import os
import random
import numpy as np
from huggingface_hub import AsyncInferenceClient
from translatepy import Translator
from gradio_client import Client, handle_file
from PIL import Image

# Constants
MAX_SEED = np.iinfo(np.int32).max
HF_TOKEN = os.getenv('HF_TOKEN')  # Set the environment variable for HF_TOKEN
HF_TOKEN_UPSCALER = os.getenv('HF_TOKEN')  # Set the environment variable for HF_TOKEN_UPSCALER


class Lorify:
    def __init__(self, hf_token=None, hf_token_upscaler=None):
        # Optionally load tokens from environment if not passed
        self.hf_token = hf_token or HF_TOKEN
        self.hf_token_upscaler = hf_token_upscaler or HF_TOKEN_UPSCALER

        # Initialize clients
        self.qwen_client = Client("K00B404/HugChatWrap", hf_token=self.hf_token)
        self.client = AsyncInferenceClient()

        # List of available LoRAs (replace with your LoRA repo names or paths)
        self.loaded_loras = []
        self.loras = [
            "Shakker-Labs/FLUX.1-dev-LoRA-add-details",
            "XLabs-AI/flux-RealismLora",
            "enhanceaiteam/Flux-uncensored"
        ]
        self.loaded_loras.extend(self.loras)

    # Enable or disable LoRA
    def enable_lora(self, lora_add, basemodel):
        return basemodel if not lora_add else lora_add

    # Generate image function
    async def generate_image(self, prompt, model, lora_word, width, height, scales, steps, seed):
        try:
            if seed == -1:
                seed = random.randint(0, MAX_SEED)
            seed = int(seed)

            # Translate prompt
            text = str(Translator().translate(prompt, 'English')) + "," + lora_word

            # Generate image
            image = await self.client.text_to_image(
                prompt=text,
                height=height,
                width=width,
                guidance_scale=scales,
                num_inference_steps=steps,
                model=model
            )

            return image, seed
        except Exception as e:
            print(f"Error generating image: {e}")
            return None, None

    # Upscale image function
    def upscale_image(self, prompt, img_path, upscale_factor):
        try:
            # Initialize the upscale client
            upscale_client = Client("finegrain/finegrain-image-enhancer", hf_token=self.hf_token_upscaler)
            result = upscale_client.predict(
                input_image=handle_file(img_path),
                prompt=prompt,
                negative_prompt="worst quality, low quality, normal quality",
                upscale_factor=upscale_factor,
                controlnet_scale=0.6,
                controlnet_decay=1,
                condition_scale=6,
                denoise_strength=0.35,
                num_inference_steps=18,
                solver="DDIM",
                api_name="/process"
            )
            return result[1]  # Return upscale image path
        except Exception as e:
            print(f"Error scaling image: {e}")
            return None

    # Main method to generate and optionally upscale image
    async def gen_image(self, prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
        model = self.enable_lora(lora_model, basemodel) if process_lora else basemodel

        image, seed = await self.generate_image(prompt, model, "", width, height, scales, steps, seed)

        if image is None:
            print("Image generation failed.")
            return []

        image_path = "temp_image.jpg"
        image.save(image_path, format="JPEG")

        upscale_image_path = None
        if process_upscale:
            upscale_image_path = self.upscale_image(prompt, image_path, upscale_factor)
            if upscale_image_path and os.path.exists(upscale_image_path):
                return [image_path, upscale_image_path]

        return [image_path]