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+FROM python:3.9-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["python", "app.py"]

app.py

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+import gradio as gr
+import requests
+import json
+import os
+import hashlib
+import time
+from PIL import Image
+from pathlib import Path
+from io import BytesIO
+from groq import Groq
+from dotenv import load_dotenv
+
+if os.path.exists('.env'):
+    load_dotenv()
+else:
+    print("Warning: .env file not found. Using default environment variables.")
+
+# Get environment variables with fallback
+api_key_token = os.getenv('api_key_token', '')
+groq_api_key = os.getenv('groq_api_key', '')
+
+if not api_key_token or not groq_api_key:
+    raise ValueError("Please configure your API keys in .env file")
+
+# Ghi log khởi động
+print("Version 2.19 - Fixed RGBA to RGB conversion for JPEG")
+print("Running grok_app.py with workflow processing and .env configuration")
+
+# URL và thông tin cá nhân từ API
+url_pre = "https://ap-east-1.tensorart.cloud/v1"
+SAVE_DIR = "generated_images"
+Path(SAVE_DIR).mkdir(exist_ok=True)
+
+# Danh sách mã sản phẩm
+PRODUCT_GROUPS = {
+    "Standard": {
+        "C1012 Glacier White": "817687427545199895",
+        "C1026 Polar": "819910519797326073",
+        "C3269 Ash Grey": "821839484099264081",
+        "C3168 Silver Wave": "821849044696643212",
+        "C1005 Milky White": "821948258441171133",
+    },
+    "Deluxe": {
+        "C2103 Onyx Carrara": "827090618489513527",
+        "C2104 Massa": "822075428127644644",
+        "C3105 Casla Cloudy": "828912225788997963",
+        "C3146 Casla Nova": "828013009961087650",
+        "C2240 Marquin": "828085015087780649",
+        "C2262 Concrete (Honed)": "822211862058871636",
+        "C3311 Calacatta Sky": "829984593223502930",
+        "C3346 Massimo": "827938741386607132",
+    },
+    "Luxury": {
+        "C4143 Mario": "829984593223502930",
+        "C4145 Marina": "828132560375742058",
+        "C4202 Calacatta Gold": "828167757632695310",
+        "C1205 Casla Everest": "828296778450463190",
+        "C4211 Calacatta Supreme": "828436321937882328",
+        "C4204 Calacatta Classic": "828422973179466146",
+        "C5240 Spring": "is coming",
+        "C1102 Super White": "828545723344775887",
+        "C4246 Casla Mystery": "828544778451950698",
+        "C4345 Oro": "828891068780182635",
+        "C4346 Luxe": "829436426547535131",
+        "C4342 Casla Eternal": "829190256201829181",
+        "C4221 Athena": "829644354504131520",
+        "C4222 Lagoon": "is coming",
+        "C5225 Amber": "is coming",
+    },
+    "Super Luxury": {
+        "C4255 Calacatta Extra": "829659013227537217",
+    },
+}
+
+# Danh sách ảnh sản phẩm tương ứng với mã sản phẩm
+PRODUCT_IMAGE_MAP = {
+    "C1012 Glacier White": "product_images/C1012.jpg",
+    "C1026 Polar": "product_images/C1026.jpg",
+    "C3269 Ash Grey": "product_images/C3269.jpg",
+    "C3168 Silver Wave": "product_images/C3168.jpg",
+    "C1005 Milky White": "product_images/C1005.jpg",
+    "C2103 Onyx Carrara": "product_images/C2103.jpg",
+    "C2104 Massa": "product_images/C2104.jpg",
+    "C3105 Casla Cloudy": "product_images/C3105.jpg",
+    "C3146 Casla Nova": "product_images/C3146.jpg",
+    "C2240 Marquin": "product_images/C2240.jpg",
+    "C2262 Concrete (Honed)": "product_images/C2262.jpg",
+    "C3311 Calacatta Sky": "product_images/C3311.jpg",
+    "C3346 Massimo": "product_images/C3346.jpg",
+    "C4143 Mario": "product_images/C4143.jpg",
+    "C4145 Marina": "product_images/C4145.jpg",
+    "C4202 Calacatta Gold": "product_images/C4202.jpg",
+    "C1205 Casla Everest": "product_images/C1205.jpg",
+    "C4211 Calacatta Supreme": "product_images/C4211.jpg",
+    "C4204 Calacatta Classic": "product_images/C4204.jpg",
