import os
import requests
import json
import time
import cv2
import base64
import random
import numpy as np
import gradio as gr

MAX_SEED = 999999

# ✅ Pixelcut API Key
pixelcut_api_key = "sk_299d9c6e36d240cbb3dd65fcbac947a4"

# ✅ ImgBB API Key (for uploading images to get valid URLs)
imgbb_api_key = "03a2ddf1ffa5df33a3999cf20c2fb20f"

# 🎯 Convert image to PNG format
def convert_to_png(image):
    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# 🔥 Resize large images to prevent upload failures (ImgBB limit: 32MB)
def resize_image(image, max_size=1024):
    height, width = image.shape[:2]
    if max(height, width) > max_size:
        scale = max_size / max(height, width)
        return cv2.resize(image, (int(width * scale), int(height * scale)))
    return image

# 🛠️ Upload images to ImgBB (fixed payload!)
def upload_image_to_imgbb(image_data):
    url = f"https://api.imgbb.com/1/upload?key={imgbb_api_key}"
    files = {"image": image_data}
    response = requests.post(url, files=files)

    if response.status_code == 200:
        return response.json().get("data", {}).get("url")
    else:
        print("❌ ImgBB upload failed:", response.text)
        return None

# 🚀 Main try-on function using Pixelcut API
def tryon(person_img, garment_img, seed, randomize_seed):
    start_time = time.time()

    # 🛑 Check if images are provided
    if person_img is None or garment_img is None:
        return None, None, "Empty image"

    # 🎲 Handle seed randomization
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    # 🔥 Convert and resize images to base64 PNG
    person_img = resize_image(convert_to_png(person_img))
    garment_img = resize_image(convert_to_png(garment_img))

    _, person_encoded = cv2.imencode('.png', person_img)
    _, garment_encoded = cv2.imencode('.png', garment_img)

    # ✅ Upload person and garment images to get valid URLs
    print("📤 Uploading person image...")
    person_url = upload_image_to_imgbb(person_encoded)

    print("📤 Uploading garment image...")
    garment_url = upload_image_to_imgbb(garment_encoded)

    if not person_url or not garment_url:
        return None, None, "Image upload failed — check API keys or connection"

    # 🌟 Setup Pixelcut API endpoint and headers
    url = "https://api.developer.pixelcut.ai/v1/remove-background"
    headers = {
        'Content-Type': 'application/json',
        'Accept': 'application/json',
        'X-API-KEY': pixelcut_api_key
    }

    # 🔧 Use the uploaded URLs in the payload
    person_data = {"image_url": person_url, "format": "png"}
    garment_data = {"image_url": garment_url, "format": "png"}

    result_img = None
    max_retries = 5
    retry_delay = 3  # Faster retries now!

    try:
     session = requests.Session()

    # 🛠️ Ensure both images are resized to 256x256
        person_img = cv2.resize(person_img, (256, 256))
        garment_img = cv2.resize(garment_img, (256, 256))

    # 🎯 Handle transparency if the garment has an alpha channel
        if garment_img.shape[-1] == 4:
            alpha = garment_img[:, :, 3] / 255.0
            for c in range(0, 3):
                person_img[:, :, c] = (1 - alpha) * person_img[:, :, c] + alpha * garment_img[:, :, c]

    # 🔧 Optional: Center the garment on the torso area
        h, w, _ = person_img.shape
        g_h, g_w, _ = garment_img.shape

        x_offset = (w - g_w) // 2
        y_offset = int(h * 0.35)  # Adjust this value for higher/lower garment placement
        person_img[y_offset:y_offset + g_h, x_offset:x_offset + g_w] = garment_img

    # ✅ Now the rest of your code continues here (retry loop, API call, etc.)

    except requests.exceptions.ReadTimeout:
        print("Timeout!")
        info = "⚡ Timeout — please try again later"
        raise gr.Error("Too many users, please try again later")

    except Exception as err:
        print(f"❌ Other error: {err}")
        info = "Error, please contact the admin"

example_path = os.path.join(os.path.dirname(__file__), 'assets')

garm_list = os.listdir(os.path.join(example_path,"cloth"))
garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]

human_list = os.listdir(os.path.join(example_path,"human"))
human_list_path = [os.path.join(example_path,"human",human) for human in human_list]

css="""
#col-left {
    margin: 0 auto;
    max-width: 430px;
}
#col-mid {
    margin: 0 auto;
    max-width: 430px;
}
#col-right {
    margin: 0 auto;
    max-width: 430px;
}
#col-showcase {
    margin: 0 auto;
    max-width: 1100px;
}
#button {
    color: blue;
}
"""

def load_description(fp):
    with open(fp, 'r', encoding='utf-8') as f:
        content = f.read()
    return content

def change_imgs(image1, image2):
    return image1, image2

with gr.Blocks(css=css) as Tryon:
    gr.HTML(load_description("assets/new_title.md"))
    with gr.Row():
        with gr.Column(elem_id = "col-left"):
            gr.HTML("""
            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
                <div>
                Step 1.  Upload a person image ⬇️
                </div>
            </div>
            """)
        with gr.Column(elem_id = "col-mid"):
            gr.HTML("""
            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
                <div>
                Step 2. Upload a garment image ⬇️
                </div>
            </div>
            """)
        with gr.Column(elem_id = "col-right"):
            gr.HTML("""
            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
                <div>
                Step 3. Press “Run” to get try-on results
                </div>
            </div>
            """)
    with gr.Row():
        with gr.Column(elem_id = "col-left"):
            imgs = gr.Image(label="Person image", sources='upload', type="numpy")
            # category = gr.Dropdown(label="Garment category", choices=['upper_body', 'lower_body', 'dresses'],  value="upper_body")
            example = gr.Examples(
                inputs=imgs,
                examples_per_page=12,
                examples=human_list_path
            )
        with gr.Column(elem_id = "col-mid"):
            garm_img = gr.Image(label="Garment image", sources='upload', type="numpy")
            example = gr.Examples(
                inputs=garm_img,
                examples_per_page=12,
                examples=garm_list_path
            )
        with gr.Column(elem_id = "col-right"):
            image_out = gr.Image(label="Result", show_share_button=False)
            with gr.Row():
                seed = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=MAX_SEED,
                    step=1,
                    value=0,
                )
                randomize_seed = gr.Checkbox(label="Random seed", value=True)
            with gr.Row():
                seed_used = gr.Number(label="Seed used")
                result_info = gr.Text(label="Response")
            # try_button = gr.Button(value="Run", elem_id="button")
            test_button = gr.Button(value="Run", elem_id="button")


    # try_button.click(fn=start_tryon, inputs=[imgs, garm_img, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name='tryon',concurrency_limit=10)
    test_button.click(fn=tryon, inputs=[imgs, garm_img, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name=False, concurrency_limit=45)

    with gr.Column(elem_id = "col-showcase"):
        gr.HTML("""
        <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
            <div> </div>
            <br>
            <div>
            Virtual try-on examples in pairs of person and garment images
            </div>
        </div>
        """)
        show_case = gr.Examples(
            examples=[
                ["assets/examples/model2.png", "assets/examples/garment2.png", "assets/examples/result2.png"],
                ["assets/examples/model3.png", "assets/examples/garment3.png", "assets/examples/result3.png"],
                ["assets/examples/model1.png", "assets/examples/garment1.png", "assets/examples/result1.png"],
            ],
            inputs=[imgs, garm_img, image_out],
            label=None
        )

Tryon.queue(api_open=False).launch(show_api=False)