app.py

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 (keeps the blue tint glitch!)
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 (with blue tint + garment overlay)
def tryon(person_img, cloth_img, seed, random_seed):
    import cv2
    import numpy as np

    # 🎲 Handle seed
    if random_seed:
        seed = random.randint(0, 1000000)

    try:
        # ✅ Convert images to RGB (fixing blue tint)
        person_img = cv2.cvtColor(person_img, cv2.COLOR_BGR2RGB)
        cloth_img = cv2.cvtColor(cloth_img, cv2.COLOR_BGR2RGB)

        # ✂️ Resize both images to 256x256 (keep consistent sizes)
        person_img = cv2.resize(person_img, (256, 256))

        # ✅ Resize garment to fit person size
        g_h, g_w = person_img.shape[:2]
        cloth_img = cv2.resize(cloth_img, (g_w, g_h))

        # 🔥 Blend garment onto person (centered overlay)
        output_img = person_img.copy()
        h, w, _ = person_img.shape

        # Center garment on the torso (adjust the offset if needed)
        x_offset = (w - g_w) // 2
        y_offset = int(h * 0.35)

        # ✅ Ensure garment fits the person image
        if y_offset + g_h > h or x_offset + g_w > w:
            raise ValueError("❗ Garment image is too large for the person image!")

        # 🛠️ Overlay garment on the person (with transparency support)
        alpha = 0.7  # Adjust transparency level (0 = invisible, 1 = solid)
        output_img[y_offset:y_offset + g_h, x_offset:x_offset + g_w] = cv2.addWeighted(
            person_img[y_offset:y_offset + g_h, x_offset:x_offset + g_w],
            1 - alpha,
            cloth_img,
            alpha,
            0
        )

        return output_img, seed, "✅ Success (Blue tint removed + Garment added)"

    except Exception as e:
        print(f"❌ Error in tryon function: {e}")
        return person_img, seed, f"❌ Error: {e}"




# 🔧 Paths for examples
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]


# 🔧 Paths for examples
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)