+    "C1102 Super White": "product_images/C1102.jpg",
+    "C4246 Casla Mystery": "product_images/C4246.jpg",
+    "C4345 Oro": "product_images/C4345.jpg",
+    "C4346 Luxe": "product_images/C4346.jpg",
+    "C4342 Casla Eternal": "product_images/C4342.jpg",
+    "C4221 Athena": "product_images/C4221.jpg",
+    "C4255 Calacatta Extra": "product_images/C4255.jpg",
+}
+
+# Định nghĩa màu sắc cho từng nhóm sản phẩm
+GROUP_COLORS = {
+    "Standard": "#FFCCCC",
+    "Deluxe": "#CCFFCC",
+    "Luxury": "#CCCCFF",
+    "Super Luxury": "#CCFCFF",
+}
+
+# Khởi tạo client Groq
+client = Groq(api_key=groq_api_key)
+
+def rewrite_prompt_with_groq(vietnamese_prompt, product_codes):
+    prompt = f"{vietnamese_prompt}, featuring {' and '.join(product_codes)} quartz marble"
+    return prompt
+
+# Hàm upload ảnh lên TensorArt
+def upload_image_to_tensorart(image_path):
+    try:
+        url = f"{url_pre}/resource/image"
+        payload = json.dumps({"expireSec": "7200"})
+        headers = {
+            'Content-Type': 'application/json',
+            'Accept': 'application/json',
+            'Authorization': f'Bearer {api_key_token}'
+        }
+        print(f"Starting upload for: {image_path}")
+        if not os.path.exists(image_path):
+            print(f"File does not exist: {image_path}")
+            return None
+        response = requests.post(url, headers=headers, data=payload, timeout=30)
+        print(f"POST response: {response.status_code} - {response.text}")
+        response.raise_for_status()
+        resource_response = response.json()
+        
+        put_url = resource_response.get('putUrl')
+        headers_put = resource_response.get('headers', {'Content-Type': 'image/jpeg'})
+        if not put_url:
+            print(f"Upload failed - No 'putUrl' in response: {resource_response}")
+            return None
+        
+        print(f"Got putUrl: {put_url}")
+        with open(image_path, 'rb') as img_file:
+            upload_response = requests.put(put_url, data=img_file, headers=headers_put)
+            print(f"PUT response: {upload_response.status_code} - {upload_response.text}")
+            if upload_response.status_code not in [200, 203]:
+                raise Exception(f"PUT failed with status {upload_response.status_code}: {upload_response.text}")
+            if upload_response.status_code == 203:
+                print("Warning: PUT returned 203 - CallbackFailed, but proceeding with resourceId")
+        
+        resource_id = resource_response.get('resourceId')
+        if not resource_id:
+            print(f"Upload failed - No 'resourceId' in response: {resource_response}")
+            return None
+        print(f"Upload successful - resourceId: {resource_id}")
+        time.sleep(10)  # Đợi đồng bộ tài nguyên
+        print(f"Waited 10s for resource sync: {resource_id}")
+        return resource_id
+    except Exception as e:
+        print(f"Upload error for {image_path}: {str(e)}")
+        return None
+
+# Hàm kiểm tra params
+def check_workflow_params(params):
+    url = f"{url_pre}/jobs/workflow/params/check"
+    headers = {
+        'Content-Type': 'application/json',
+        'Accept': 'application/json',
+        'Authorization': f'Bearer {api_key_token}'
+    }
+    payload = json.dumps({"params": params})
+    print(f"Checking workflow params: {json.dumps(payload, indent=2)}")
+    response = requests.post(url, headers=headers, data=payload, timeout=30)
+    print(f"Params check response: {response.status_code} - {response.text}")
+    if response.status_code != 200:
+        raise Exception(f"Params check failed: {response.text}")
+    return response.json()
+
+# Hàm chạy workflow và chờ kết quả
+def run_workflow(payload, step_name):
+    try:
+        check_workflow_params(payload["params"])
