"""
OCR Arena - Main Application
A Gradio web application for comparing OCR results from different AI models.
"""

import gradio as gr
import logging
import os
import datetime
from dotenv import load_dotenv
from storage import upload_file_to_bucket
from db import add_vote, get_all_votes, calculate_elo_ratings_from_votes
from ocr_models import process_model_ocr, initialize_gemini, initialize_mistral, initialize_openai
from ui_helpers import (
    get_model_display_name, select_random_models, format_votes_table, 
    format_elo_leaderboard
)

# Load environment variables
load_dotenv()

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
logger = logging.getLogger(__name__)

# Initialize API keys and models
initialize_gemini()
initialize_mistral()
initialize_openai()

# Get Supabase credentials
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY = os.getenv("SUPABASE_KEY")

# Global variables to store current OCR results and image URL
current_gemini_output = ""
current_mistral_output = ""
current_openai_output = ""
current_gpt5_output = ""
current_gpt5_output = ""
current_image_url = ""
current_voted_users = set()  # Track users who have already voted
current_model_a = ""  # Store which model was selected as model A
current_model_b = ""  # Store which model was selected as model B


def get_default_username(profile: gr.OAuthProfile | None) -> str:
    """Returns the username if the user is logged in, or an empty string if not logged in."""
    if profile is None:
        return ""
    return profile.username

def get_current_username(profile_or_username) -> str:
    """Returns the username from login or "Anonymous" if not logged in."""
    # Check if profile_or_username is a profile object with username attribute
    if hasattr(profile_or_username, 'username') and profile_or_username.username:
        return profile_or_username.username
    # Check if profile_or_username is a direct username string
    elif isinstance(profile_or_username, str) and profile_or_username.strip():
        # Extract username from "Logout (username)" format
        if profile_or_username.startswith("Logout (") and profile_or_username.endswith(")"):
            return profile_or_username[8:-1]  # Remove "Logout (" and ")"
        # If it's just a username string, return it
        elif profile_or_username != "Sign in with Hugging Face":
            return profile_or_username.strip()
    
    # Return "Anonymous" if no valid username found
    return "Anonymous"

def process_image(image):
    """Process uploaded image and select random models for comparison."""
    global current_gemini_output, current_mistral_output, current_openai_output, current_image_url, current_voted_users, current_model_a, current_model_b
    
    if image is None:
        return (
            "Please upload an image.", 
            "Please upload an image.",
            gr.update(visible=False),  # Hide vote buttons
            gr.update(visible=False)   # Hide vote buttons
        )
    
    # Reset voted users for new image
    current_voted_users.clear()
    
    # Select two random models
    model_a, model_b = select_random_models()
    current_model_a = model_a
    current_model_b = model_b
    
    logger.info(f"🎲 Randomly selected two models for comparison")
    
    try:
        # Save the PIL image to a temporary file
        temp_filename = f"temp_image_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.png"
        image.save(temp_filename)
        
        # Upload the temporary file to Supabase storage
        logger.info(f"📤 Uploading image to Supabase storage: {temp_filename}")
        upload_result = upload_file_to_bucket(
            file_path=temp_filename,
            bucket_name="images",
            storage_path=f"ocr_images/{temp_filename}",
            file_options={"cache-control": "3600", "upsert": "false"}
        )
        
        if upload_result["success"]:
            logger.info(f"✅ Image uploaded successfully: {upload_result['storage_path']}")
            logger.info(f"🔗 Public URL: {upload_result['public_url']}")
            # Store the image URL for voting
            current_image_url = upload_result.get('public_url') or f"{SUPABASE_URL}/storage/v1/object/public/images/ocr_images/{temp_filename}"
        else:
            logger.error(f"❌ Image upload failed: {upload_result['error']}")
            current_image_url = ""
        
        # Clean up temporary file
        try:
            os.remove(temp_filename)
            logger.info(f"🗑️ Cleaned up temporary file: {temp_filename}")
        except Exception as e:
            logger.warning(f"⚠️ Could not remove temporary file {temp_filename}: {e}")
        
