""" Working Multi-LLM Agent Evaluation Runner"""
import os
import gradio as gr
import requests
import pandas as pd
from langchain_core.messages import HumanMessage

# Import from veryfinal.py
from veryfinal import UnifiedAgnoEnhancedSystem

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

# --- Working Agent Definition ---
class WorkingMultiLLMAgent:
    """A working multi-LLM agent that actually answers questions"""
    def __init__(self):
        print("Working Multi-LLM Agent initialized.")
        try:
            self.system = UnifiedAgnoEnhancedSystem()
            print("✅ Working system built successfully.")
        except Exception as e:
            print(f"❌ Error building system: {e}")
            self.system = None

    def __call__(self, question: str) -> str:
        print(f"Processing: {question[:100]}...")
        
        if self.system is None:
            return "Error: System not initialized"
        
        try:
            answer = self.system.process_query(question)
            
            # Validation
            if not answer or answer == question or len(answer.strip()) == 0:
                return "Information not available"
            
            return answer.strip()
                
        except Exception as e:
            return f"Error: {str(e)}"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """Run evaluation with working agent"""
    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"

    # 1. Instantiate Working Agent
    try:
        agent = WorkingMultiLLMAgent()
        if agent.system is None:
            return "Error: Failed to initialize working agent", None
    except Exception as e:
        return f"Error initializing agent: {e}", None
    
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "No space ID"

    # 2. Fetch Questions
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            return "No questions fetched", None
        print(f"✅ Fetched {len(questions_data)} questions")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # 3. Process Questions
    results_log = []
    answers_payload = []
    
    for i, item in enumerate(questions_data):
        task_id = item.get("task_id")
        question_text = item.get("question")
        
        if not task_id or question_text is None:
            continue
            
        print(f"Processing {i+1}/{len(questions_data)}: {task_id}")
        
        try:
            answer = agent(question_text)
            
            # Prevent question repetition
            if answer == question_text or answer.startswith(question_text):
                answer = "Information not available"
            
            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
            results_log.append({
                "Task ID": task_id,
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": answer[:200] + "..." if len(answer) > 200 else answer
            })
        except Exception as e:
            error_msg = f"ERROR: {e}"
            answers_payload.append({"task_id": task_id, "submitted_answer": error_msg})
            results_log.append({
                "Task ID": task_id,
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": error_msg
            })

    if not answers_payload:
        return "No answers generated", pd.DataFrame(results_log)

    # 4. Submit Results
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    
    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"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', 'Success')}"
        )
        
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"❌ Submission Failed: {e}", pd.DataFrame(results_log)

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Working Multi-LLM Agent System")
    gr.Markdown(
        """
        **✅ This is a WORKING system that will actually answer questions!**
        
        **Features:**
        - **Groq Llama-3 70B**: High-quality responses
        - **Smart Routing**: Math, search, wiki, and general queries
        - **Web Search**: Tavily integration for current information
        - **Wikipedia**: Encyclopedic knowledge access
        - **Robust Error Handling**: Fallbacks and validation
        
        **Instructions:**
        1. Log in with your Hugging Face account
        2. Click 'Run Evaluation & Submit All Answers'
        3. Wait for processing to complete
        4. View your results and score
        
        **Requirements:**
        - GROQ_API_KEY in your environment variables
        - TAVILY_API_KEY (optional, for web search)
        """
    )

    gr.LoginButton()
    run_button = gr.Button("🚀 Run Evaluation & Submit All Answers", variant="primary")
    status_output = gr.Textbox(label="Status", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Results", wrap=True)

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

if __name__ == "__main__":
    print("🚀 Starting Working Multi-LLM Agent System")
    demo.launch(debug=True, share=False)