# # Copyright (c) 2023-2024, Qi Zuo
# #
# # Licensed under the Apache License, Version 2.0 (the "License");
# # you may not use this file except in compliance with the License.
# # You may obtain a copy of the License at
# #
# #     https://www.apache.org/licenses/LICENSE-2.0
# #
# # Unless required by applicable law or agreed to in writing, software
# # distributed under the License is distributed on an "AS IS" BASIS,
# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# # See the License for the specific language governing permissions and
# # limitations under the License.


# import os
# from PIL import Image
# import numpy as np
# import gradio as gr
# import base64
# import spaces
# import subprocess
# import os

# # def install_cuda_toolkit():
# # #     CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run"
# # #     # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run"
# # #     CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL)
# # #     subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE])
# # #     subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE])
# # #     subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"])

# #     os.environ["CUDA_HOME"] = "/usr/local/cuda"
# #     os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"])
# #     os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % (
# #         os.environ["CUDA_HOME"],
# #         "" if "LD_LIBRARY_PATH" not in os.environ else os.environ["LD_LIBRARY_PATH"],
# #     )
# #     # Fix: arch_list[-1] += '+PTX'; IndexError: list index out of range
# #     os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"

# # install_cuda_toolkit()

# def launch_pretrained():
#     from huggingface_hub import snapshot_download, hf_hub_download
#     hf_hub_download(repo_id="DyrusQZ/LHM_Runtime", repo_type='model', filename='assets.tar', local_dir="./")
#     os.system("tar -xvf assets.tar && rm assets.tar")
#     hf_hub_download(repo_id="DyrusQZ/LHM_Runtime", repo_type='model', filename='LHM-0.5B.tar', local_dir="./")
#     os.system("tar -xvf LHM-0.5B.tar && rm LHM-0.5B.tar")
#     hf_hub_download(repo_id="DyrusQZ/LHM_Runtime", repo_type='model', filename='LHM_prior_model.tar', local_dir="./")
#     os.system("tar -xvf LHM_prior_model.tar && rm LHM_prior_model.tar")

# def launch_env_not_compile_with_cuda():
#     os.system("pip install chumpy")
#     os.system("pip uninstall -y basicsr")
#     os.system("pip install git+https://github.com/hitsz-zuoqi/BasicSR/")
#     # os.system("pip install -e ./third_party/sam2")
#     os.system("pip install numpy==1.23.0")
#     # os.system("pip install git+https://github.com/hitsz-zuoqi/sam2/")
#     # os.system("pip install git+https://github.com/ashawkey/diff-gaussian-rasterization/")
#     # os.system("pip install git+https://github.com/camenduru/simple-knn/")
#     os.system("pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu121_pyt251/download.html")

# # def launch_env_compile_with_cuda():
# #     # simple_knn
# #     os.system("wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/simple_knn.zip && wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/simple_knn-0.0.0.dist-info.zip")
# #     os.system("unzip simple_knn.zip && unzip simple_knn-0.0.0.dist-info.zip")
# #     os.system("mv simple_knn /usr/local/lib/python3.10/site-packages/")
# #     os.system("mv simple_knn-0.0.0.dist-info /usr/local/lib/python3.10/site-packages/")

# #     # diff_gaussian
# #     os.system("wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/diff_gaussian_rasterization.zip && wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/diff_gaussian_rasterization-0.0.0.dist-info.zip")
# #     os.system("unzip diff_gaussian_rasterization.zip && unzip diff_gaussian_rasterization-0.0.0.dist-info.zip")
# #     os.system("mv diff_gaussian_rasterization /usr/local/lib/python3.10/site-packages/")
# #     os.system("mv diff_gaussian_rasterization-0.0.0.dist-info /usr/local/lib/python3.10/site-packages/")

# #     # pytorch3d
# #     os.system("wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/pytorch3d.zip && wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/pytorch3d-0.7.8.dist-info.zip")
# #     os.system("unzip pytorch3d.zip && unzip pytorch3d-0.7.8.dist-info.zip")
# #     os.system("mv pytorch3d /usr/local/lib/python3.10/site-packages/")
# #     os.system("mv pytorch3d-0.7.8.dist-info /usr/local/lib/python3.10/site-packages/")


# # launch_env_compile_with_cuda()

# def assert_input_image(input_image):
#     if input_image is None:
#         raise gr.Error("No image selected or uploaded!")