        # Return initial state - OCR processing will happen via separate button clicks
        return (
            "Please click 'Run OCR' to start processing.", 
            "Please click 'Run OCR' to start processing.", 
            gr.update(visible=False),  # Hide vote buttons initially
            gr.update(visible=False)   # Hide vote buttons initially
        )
        
    except Exception as e:
        logger.error(f"Error processing image: {e}")
        return (
            f"Error processing image: {e}", 
            f"Error processing image: {e}",
            gr.update(visible=False),  # Hide vote buttons
            gr.update(visible=False)   # Hide vote buttons
        )

def check_ocr_completion(model_a_output, model_b_output):
    """Check if both OCR results are ready and update UI accordingly."""
    global current_gemini_output, current_mistral_output, current_openai_output, current_gpt5_output, current_model_a, current_model_b

    # Check if both results are complete (not processing messages)
    model_a_ready = (model_a_output and
                    model_a_output != "Please upload an image." and
                    model_a_output != "Processing OCR..." and
                    model_a_output != "Please click 'Run OCR' to start processing." and
                    not model_a_output.startswith("OCR error:"))

    model_b_ready = (model_b_output and
                     model_b_output != "Please upload an image." and
                     model_b_output != "Processing OCR..." and
                     model_b_output != "Please click 'Run OCR' to start processing." and
                     not model_b_output.startswith("OCR error:"))

    # Update global variables with actual results based on which models were selected
    if model_a_ready:
        if current_model_a == "gemini":
            current_gemini_output = model_a_output
        elif current_model_a == "mistral":
            current_mistral_output = model_a_output
        elif current_model_a == "openai":
            current_openai_output = model_a_output
        elif current_model_a == "gpt5":
            current_gpt5_output = model_a_output
    
    if model_b_ready:
        if current_model_b == "gemini":
            current_gemini_output = model_b_output
        elif current_model_b == "mistral":
            current_mistral_output = model_b_output
        elif current_model_b == "openai":
            current_openai_output = model_b_output
        elif current_model_b == "gpt5":
            current_gpt5_output = model_b_output

    # Show vote buttons only when both are ready
    if model_a_ready and model_b_ready:
        return (
            gr.update(visible=True),  # Show Model A vote button
            gr.update(visible=True)   # Show Model B vote button
        )
    else:
        return (
            gr.update(visible=False),  # Hide vote buttons
            gr.update(visible=False)   # Hide vote buttons
        )

def load_vote_data():
    """Load and format vote data for display."""
    try:
        # Get all votes
        votes = get_all_votes()
        votes_table_html = format_votes_table(votes)

        return votes_table_html

    except Exception as e:
        logger.error(f"Error loading vote data: {e}")
        error_html = f"<p style='color: red;'>Error loading data: {e}</p>"
        return error_html

def load_elo_leaderboard():
    """Load and format ELO leaderboard data."""
    try:
        # Get all votes
        votes = get_all_votes()
        
        # Calculate ELO ratings
        elo_ratings = calculate_elo_ratings_from_votes(votes)
        
        # Calculate vote counts for each model
        vote_counts = {
            "gemini": 0,
            "mistral": 0,
            "openai": 0,
            "gpt5": 0
        }
        
        for vote in votes:
            model_a = vote.get('model_a')
            model_b = vote.get('model_b')
            vote_choice = vote.get('vote')
            
            if vote_choice == 'model_a' and model_a in vote_counts:
                vote_counts[model_a] += 1
            elif vote_choice == 'model_b' and model_b in vote_counts:
                vote_counts[model_b] += 1
        
        # Format leaderboard with vote counts
        leaderboard_html = format_elo_leaderboard(elo_ratings, vote_counts)
        
        return leaderboard_html

    except Exception as e:
        logger.error(f"Error loading ELO leaderboard: {e}")
        error_html = f"<p style='color: red;'>Error loading ELO leaderboard: {e}</p>"
        return error_html

# Create the Gradio interface
with gr.Blocks(title="OCR Comparison", css="""
    .output-box {
        border: 2px solid #e0e0e0;
        border-radius: 8px;
        padding: 15px;
        margin: 10px 0;
        background-color: #f9f9f9;
        min-height: 200px;
    }
    .output-box:hover {
        border-color: #007bff;
        box-shadow: 0 2px 8px rgba(0,123,255,0.1);
    }
    .vote-table {
        border-collapse: collapse;
        width: 100%;
        margin: 10px 0;
        min-width: 800px;
    }
    .vote-table th, .vote-table td {
        border: 1px solid #ddd;
        padding: 6px;
        text-align: left;
        vertical-align: top;
    }
    .vote-table th {
        background-color: #f2f2f2;
        font-weight: bold;
        position: sticky;
        top: 0;
        z-index: 10;
    }
    .vote-table tr:nth-child(even) {
        background-color: #f9f9f9;
    }
    .vote-table tr:hover {
        background-color: #f5f5f5;
    }
    .vote-table img {
        transition: transform 0.2s ease;
        max-width: 100%;
        height: auto;
    }
    .vote-table img:hover {
        transform: scale(1.1);
        box-shadow: 0 4px 8px rgba(0,0,0,0.2);
    }
""") as demo:

