import os
import gradio as gr
import requests
import pandas as pd
from smolagents import CodeAgent, DuckDuckGoSearchTool, LiteLLMModel, VisitWebpageTool

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

# --- Agent Definition ---
class AgentArchitect:
    def __init__(self):
        # SECURE: Fetches the OpenAI API key from your Space Secrets
        openai_api_key = os.getenv("OPENAI_API_KEY")
        
        if not openai_api_key:
            print("CRITICAL: OPENAI_API_KEY is missing. Please add it to your Space Secrets!")

        # Bypassing Hugging Face billing completely.
        # We use gpt-4o-mini because it is highly capable at coding and very cost-effective.
        self.model = LiteLLMModel(
            model_id="gpt-4o-mini",
            api_key=openai_api_key
        )
        
        self.tools = [DuckDuckGoSearchTool(), VisitWebpageTool()]
        
        self.agent = CodeAgent(
            tools=self.tools,
            model=self.model,
            add_base_tools=True 
        )

    def __call__(self, question: str) -> str:
        try:
            # Enforce Exact Match scoring formatting
            prompt = (
                f"{question}\n\n"
                f"Instructions: Think step-by-step. Solve the problem using your tools. "
                f"Provide ONLY the final, concise answer."
            )
            result = self.agent.run(prompt)
            return str(result)
        except Exception as e:
            return f"Error: {e}"

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

    if not profile:
        return "Please Login to Hugging Face with the button.", None

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

    try:
        agent_instance = AgentArchitect()
        
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()

        results_log = []
        answers_payload = []
        
        for item in questions_data:
            task_id = item.get("task_id")
            question_text = item.get("question")
            
            # Agent logic with OpenAI's brain
            submitted_answer = agent_instance(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})

        agent_code_link = f"https://huggingface.co/spaces/{space_id}/tree/main"
        submission_data = {
            "username": username.strip(), 
            "agent_code": agent_code_link, 
            "answers": answers_payload
        }
        
        submit_response = requests.post(submit_url, json=submission_data, timeout=60)
        submit_response.raise_for_status()
        result_data = submit_response.json()
        
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Final Score: {result_data.get('score')}% \n"
            f"Message: {result_data.get('message')}"
        )
        return final_status, pd.DataFrame(results_log)
        
    except Exception as e:
        return f"Submission Failed: {e}", None

# --- Gradio UI ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🚀 Professional Agent Evaluator (OpenAI Edition)")
    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
    status_output = gr.Textbox(label="Leaderboard Status", lines=4)
    results_table = gr.DataFrame(label="Agent Reasoning Trace", wrap=True)

    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])

if __name__ == "__main__":
    demo.launch()