app.py

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

from smolagents import CodeAgent, DuckDuckGoSearchTool
from smolagents.models import OpenAIServerModel

SYSTEM_PROMPT = """You are a general AI assistant. Reason step by step, then finish with:

FINAL ANSWER: [YOUR FINAL ANSWER]

Answer rules:
- Numbers: no commas, units, or extra words. Just digits.
- Strings: lowercase, no articles or abbreviations.
- Lists: comma-separated, following the above.

Examples:
Q: What is 12 + 7?
A: 12 + 7 = 19
FINAL ANSWER: 19

Q: Name three European capital cities.
A: They are Amsterdam, Berlin, and Rome.
FINAL ANSWER: amsterdam, berlin, rome

Q: What is the square root of 81?
A: \u221a81 = 9
FINAL ANSWER: 9

Now answer the following:
"""

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

class PatchedOpenAIServerModel(OpenAIServerModel):
    def generate(self, messages, stop_sequences=None, **kwargs):
        if isinstance(messages, list):
            if not any(m["role"] == "system" for m in messages):
                messages = [{"role": "system", "content": SYSTEM_PROMPT}] + messages
        else:
            raise TypeError("Expected 'messages' to be a list of message dicts")

        return super().generate(messages=messages, stop_sequences=stop_sequences, **kwargs)

class MyAgent:
    def __init__(self):
        self.model = PatchedOpenAIServerModel(model_id="gpt-4")
        self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=self.model)

    def __call__(self, question: str) -> str:
        return self.agent.run(question)

def extract_final_answer(output: str) -> str:
    if "FINAL ANSWER:" in output:
        return output.split("FINAL ANSWER:")[-1].strip().rstrip('.')
    return output.strip()

def sanitize_answer(ans: str) -> str:
    ans = re.sub(r'\$|%|,', '', ans)
    ans = ans.strip().rstrip('.')
    return ans

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

    if profile:
        username = profile.username.strip()
        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")
        question_text = item.get("question")
        if not task_id or question_text is None:
            continue
        try:
            raw_output = agent(question_text)
            extracted = extract_final_answer(raw_output)
            submitted_answer = sanitize_answer(extracted)
            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:
            error_msg = f"AGENT ERROR: {e}"
            results_log.append({"Task ID": task_id, "Question": question_text, "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, "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)

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)