    with gr.Tabs():
        # Arena Tab (default)
        with gr.Tab("⚔️ Arena", id=0):
            gr.Markdown("# ⚔️ OCR Arena: Random Model Selection")
            gr.Markdown("Upload an image to compare two randomly selected OCR models.")

            # Authentication section (optional)
            with gr.Row():
                with gr.Column(scale=3):
                    username_display = gr.Textbox(
                        label="Current User",
                        placeholder="Login with Hugging Face to vote (optional) - Anonymous users welcome!",
                        interactive=False,
                        show_label=False
                    )
                with gr.Column(scale=1):
                    login_button = gr.LoginButton()

            with gr.Row():
                with gr.Column():
                    gemini_vote_btn = gr.Button("A is better", variant="primary", size="sm", visible=False)
                    gemini_output = gr.Markdown(label="Model A Output", elem_classes=["output-box"])

                image_input = gr.Image(type="pil", label="Upload or Paste Image")

                with gr.Column():
                    mistral_vote_btn = gr.Button("B is better", variant="primary", size="sm", visible=False)
                    mistral_output = gr.Markdown(label="Model B Output", elem_classes=["output-box"])



            with gr.Row():
                process_btn = gr.Button("🔍 Run OCR", variant="primary")

        # Data Tab
        with gr.Tab("📊 Data", id=1):
            gr.Markdown("# 📊 Vote Data")
            gr.Markdown("View all votes from the OCR Arena")

            with gr.Row():
                refresh_btn = gr.Button("🔄 Refresh Data", variant="secondary")

            with gr.Row():
                votes_table = gr.HTML(
                    value="<p>Loading vote data...</p>",
                    label="📋 All Votes (Latest First)"
                )

        # Leaderboard Tab
        with gr.Tab("🏆 Leaderboard", id=2):
            gr.Markdown("# 🏆 ELO Leaderboard")
            gr.Markdown("See how the models rank based on their ELO ratings from head-to-head comparisons.")

            with gr.Row():
                refresh_leaderboard_btn = gr.Button("🔄 Refresh Leaderboard", variant="secondary")

            with gr.Row():
                leaderboard_display = gr.HTML(
                    value="<p>Loading ELO leaderboard...</p>",
                    label="🏆 Model Rankings"
                )

    # Vote functions
    def vote_model_a(profile_or_username):
        global current_gemini_output, current_mistral_output, current_openai_output, current_gpt5_output, current_image_url, current_voted_users, current_model_a, current_model_b
        
        # Get current username
        username = get_current_username(profile_or_username)
        
        if not username:
            username = "Anonymous"
        
        # Check if user has already voted
        if username in current_voted_users:
            gr.Info(f"You have already voted for this image, {username}!")
            return
        
        try:
            # Use the stored image URL from the upload
            image_url = current_image_url if current_image_url else "no_image"
            
            # Add vote to database
            logger.info(f"📊 Adding Model A vote for user: {username}")
            def output_for(model: str) -> str:
                return {
                    "gemini": current_gemini_output,
                    "mistral": current_mistral_output,
                    "openai": current_openai_output,
                    "gpt5": current_gpt5_output,
                }.get(model, "")

            add_vote(
                username=username,
                model_a=current_model_a,
                model_b=current_model_b,
                model_a_output=output_for(current_model_a),
                model_b_output=output_for(current_model_b),
                vote="model_a",
                image_url=image_url
            )
            
