URL: https://{space_host}.hf.space")
    else:
        print("ℹ️ SPACE_HOST not found.")

    if space_id:
        print(f"✅ SPACE_ID: {space_id}")
        print(f"Repo: https://huggingface.co/spaces/{space_id}/tree/main")
    else:
        print("ℹ️ SPACE_ID not found.")

    print("-" * 70)
    print("Launching Gradio Interface...")
    demo.launch(debug=True, share=False)