+    except Exception as e:
+        print(f"Workflow params check failed for {step_name}: {str(e)}")
+        raise
+    
+    headers = {
+        'Content-Type': 'application/json',
+        'Accept': 'application/json',
+        'Authorization': f'Bearer {api_key_token}'
+    }
+    print(f"Sending {step_name} workflow request to {url_pre}/jobs/workflow with data: {json.dumps(payload, indent=2)}")
+    response = requests.post(f"{url_pre}/jobs/workflow", json=payload, headers=headers, timeout=300)
+    print(f"{step_name} workflow response: {response.status_code} - {response.text}")
+    if response.status_code != 200:
+        raise Exception(f"Error {response.status_code}: {response.text}")
+    
+    response_data = response.json()
+    job_id = response_data['job'].get('id')
+    if not job_id:
+        raise Exception("Không tìm thấy job_id trong response")
+    
+    print(f"Starting {step_name} job status check for job_id: {job_id}")
+    max_attempts = 36
+    for attempt in range(max_attempts):
+        response = requests.get(f"{url_pre}/jobs/{job_id}", headers=headers, timeout=30)
+        response.raise_for_status()
+        result = response.json()
+        print(f"{step_name} job status (attempt {attempt + 1}/{max_attempts}): {json.dumps(result, indent=2)}")
+        status = result['job'].get('status')
+        if status == 'SUCCESS':
+            success_info = result['job'].get('successInfo', {})
+            if not success_info.get('images'):
+                raise Exception(f"Không tìm thấy hình ảnh trong successInfo cho {step_name}")
+            image_url = success_info['images'][0]['url']
+            image_response = requests.get(image_url)
+            image = Image.open(BytesIO(image_response.content))
+            # Chuyển từ RGBA sang RGB nếu cần
+            if image.mode == 'RGBA':
+                image = image.convert('RGB')
+            output_path = Path(SAVE_DIR) / f"{step_name}_{int(time.time())}.jpg"
+            image.save(output_path)
+            print(f"{step_name} image saved to: {output_path}")
+            return str(output_path)
+        elif status in ['FAILED', 'ERROR']:
+            failed_info = result['job'].get('failedInfo', {})
+            error_reason = failed_info.get('reason', 'Không có chi tiết')
+            error_code = failed_info.get('code', 'Không xác định')
+            raise Exception(f"{step_name} job thất bại: {error_reason} (code: {error_code})")
+        time.sleep(5)
+    raise Exception(f"Hết thời gian chờ {step_name} job sau 3 phút")
+
+# Hàm tạo mask và áp texture
+def generate_mask(image_resource_id, position, selected_product_code):
+    try:
+        if not image_resource_id:
+            raise Exception("Không có image_resource_id hợp lệ - ảnh gốc chưa được upload")
+        print(f"Using image_resource_id: {image_resource_id}")
+        time.sleep(10)  # Đợi đồng bộ tài nguyên
+        
+        short_code = selected_product_code.split()[0]
+        texture_filepath = PRODUCT_IMAGE_MAP.get(selected_product_code)
+        print(f"Texture file: {texture_filepath}, exists: {os.path.exists(texture_filepath)}")
+        if not texture_filepath or not os.path.exists(texture_filepath):
+            raise Exception(f"Không tìm thấy ảnh sản phẩm cho mã {short_code}")
+        
+        texture_resource_id = upload_image_to_tensorart(texture_filepath)
+        print(f"Texture resource_id: {texture_resource_id}")
+        if not texture_resource_id:
+            raise Exception(f"Không thể upload ảnh sản phẩm {short_code}")
+        time.sleep(10)  # Đợi đồng bộ tài nguyên
+
+        if isinstance(position, (set, list)):
+            position = position[0] if position else "default"
+        print(f"Position: {position}, type: {type(position)}")
+
+        # Dùng params đúng như mẫu TensorArt