            # Mark user as voted
            current_voted_users.add(username)
            
            model_a_name = get_model_display_name(current_model_a)
            model_b_name = get_model_display_name(current_model_b)
            info_message = (
                f"<p>You voted for <strong style='color:green;'>{model_a_name}</strong>.</p>"
                f"<p><span style='color:green;'>{model_a_name}</span> - "
                f"<span style='color:blue;'>{model_b_name}</span></p>"
            )
            gr.Info(info_message)
            
        except Exception as e:
            logger.error(f"❌ Error adding Model A vote: {e}")
            gr.Info(f"Error recording vote: {e}")

    def vote_model_b(profile_or_username):
        global current_gemini_output, current_mistral_output, current_openai_output, current_gpt5_output, current_image_url, current_voted_users, current_model_a, current_model_b
        
        # Get current username
        username = get_current_username(profile_or_username)
        
        if not username:
            username = "Anonymous"
        
        # Check if user has already voted
        if username in current_voted_users:
            gr.Info(f"You have already voted for this image, {username}!")
            return
        
        try:
            # Use the stored image URL from the upload
            image_url = current_image_url if current_image_url else "no_image"
            
            # Add vote to database
            logger.info(f"📊 Adding Model B vote for user: {username}")
            def output_for(model: str) -> str:
                return {
                    "gemini": current_gemini_output,
                    "mistral": current_mistral_output,
                    "openai": current_openai_output,
                    "gpt5": current_gpt5_output,
                }.get(model, "")

            add_vote(
                username=username,
                model_a=current_model_a,
                model_b=current_model_b,
                model_a_output=output_for(current_model_a),
                model_b_output=output_for(current_model_b),
                vote="model_b",
                image_url=image_url
            )
            
            # Mark user as voted
            current_voted_users.add(username)
            
            model_a_name = get_model_display_name(current_model_a)
            model_b_name = get_model_display_name(current_model_b)
            info_message = (
                f"<p>You voted for <strong style='color:blue;'>{model_b_name}</strong>.</p>"
                f"<p><span style='color:green;'>{model_a_name}</span> - "
                f"<span style='color:blue;'>{model_b_name}</span></p>"
            )
            gr.Info(info_message)
            
        except Exception as e:
            logger.error(f"❌ Error adding Model B vote: {e}")
            gr.Info(f"Error recording vote: {e}")

    # Event handlers
    process_btn.click(
        process_image,
        inputs=[image_input],
        outputs=[gemini_output, mistral_output, gemini_vote_btn, mistral_vote_btn],
    )
    
    # Process both randomly selected OCRs when the process button is clicked
    def process_model_a_ocr(image):
        global current_model_a
        return process_model_ocr(image, current_model_a)
    
    def process_model_b_ocr(image):
        global current_model_b
        return process_model_ocr(image, current_model_b)
    
    process_btn.click(
        process_model_a_ocr,
        inputs=[image_input],
        outputs=[gemini_output],
    )
    
    process_btn.click(
        process_model_b_ocr,
        inputs=[image_input],
        outputs=[mistral_output],
    )
    
    # Check completion status when either OCR output changes
    gemini_output.change(
        check_ocr_completion,
        inputs=[gemini_output, mistral_output],
        outputs=[gemini_vote_btn, mistral_vote_btn],
    )
    
    mistral_output.change(
        check_ocr_completion,
        inputs=[gemini_output, mistral_output],
        outputs=[gemini_vote_btn, mistral_vote_btn],
    )

    gemini_vote_btn.click(
        vote_model_a,
        inputs=[login_button]
    )
    
    mistral_vote_btn.click(
        vote_model_b,
        inputs=[login_button]
    )
    
    # Refresh data button
    refresh_btn.click(
        load_vote_data,
        inputs=None,
        outputs=[votes_table]
    )

    # Refresh leaderboard button
    refresh_leaderboard_btn.click(
        load_elo_leaderboard,
        inputs=None,
        outputs=[leaderboard_display]
    )

    # Update username display when user logs in
    demo.load(fn=get_default_username, inputs=None, outputs=username_display)

    # Load vote data when app starts
    demo.load(fn=load_vote_data, inputs=None, outputs=[votes_table])

    # Load leaderboard when app starts
    demo.load(fn=load_elo_leaderboard, inputs=None, outputs=[leaderboard_display])

if __name__ == "__main__":
    logger.info("Starting OCR Comparison App...")
    try:
        # Try to launch on localhost first
        demo.launch(share=True)
    except ValueError as e:
        logger.warning(f"Localhost not accessible: {e}")
        logger.info("Launching with public URL...")
        demo.launch(share=True)