"""
GAIA Agent - Main Gradio Interface
Enhanced agent with question-type specific strategies
"""
import os
import re
import json
import time
import gradio as gr
import requests
import pandas as pd

# Importar módulos locales
from config import DEFAULT_API_URL
from agent import EnhancedAgent
from utils import detect_question_type, download_file_for_task


# ============================================================================
# FUNCIONES PRINCIPALES DE GRADIO
# ============================================================================

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """Run the agent on all questions and submit the results."""
    space_id = os.getenv("SPACE_ID")
    
    if profile:
        username = f"{profile.username}"
    else:
        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"
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    # Load questions
    try:
        print("📥 Loading questions from server...")
        response = requests.get(questions_url, timeout=15)
        questions_data = response.json()
        print(f"   ✓ {len(questions_data)} questions loaded")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # Create agent (reusable)
    print("\n🤖 Creating agent...")
    agent = EnhancedAgent()

    results_log = []
    answers_payload = []
    diagnostics = []
    
    print(f"\n{'='*80}")
    print(f"🚀 Starting processing of {len(questions_data)} questions")
    print(f"{'='*80}\n")
    
    for i, item in enumerate(questions_data):
        task_id = item.get("task_id")
        question_text = item.get("question", "")
        file_name = item.get("file_name", "")
        
        if not task_id:
            continue
        
        print(f"\n{'='*80}")
        print(f"[{i+1}/{len(questions_data)}] Task: {task_id}")
        print(f"{'='*80}")
        print(f"❓ Question: {question_text[:150]}...")
        
        # Detect question type
        question_type = detect_question_type(question_text, file_name)
        print(f"🔍 Detected type: {question_type}")
        
        if file_name:
            print(f"📎 Expected file: {file_name}")
        
        # Download file if exists
        local_file = download_file_for_task(task_id)
        
        # Show URLs found in the question
        url_pattern = r"https?://[\w\-\./?&=%#]+"
        found_urls = re.findall(url_pattern, question_text)
        for url in found_urls:
            print(f"   🔗 URL found: {url}")
        
        # Execute agent
        start_time = time.time()
        print(f"⚙️  Processing with strategy '{question_type}'...")
        
        try:
            submitted_answer, execution_logs = agent.solve(
                question_text, 
                local_file, 
                question_type
            )
            
            # Limpieza de seguridad
            if len(submitted_answer) > 200:
                submitted_answer = submitted_answer[:200]
            
        except Exception as e:
            print(f"❌ Error: {e}")
            submitted_answer = "Error"
            execution_logs = str(e)
        
        elapsed = time.time() - start_time
        
        print(f"\n✅ Answer: {submitted_answer}")
        print(f"⏱️  Time: {elapsed:.1f}s")
        
        # Save results
        answers_payload.append({
            "task_id": task_id, 
            "submitted_answer": submitted_answer
        })
        
        results_log.append({
            "Task ID": task_id,
            "Index": i,
            "Type": question_type,
            "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
            "File": file_name or "N/A",
            "Answer": submitted_answer,
            "Time (s)": round(elapsed, 1)
        })
        
        diagnostics.append({
            "index": i,
            "task_id": task_id,
            "question_type": question_type,
            "question": question_text,
            "file_name": file_name,
            "answer": submitted_answer,
            "elapsed_seconds": round(elapsed, 1)
        })
        
        # Clean up temporary file
        if local_file and os.path.exists(local_file):
            try:
                os.remove(local_file)
            except:
                pass

    # Save diagnostics
    try:
        ts = time.strftime("%Y%m%d_%H%M%S")
        diag_path = f"diagnostics_{ts}.json"
        with open(diag_path, "w", encoding="utf-8") as f:
            json.dump(diagnostics, f, ensure_ascii=False, indent=2)
        print(f"\n📊 Diagnostics saved: {diag_path}")
    except Exception as e:
        print(f"⚠️ Error saving diagnostics: {e}")

    # Submit results
    print(f"\n{'='*80}")
    print("📤 Submitting answers to server...")
    
    submission_data = {
        "username": username.strip(), 
        "agent_code": agent_code, 
        "answers": answers_payload
    }
    
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        try:
            result = response.json()
            score = result.get('score', 'N/A')
            correct = result.get('correct_count', '?')
            total = result.get('total_count', len(questions_data))
            
            status_msg = f"""✅ Submission Successful!
            
📊 Score: {score}%
✓ Correct: {correct}/{total}
👤 Username: {username}
"""
            print(status_msg)
            return status_msg, pd.DataFrame(results_log)
            
        except Exception:
            return f"Submission Failed (Server Error): {response.text}", pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)


# ============================================================================
# INTERFAZ GRADIO
# ============================================================================

with gr.Blocks() as demo:
    gr.Markdown("""
    # 🤖 GAIA Agent - Optimized for Files, YouTube and Logic
    
    This agent uses question-specific strategies:
    - 📊 **Excel/CSV Files**: Reads and analyzes data with pandas
    - 🎬 **YouTube**: Searches for transcripts and online discussions
    - 🖼️ **Images**: Searches for information on the web
    - 🎵 **Audio**: Searches for transcripts online
    - 📝 **Wikipedia**: Navigates and extracts information
    - 🔢 **Counting**: Lists items and counts programmatically
    - 🔄 **Text Manipulation**: Handles reversed text, opposites, etc.
    """)
    
    gr.LoginButton()
    
    with gr.Row():
        run_button = gr.Button("🚀 Run Evaluation & Submit All Answers", variant="primary")
    
    status_output = gr.Textbox(label="📋 Status", lines=6, interactive=False)
    
    results_table = gr.DataFrame(
        label="📊 Detailed Results", 
        wrap=True,
        headers=["Task ID", "Index", "Type", "Question", "File", "Answer", "Time (s)"]
    )
    
    run_button.click(
        fn=run_and_submit_all, 
        outputs=[status_output, results_table]
    )


if __name__ == "__main__":
    print("🚀 Starting GAIA Agent...")
    demo.launch()