import os

# Install Flask if not already installed
return_code = os.system('pip install flask')
if return_code != 0:
    raise RuntimeError("Failed to install Flask")

import gradio as gr
from random import randint
from all_models import models
from flask import Flask, request, send_file
from io import BytesIO
from PIL import Image, ImageChops

app = Flask(__name__)

# グローバルなモデル辞書
models_load = {}

def load_model(model_name):
    global models_load
    if model_name not in models_load:
        try:
            m = gr.load(f'models/{model_name}')
            print(f"Model {model_name} loaded successfully.")
            models_load[model_name] = m
        except Exception as error:
            print(f"Error loading model {model_name}: {error}")
            models_load[model_name] = gr.Interface(lambda txt: None, ['text'], ['image'])

def gen_fn(model_str, prompt, negative_prompt=None, noise=None, cfg_scale=None, num_inference_steps=None, sampler=None):
    if model_str not in models_load:
        load_model(model_str)

    if model_str in models_load:
        if noise == "random":
            noise = str(randint(0, 99999999999))
        full_prompt = f'{prompt} {noise}' if noise else prompt

        # ネガティブプロンプトとその他のパラメータをログに出力
        print(f"Prompt: {full_prompt}, Negative Prompt: {negative_prompt}, CFG Scale: {cfg_scale}, Steps: {num_inference_steps}, Sampler: {sampler}")

        try:
            # Adjust the inputs depending on model capabilities
            if negative_prompt:
                inputs = {
                    'prompt': full_prompt,
                    'negative_prompt': negative_prompt,
                    'cfg_scale': cfg_scale,
                    'num_inference_steps': num_inference_steps,
                    'sampler': sampler
                }
            else:
                inputs = {
                    'prompt': full_prompt,
                    'cfg_scale': cfg_scale,
                    'num_inference_steps': num_inference_steps,
                    'sampler': sampler
                }

            # Filter out None values
            inputs = {k: v for k, v in inputs.items() if v is not None}

            # モデル呼び出し
            result = models_load[model_str](**inputs)

            # Debugging result type
            print(f"Result type: {type(result)}, Result: {result}")

            # Check if result is an image or a file path
            if isinstance(result, str):  # Assuming result might be a file path
                if os.path.exists(result):
                    image = Image.open(result)
                else:
                    print(f"File path not found: {result}")
                    return None, 'File path not found'
            elif isinstance(result, Image.Image):
                image = result
            else:
                print("Result is not an image:", type(result))
                return None, f"Unexpected result type: {type(result)}"
            
            # Check if the image is completely black
            black = Image.new('RGB', image.size, (0, 0, 0))
            if ImageChops.difference(image, black).getbbox() is None:
                return None, 'The image is completely black. There may be a parameter that cannot be specified, or an error may have occurred internally.'

            return image, None

        except Exception as e:
            print("Error generating image:", e)
            return None, f"Error generating image: {e}"
    else:
        print(f"Model {model_str} not found")
        return None, f"Model {model_str} not found"

@app.route('/', methods=['GET'])
def home():
    prompt = request.args.get('prompt', '')
    model = request.args.get('model', '')
    negative_prompt = request.args.get('Nprompt', None)
    noise = request.args.get('noise', None)
    cfg_scale = request.args.get('cfg_scale', None)
    num_inference_steps = request.args.get('steps', None)
    sampler = request.args.get('sampler', None)
    
    try:
        if cfg_scale is not None:
            cfg_scale = float(cfg_scale)
    except ValueError:
        return 'Invalid "cfg_scale" parameter. It should be a number.', 400

    try:
        if num_inference_steps is not None:
            num_inference_steps = int(num_inference_steps)
    except ValueError:
        return 'Invalid "steps" parameter. It should be an integer.', 400
    
    if not model:
        return 'Please provide a "model" query parameter in the URL.', 400

    if not prompt:
        return 'Please provide a "prompt" query parameter in the URL.', 400
    
    # Generate the image
    image, error_message = gen_fn(model, prompt, negative_prompt, noise, cfg_scale, num_inference_steps, sampler)
    if error_message:
        return error_message, 400
    
    if isinstance(image, Image.Image):  # Ensure the result is a PIL image
        # Save image to BytesIO object
        img_io = BytesIO()
        image.save(img_io, format='PNG')
        img_io.seek(0)
        return send_file(img_io, mimetype='image/png', as_attachment=False)
    
    return 'Failed to generate image.', 500

if __name__ == '__main__':
    # Launch Flask app
    app.run(host='0.0.0.0', port=7860)  # Run Flask app