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

# Use LiteLLMModel to natively route through Gemini's endpoint matrix
from smolagents import CodeAgent, LiteLLMModel, DuckDuckGoSearchTool

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


def _clean_answer(text: str) -> str:
    text = text.strip()
    prefixes = [
        "FINAL ANSWER:",
        "Final Answer:",
        "Answer:",
        "ANSWER:",
    ]
    for prefix in prefixes:
        if text.startswith(prefix):
            text = text[len(prefix) :].strip()
            break
    return text


class BasicAgent:
    """Automated Agent running on Google Gemini using Space repository secrets."""

    def __init__(self, gemini_key: str):
        print("Initializing smolagents CodeAgent via Google Gemini endpoint...")
        
        if not gemini_key:
            raise ValueError("GEMINI_API_KEY environment secret is missing from Space Settings.")

        # Connect straight to Gemini without routing through Hugging Face's credit pool
        self.model = LiteLLMModel(
            model_id="gemini/gemini-2.5-flash",
            api_key=gemini_key
        )
        
        # Equip with Web Search capabilities for GAIA questions
        self.search_tool = DuckDuckGoSearchTool()
        
        # Initialize CodeAgent engine
        self.agent = CodeAgent(
            model=self.model,
            tools=[self.search_tool],
            additional_authorized_imports=["math", "json", "re", "collections", "datetime", "urllib"]
        )

    def __call__(self, question: str) -> str:
        print(f"Agent processing question (first 50 chars): {question[:50]}...")
        
        gaia_prompt = (
            f"You are an elite agent solving a precise GAIA benchmark question.\n"
            f"Question: {question}\n\n"
            f"Execute any python reasoning code or search queries required to find the exact answer. "
            f"Provide your final answer clearly and concisely at the very end."
        )
        
        try:
            output = self.agent.run(gaia_prompt)
            answer = _clean_answer(str(output))
        except Exception as e:
            print(f"Internal execution pipeline error: {e}")
            answer = f"ERROR: {e}"

        print(f"Agent returning answer: {answer}")
        return answer


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """Fetch all benchmark questions, pull key from background secrets, and submit answers."""

    space_id = os.getenv("SPACE_ID")
    gemini_key = os.getenv("GEMINI_API_KEY")

    if not gemini_key:
        return "Error: GEMINI_API_KEY environment secret not found. Please add it to your Space Settings tab.", None

    if profile:
        username = f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please log in to Hugging Face with the button below.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    try:
        agent = BasicAgent(gemini_key=gemini_key)
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {str(e)}", None

    if space_id:
        agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    else:
        agent_code = "https://huggingface.co/spaces/<your-space>/tree/main"

    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
    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:
            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:
            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 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.')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)


with gr.Blocks() as demo:
    gr.Markdown("# Fully Automated GAIA Agent Runner (Gemini Mode)")
    gr.Markdown(
        """
        **Instructions:**
        1. Ensure your `GEMINI_API_KEY` is added to your Space's Settings tab.
        2. Click the Hugging Face Login Button to authorize your identity.
        3. Click **Run Evaluation & Submit All Answers** to process automatically.
        """
    )

    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__":
    # Force explicit server network binding to prevent Space hanging on 'Starting'
    demo.launch(server_name="0.0.0.0", server_port=7860)