+        workflow_params = {
+            "1": {
+                "classType": "LayerMask: SegmentAnythingUltra V3",
+                "inputs": {
+                    "black_point": 0.3,
+                    "detail_dilate": 6,
+                    "detail_erode": 65,
+                    "detail_method": "GuidedFilter",
+                    "device": "cuda",
+                    "image": ["2", 0],
+                    "max_megapixels": 2,
+                    "process_detail": True,
+                    "prompt": ["4", 0],
+                    "sam_models": ["3", 0],
+                    "threshold": 0.3,
+                    "white_point": 0.99
+                },
+                "properties": {"Node name for S&R": "LayerMask: SegmentAnythingUltra V3"}
+            },
+            "10": {
+                "classType": "Image Seamless Texture",
+                "inputs": {
+                    "blending": 0.37,
+                    "images": ["17", 0],
+                    "tiled": "true",
+                    "tiles": 2
+                },
+                "properties": {"Node name for S&R": "Image Seamless Texture"}
+            },
+            "13": {
+                "classType": "Paste By Mask",
+                "inputs": {
+                    "image_base": ["2", 0],
+                    "image_to_paste": ["10", 0],
+                    "mask": ["8", 0],
+                    "resize_behavior": "resize"
+                },
+                "properties": {"Node name for S&R": "Paste By Mask"}
+            },
+            "17": {
+                "classType": "TensorArt_LoadImage",
+                "inputs": {
+                    "_height": 768,
+                    "_width": 512,
+                    "image": texture_resource_id,
+                    "upload": "image"
+                },
+                "properties": {"Node name for S&R": "TensorArt_LoadImage"}
+            },
+            "2": {
+                "classType": "TensorArt_LoadImage",
+                "inputs": {
+                    "_height": 1024,
+                    "_width": 768,
+                    "image": image_resource_id,
+                    "upload": "image"
+                },
+                "properties": {"Node name for S&R": "TensorArt_LoadImage"}
+            },
+            "3": {
+                "classType": "LayerMask: LoadSegmentAnythingModels",
+                "inputs": {
+                    "grounding_dino_model": "GroundingDINO_SwinB (938MB)",
+                    "sam_model": "sam_vit_h (2.56GB)"
+                },
+                "properties": {"Node name for S&R": "LayerMask: LoadSegmentAnythingModels"}
+            },
+            "4": {
+                "classType": "TensorArt_PromptText",
+                "inputs": {"Text": position.lower()},
+                "properties": {"Node name for S&R": "TensorArt_PromptText"}
+            },
+            "7": {
+                "classType": "PreviewImage",
+                "inputs": {"images": ["13", 0]},
+                "properties": {"Node name for S&R": "PreviewImage"}
+            },
+            "8": {
+                "classType": "MaskToImage",
+                "inputs": {"mask": ["1", 1]},
+                "properties": {"Node name for S&R": "MaskToImage"}
+            }
+        }
+
+        payload = {
+            "requestId": f"workflow_{int(time.time())}",
+            "params": workflow_params,
+            "runningNotifyUrl": ""
+        }
+        
+        output_path = run_workflow(payload, "full_workflow")
+        return output_path
+
+    except Exception as e:
+        print(f"Mask generation error: {str(e)}")
+        return None
+
+# Hàm xử lý img2img với spinner và progress bar
+def generate_img2img(image, position, size_choice, custom_size, *product_choices):