# def prepare_working_dir():
#     import tempfile
#     working_dir = tempfile.TemporaryDirectory()
#     return working_dir

# def init_preprocessor():
#     from LHM.utils.preprocess import Preprocessor
#     global preprocessor
#     preprocessor = Preprocessor()

# def preprocess_fn(image_in: np.ndarray, remove_bg: bool, recenter: bool, working_dir):
#     image_raw = os.path.join(working_dir.name, "raw.png")
#     with Image.fromarray(image_in) as img:
#         img.save(image_raw)
#     image_out = os.path.join(working_dir.name, "rembg.png")
#     success = preprocessor.preprocess(image_path=image_raw, save_path=image_out, rmbg=remove_bg, recenter=recenter)
#     assert success, f"Failed under preprocess_fn!"
#     return image_out

# def get_image_base64(path):
#     with open(path, "rb") as image_file:
#         encoded_string = base64.b64encode(image_file.read()).decode()
#     return f"data:image/png;base64,{encoded_string}"


# def demo_lhm(infer_impl):

#     def core_fn(image: str, video_params, working_dir):
#         image_raw = os.path.join(working_dir.name, "raw.png")
#         with Image.fromarray(image) as img:
#             img.save(image_raw)
        
#         base_vid = os.path.basename(video_params).split("_")[0]
#         smplx_params_dir = os.path.join("./assets/sample_motion", base_vid, "smplx_params")

#         dump_video_path = os.path.join(working_dir.name, "output.mp4")
#         dump_image_path = os.path.join(working_dir.name, "output.png")

#         status = spaces.GPU(infer_impl(
#             gradio_demo_image=image_raw, 
#             gradio_motion_file=smplx_params_dir, 
#             gradio_masked_image=dump_image_path, 
#             gradio_video_save_path=dump_video_path
#         ))
#         if status:
#             return dump_image_path, dump_video_path
#         else:
#             return None, None

#     _TITLE = '''LHM: Large Animatable Human Model'''

#     _DESCRIPTION = '''
#         <strong>Reconstruct a human avatar in 0.2 seconds with A100!</strong>
#     '''

#     with gr.Blocks(analytics_enabled=False) as demo:

#         # </div>
#         logo_url = "./assets/rgba_logo_new.png"
#         logo_base64 = get_image_base64(logo_url)
#         gr.HTML(
#             f"""
#             <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
#             <div>
#                 <h1> <img src="{logo_base64}" style='height:35px; display:inline-block;'/> Large Animatable Human Model </h1>
#             </div>
#             </div>
#             """
#         )
#         gr.HTML(
#             """<p><h4 style="color: red;"> Notes: Please input full-body image in case of detection errors.</h4></p>"""
#         )

#         # DISPLAY
#         with gr.Row():

#             with gr.Column(variant='panel', scale=1):
#                 with gr.Tabs(elem_id="openlrm_input_image"):
#                     with gr.TabItem('Input Image'):
#                         with gr.Row():
#                             input_image = gr.Image(label="Input Image", image_mode="RGBA", height=480, width=270, sources="upload", type="numpy", elem_id="content_image")
#                 # EXAMPLES
#                 with gr.Row():
#                     examples = [
#                         ['assets/sample_input/joker.jpg'],
#                         ['assets/sample_input/anime.png'],
#                         ['assets/sample_input/basket.png'],
#                         ['assets/sample_input/ai_woman1.JPG'],
#                         ['assets/sample_input/anime2.JPG'],
#                         ['assets/sample_input/anime3.JPG'],
#                         ['assets/sample_input/boy1.png'],
#                         ['assets/sample_input/choplin.jpg'],
#                         ['assets/sample_input/eins.JPG'],
#                         ['assets/sample_input/girl1.png'],
#                         ['assets/sample_input/girl2.png'],
#                         ['assets/sample_input/robot.jpg'],
#                     ]
#                     gr.Examples(
#                         examples=examples,
#                         inputs=[input_image], 
#                         examples_per_page=20,
#                     )

