app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from smolagents import CodeAgent, InferenceClientModel
from smolagents.tools.serper import SerperSearchTool  # make sure this path is correct


# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
serper_api_key = os.getenv("SERPER_API_KEY")

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self):

        serper_api_key = os.getenv("SERPER_API_KEY")
        if not serper_api_key:
            raise ValueError("Missing SERPER_API_KEY in environment variables.")

        search_tool = SerperSearchTool(api_key=serper_api_key)
        model = InferenceClientModel()

        self.agent = CodeAgent(
            model=model,
            tools=[search_tool],
        )

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        try:
            answer = self.agent.run(question)
            print(f"Agent returned answer: {answer}")
            return answer
        except Exception as e:
            print(f"Error running agent: {e}")
            return f"AGENT 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"

    # 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)

    # 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}")
        print(f"Response text: {response.text[:500]}")
        return f"Error decoding server response: {e}", None
    except Exception as e:
        print(f"Unexpected error: {e}")
        return f"Unexpected error fetching questions: {e}", None

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

    # 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)

    # Submit Answers
    print(f"Submitting 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.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"HTTP {e.response.status_code}: "
        try:
            error_json = e.response.json()
            error_detail += f"{error_json.get('detail', e.response.text)}"
        except:
            error_detail += f"{e.response.text[:500]}"
        print(error_detail)
        return f"Submission Failed: {error_detail}", pd.DataFrame(results_log)
    except requests.exceptions.Timeout:
        return "Submission Failed: 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)


# --- Build Gradio Interface using Blocks ---
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 agent's logic, tools, and dependencies.
        2. Log in using the button below. Your Hugging Face username is required for submission.
        3. Click 'Run Evaluation & Submit All Answers' to test your agent and get a score.
        ---
        **Note:** The submission process may take time. You are encouraged to optimize your implementation.
        """
    )

    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]
    )


# --- Entry Point ---
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 (running locally?).")

    if space_id:
        print(f"✅ SPACE_ID: {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 not found (running locally?).")

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