import os
import base64
from io import BytesIO
from flask import Flask, render_template, request, jsonify
from PIL import Image
import openai
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

app = Flask(__name__)
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16MB max file size

# Initialize OpenAI client
client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY'))

def process_image(image_file):
    """Convert uploaded image to base64"""
    img = Image.open(image_file)
    # Convert RGBA to RGB if necessary
    if img.mode == 'RGBA':
        img = img.convert('RGB')
    buffered = BytesIO()
    img.save(buffered, format="JPEG")
    return base64.b64encode(buffered.getvalue()).decode('utf-8')

@app.route('/')
def home():
    return render_template('predictor.html')

@app.route('/predict', methods=['POST'])
def predict():
    try:
        # Get uploaded images
        red_fighter = request.files['red_fighter']
        blue_fighter = request.files['blue_fighter']
        
        if not red_fighter or not blue_fighter:
            return jsonify({'error': 'Please upload both fighter images'}), 400

        # Process images to base64
        red_image = process_image(red_fighter)
        blue_image = process_image(blue_fighter)

        # Create the prompt
        prompt_text = (
            "🎤 LADIES AND GENTLEMEN! Welcome to the most electrifying show in sports entertainment "
            "Let's break down this matchup that's got everyone talking!\n\n"
            "In the red corner, we have:(YOUR FIRST IMAGE):\n"
            "And in the blue corner: (YOUR SECOND IMAGE):\n\n"
            "Now, as your favorite fight comentator, I want you to:\n"
            "create a fight commentary of whats happening in the fight live\n"
            "Give us your best fight commentary! Make it exciting, make it dramatic, make it sound like you're calling the fight live! "
            "Throw in some classic commentator phrases, maybe a 'OH MY GOODNESS!' or two, and definitely some dramatic pauses for effect.\n\n"
            "End your masterpiece with your prediction in this exact format:\n"
            "\\boxed{Red} or \\boxed{Blue}"
            "PLEASE FORMAT THE COMMENTARY IN THE EXACT FORMAT AS THE EXAMPLE BELOW:\n"
            "[S1]Hello im your host  [S2] And so am i (name) [S1] Wow. Amazing. (laughs) [S2] Lets get started! (coughs) ( add lots of coughs and laughs)\n\n"
        )

        # Create the messages for the API call
        messages = [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt_text},
                    {
                        "type": "image_url",
                        "image_url": {"url": f"data:image/jpeg;base64,{red_image}"}
                    },
                    {
                        "type": "image_url",
                        "image_url": {"url": f"data:image/jpeg;base64,{blue_image}"}
                    }
                ]
            }
        ]

        # Make the API call
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            max_tokens=2048,
            temperature=0.7,
            top_p=0.95
        )

        # Extract the prediction
        prediction = response.choices[0].message.content
        
        return jsonify({
            'prediction': prediction,
            'success': True
        })

    except Exception as e:
        return jsonify({
            'error': str(e),
            'success': False
        }), 500

if __name__ == '__main__':
    app.run(debug=True)