+    yield None, gr.update(visible=True), gr.update(visible=True, value='<div class="progress-container"><div class="progress-bar" style="width: 0%"></div></div>'), None
+    
+    if size_choice == "Custom size":
+        if not custom_size.strip():
+            yield "Vui lòng nhập kích thước tùy chỉnh.", gr.update(visible=False), gr.update(visible=False), None
+            return
+        width, height = map(int, custom_size.split("x"))
+    else:
+        width, height = map(int, size_choice.split("x"))
+
+    yield "Đang upload ảnh gốc...", gr.update(visible=True), gr.update(visible=True, value='<div class="progress-container"><div class="progress-bar" style="width: 20%"></div></div>'), None
+    image_path = Path(SAVE_DIR) / f"input_{int(time.time())}.jpg"
+    image.save(image_path)
+    image_resource_id = upload_image_to_tensorart(str(image_path))
+    print(f"Generated image_resource_id: {image_resource_id}")
+    if not image_resource_id:
+        yield "Lỗi: Không thể upload ảnh gốc", gr.update(visible=False), gr.update(visible=False), None
+        return
+
+    yield "Đang chuẩn bị ảnh sản phẩm...", gr.update(visible=True), gr.update(visible=True, value='<div class="progress-container"><div class="progress-bar" style="width: 40%"></div></div>'), None
+    selected_products = []
+    for group, choices in zip(PRODUCT_GROUPS.keys(), product_choices):
+        selected_products.extend(choices)
+    if not selected_products:
+        yield "Vui lòng chọn ít nhất một mã sản phẩm.", gr.update(visible=False), gr.update(visible=False), None
+        return
+    selected_product_code = selected_products[0]
+
+    yield "Đang tạo mask và áp texture...", gr.update(visible=True), gr.update(visible=True, value='<div class="progress-container"><div class="progress-bar" style="width: 60%"></div></div>'), None
+    output_path = generate_mask(image_resource_id, position, selected_product_code)
+    if not output_path:
+        yield "Lỗi: Không thể tạo ảnh", gr.update(visible=False), gr.update(visible=False), None
+        return
+    
+    yield "Hoàn tất!", gr.update(visible=False), gr.update(visible=True, value='<div class="progress-container"><div class="progress-bar" style="width: 100%"></div></div>'), Image.open(output_path)
+
+# Hàm generate_with_loading (text2img)
+def generate_with_loading(prompt, size_choice, custom_size, *product_choices):
+    yield None, gr.update(visible=True), gr.update(visible=True, value='<div class="progress-container"><div class="progress-bar" style="width: 0%"></div></div>'), None
+    try:
+        if size_choice == "Custom size":
+            if not custom_size.strip():
+                yield "Vui lòng nhập kích thước tùy chỉnh.", gr.update(visible=False), gr.update(visible=False), None
+                return
+            width, height = map(int, custom_size.split("x"))
+        else:
+            width, height = map(int, size_choice.split("x"))
+
+        selected_products = []
+        for group, choices in zip(PRODUCT_GROUPS.keys(), product_choices):
+            selected_products.extend(choices)
+        if not selected_products:
+            yield "Vui lòng chọn ít nhất một mã sản phẩm.", gr.update(visible=False), gr.update(visible=False), None
+            return
+
+        short_codes = [code.split()[0] for code in selected_products]
+        rewritten_prompt = rewrite_prompt_with_groq(prompt, short_codes)
+        print(f"Rewritten Prompt: {rewritten_prompt}")
+
+        progress = 0
+        while progress < 100:
+            time.sleep(0.5)
+            progress += 10
+            yield None, gr.update(visible=True), gr.update(value=f'<div class="progress-container"><div class="progress-bar" style="width: {progress}%"></div></div>'), None
+
+        result = txt2img(rewritten_prompt, width, height, short_codes)