#             with gr.Column():
#                 with gr.Tabs(elem_id="openlrm_input_video"):
#                     with gr.TabItem('Input Video'):
#                         with gr.Row():
#                             video_input = gr.Video(label="Input Video",height=480, width=270, interactive=False)

#                 examples = [
#                     # './assets/sample_motion/danaotiangong/danaotiangong_origin.mp4',
#                     './assets/sample_motion/ex5/ex5_origin.mp4',
#                     './assets/sample_motion/girl2/girl2_origin.mp4',
#                     './assets/sample_motion/jntm/jntm_origin.mp4',
#                     './assets/sample_motion/mimo1/mimo1_origin.mp4',
#                     './assets/sample_motion/mimo2/mimo2_origin.mp4',
#                     './assets/sample_motion/mimo4/mimo4_origin.mp4',
#                     './assets/sample_motion/mimo5/mimo5_origin.mp4',
#                     './assets/sample_motion/mimo6/mimo6_origin.mp4',
#                     './assets/sample_motion/nezha/nezha_origin.mp4',
#                     './assets/sample_motion/taiji/taiji_origin.mp4'
#                 ]

#                 gr.Examples(
#                     examples=examples,
#                     inputs=[video_input],
#                     examples_per_page=20,
#                 )
#             with gr.Column(variant='panel', scale=1):
#                 with gr.Tabs(elem_id="openlrm_processed_image"):
#                     with gr.TabItem('Processed Image'):
#                         with gr.Row():
#                             processed_image = gr.Image(label="Processed Image", image_mode="RGBA", type="filepath", elem_id="processed_image", height=480, width=270, interactive=False)

#             with gr.Column(variant='panel', scale=1):
#                 with gr.Tabs(elem_id="openlrm_render_video"):
#                     with gr.TabItem('Rendered Video'):
#                         with gr.Row():
#                             output_video = gr.Video(label="Rendered Video", format="mp4", height=480, width=270, autoplay=True)

#         # SETTING
#         with gr.Row():
#             with gr.Column(variant='panel', scale=1):
#                 submit = gr.Button('Generate', elem_id="openlrm_generate", variant='primary')


#         working_dir = gr.State()
#         submit.click(
#             fn=assert_input_image,
#             inputs=[input_image],
#             queue=False,
#         ).success(
#             fn=prepare_working_dir,
#             outputs=[working_dir],
#             queue=False,
#         ).success(
#             fn=core_fn,
#             inputs=[input_image, video_input, working_dir], # video_params refer to smpl dir
#             outputs=[processed_image, output_video],
#         )

#         demo.queue()
#         demo.launch()


# def launch_gradio_app():

#     os.environ.update({
#         "APP_ENABLED": "1",
#         "APP_MODEL_NAME": "./exps/releases/video_human_benchmark/human-lrm-500M/step_060000/",
#         "APP_INFER": "./configs/inference/human-lrm-500M.yaml",
#         "APP_TYPE": "infer.human_lrm",
#         "NUMBA_THREADING_LAYER": 'omp',
#     })

#     from LHM.runners import REGISTRY_RUNNERS
#     RunnerClass = REGISTRY_RUNNERS[os.getenv("APP_TYPE")]
#     with RunnerClass() as runner:
#         demo_lhm(infer_impl=runner.infer)


# if __name__ == '__main__':
#     # launch_pretrained()
#     # launch_env_not_compile_with_cuda()
#     launch_gradio_app()

import gradio as gr

def greet(name):
    return "Hello " + name + "!!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()