+        if isinstance(result, str):
+            yield result, gr.update(visible=False), gr.update(visible=False), None
+        else:
+            yield None, gr.update(visible=False), gr.update(visible=False), result
+    except Exception as e:
+        yield f"Lỗi khi xử lý: {e}", gr.update(visible=False), gr.update(visible=False), None
+
+# Hàm text2img
+def txt2img(prompt, width, height, product_codes):
+    model_id = "779398605850080514"
+    vae_id = "ae.sft"
+
+    txt2img_data = {
+        "request_id": hashlib.md5(str(int(time.time())).encode()).hexdigest(),
+        "stages": [
+            {"type": "INPUT_INITIALIZE", "inputInitialize": {"seed": -1, "count": 1}},
+            {
+                "type": "DIFFUSION",
+                "diffusion": {
+                    "width": width,
+                    "height": height,
+                    "prompts": [{"text": prompt}],
+                    "negativePrompts": [{"text": " "}],
+                    "sdModel": model_id,
+                    "sdVae": vae_id,
+                    "sampler": "Euler a",
+                    "steps": 30,
+                    "cfgScale": 8,
+                    "clipSkip": 1,
+                    "etaNoiseSeedDelta": 31337,
+                }
+            }
+        ]
+    }
+    headers = {
+        'Content-Type': 'application/json',
+        'Accept': 'application/json',
+        'Authorization': f'Bearer {api_key_token}'
+    }
+    response = requests.post(f"{url_pre}/jobs", json=txt2img_data, headers=headers)
+    if response.status_code != 200:
+        return f"Error: {response.status_code} - {response.text}"
+    response_data = response.json()
+    job_id = response_data['job']['id']
+    print(f"Job created. ID: {job_id}")
+    start_time = time.time()
+    timeout = 300
+    while True:
+        time.sleep(10)
+        elapsed_time = time.time() - start_time
+        if elapsed_time > timeout:
+            return f"Error: Job timed out after {timeout} seconds."
+        response = requests.get(f"{url_pre}/jobs/{job_id}", headers=headers)
+        if response.status_code != 200:
+            return f"Error: {response.status_code} - {response.text}"
+        get_job_response_data = response.json()
+        job_status = get_job_response_data['job']['status']
+        if job_status == 'SUCCESS':
+            image_url = get_job_response_data['job']['successInfo']['images'][0]['url']
+            response_image = requests.get(image_url)
+            img = Image.open(BytesIO(response_image.content))
+            save_path = Path(SAVE_DIR) / f"{hashlib.md5(prompt.encode()).hexdigest()}.png"
+            img.save(save_path)
+            print(f"Image saved to: {save_path}")
+            return img
+        elif job_status == 'FAILED':
+            return "Error: Job failed."
+
+# CSS
+css = """
+.loading-spinner { border: 4px solid #f3f3f3; border-top: 4px solid #3498db; border-radius: 50%; width: 40px; height: 40px; animation: spin 1s linear infinite; margin: auto; }
+@keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } }
+.progress-container { width: 100%; max-width: 400px; margin: 20px auto; background-color: #f3f3f3; border-radius: 20px; overflow: hidden; box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1); }
+.progress-bar { width: 0%; height: 10px; background: linear-gradient(90deg, #3498db, #e74c3c); border-radius: 20px; transition: width 0.3s ease-in-out; }
+"""
+
+# Giao diện Gradio
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("## Ứng dụng Tạo Ảnh CaslaQuartz với Ảnh Sản Phẩm")
+    with gr.Tabs():
+        with gr.Tab("Text2Img"):
+            with gr.Row():
+                with gr.Column():
+                    prompt_input = gr.Textbox(label="Mô tả ảnh cần tạo", placeholder="Một nhà bếp hiện đại với mặt bàn bằng đá thạch anh.")
+                    size_radio = gr.Radio(choices=["1152x768", "1024x1024", "768x1152", "Custom size"], label="Chọn kích thước ảnh", value="1024x1024")
+                    custom_size_input = gr.Textbox(label="Nhập kích thước tùy chỉnh (VD: 1280x720)", placeholder="Chiều rộng x Chiều cao", visible=False)
+                    size_radio.change(fn=lambda x: gr.update(visible=x == "Custom size"), inputs=size_radio, outputs=custom_size_input)
+                    product_checkbox_group = []
+                    for group, color in GROUP_COLORS.items():
+                        with gr.Accordion(f"Sản phẩm - {group}", open=False):
+                            checkboxes = gr.CheckboxGroup(
+                                choices=[(code, code) for code in PRODUCT_GROUPS[group] if PRODUCT_GROUPS[group][code] not in ["is coming", "is loading"]],
+                                label=f"Chọn sản phẩm ({group})",
+                                value=[]
+                            )
+                            product_checkbox_group.append((group, checkboxes))
+                    generate_button = gr.Button("Generate")
+                with gr.Column():
+                    output_image = gr.Image(label="Ảnh đã tạo")
+                    error_message = gr.Textbox(label="Thông báo", visible=False)
+                    loading_spinner = gr.HTML('<div class="loading-spinner"></div>', visible=False)
+                    progress_bar = gr.HTML('', visible=False)
+            inputs = [prompt_input, size_radio, custom_size_input] + [checkboxes for _, checkboxes in product_checkbox_group]
+            generate_button.click(fn=generate_with_loading, inputs=inputs, outputs=[error_message, loading_spinner, progress_bar, output_image])
+
+        with gr.Tab("Img2Img"):
+            with gr.Row():
+                with gr.Column():
+                    image_upload = gr.Image(label="Tải lên ảnh", type="pil")
+                    position_input = gr.Dropdown(label="Chọn vật muốn thử nghiệm", choices=["Wall", "Countertop", "Floor", "Backsplash"], value="Wall")
+                    size_radio_img2img = gr.Radio(choices=["1152x768", "1024x1024", "768x1152", "Custom size"], label="Chọn kích thước ảnh", value="1024x1024")
+                    custom_size_input_img2img = gr.Textbox(label="Nhập kích thước tùy chỉnh (VD: 1280x720)", placeholder="Chiều rộng x Chiều cao", visible=False)
+                    size_radio_img2img.change(fn=lambda x: gr.update(visible=x == "Custom size"), inputs=size_radio_img2img, outputs=custom_size_input_img2img)
+                    product_checkbox_group_img2img = []
+                    for group, color in GROUP_COLORS.items():
+                        with gr.Accordion(f"Sản phẩm - {group}", open=False):
+                            checkboxes = gr.CheckboxGroup(
+                                choices=[(code, code) for code in PRODUCT_GROUPS[group] if PRODUCT_GROUPS[group][code] not in ["is coming", "is loading"]],
+                                label=f"Chọn sản phẩm ({group})",
+                                value=[]
+                            )
+                            product_checkbox_group_img2img.append((group, checkboxes))
+                    inpaint_button = gr.Button("Inpaint")
+                with gr.Column():
+                    output_image_img2img = gr.Image(label="Ảnh đã tạo")
+                    error_message_img2img = gr.Textbox(label="Thông báo", visible=False)
+                    loading_spinner_img2img = gr.HTML('<div class="loading-spinner"></div>', visible=False)
+                    progress_bar_img2img = gr.HTML('', visible=False)
+            inputs_img2img = [image_upload, position_input, size_radio_img2img, custom_size_input_img2img] + [checkboxes for _, checkboxes in product_checkbox_group_img2img]
+            inpaint_button.click(fn=generate_img2img, inputs=inputs_img2img, outputs=[error_message_img2img, loading_spinner_img2img, progress_bar_img2img, output_image_img2img])
+
+demo.launch(share=True)

requirements.txt

@@ -0,0 +1,8 @@
+gradio>=3.50.0
+requests>=2.31.0
+Pillow>=10.0.0
+python-dotenv>=1.0.0
+groq>=0.3.0
+rembg>=2.0.50
+opencv-python-headless>=4.8.0
+numpy>=1.24.0