Update app.py

app.py

@@ -1,117 +1,71 @@
-import gradio as gr
-from huggingface_hub import hf_hub_download
+from typing import List
+from setuptools import find_packages, setup
 import subprocess
-import tempfile
-import shutil
-import os
-import spaces
 
-from transformers import T5ForConditionalGeneration, T5Tokenizer
-import os
-
-subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
-def install_apex():
-    subprocess.run([
-        'pip', 'install', '-v', '--disable-pip-version-check', '--no-cache-dir',
-        '--no-build-isolation', 'git+https://github.com/NVIDIA/apex.git'
-    ], check=True)
-
-try:
-    import apex
-except ModuleNotFoundError:
-    print("Installing Apex...")
-    install_apex()
-    import apex  # Try to import Apex again after installation
-
-
-def download_t5_model(model_id, save_directory):
-    # Modelin tokenizer'ını ve modeli indir
-    model = T5ForConditionalGeneration.from_pretrained(model_id)
-    tokenizer = T5Tokenizer.from_pretrained(model_id)
-
-    # Model ve tokenizer'ı belirtilen dizine kaydet
-    if not os.path.exists(save_directory):
-        os.makedirs(save_directory)
-    model.save_pretrained(save_directory)
-    tokenizer.save_pretrained(save_directory)
-
-# Model ID ve kaydedilecek dizin
-model_id = "DeepFloyd/t5-v1_1-xxl"
-save_directory = "pretrained_models/t5_ckpts/t5-v1_1-xxl"
-
-# Modeli indir
-download_t5_model(model_id, save_directory)
-
-def download_model(repo_id, model_name):
-    model_path = hf_hub_download(repo_id=repo_id, filename=model_name)
-    return model_path
-
-import glob
-
-@spaces.GPU
-def run_inference(model_name, prompt_text):
-    repo_id = "hpcai-tech/Open-Sora"
-    
-    # Map model names to their respective configuration files
-    config_mapping = {
-        "OpenSora-v1-16x256x256.pth": "configs/opensora/inference/16x256x256.py",
-        "OpenSora-v1-HQ-16x256x256.pth": "configs/opensora/inference/16x512x512.py",
-        "OpenSora-v1-HQ-16x512x512.pth": "configs/opensora/inference/64x512x512.py"
-    }
-    
-    config_path = config_mapping[model_name]
-    ckpt_path = download_model(repo_id, model_name)
-
-    # Save prompt_text to a temporary text file
-    prompt_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode='w')
-    prompt_file.write(prompt_text)
-    prompt_file.close()
-
-    with open(config_path, 'r') as file:
-        config_content = file.read()
-    config_content = config_content.replace('prompt_path = "./assets/texts/t2v_samples.txt"', f'prompt_path = "{prompt_file.name}"')
-    
-    with tempfile.NamedTemporaryFile('w', delete=False, suffix='.py') as temp_file:
-        temp_file.write(config_content)
-        temp_config_path = temp_file.name
-
-    cmd = [
-        "torchrun", "--standalone", "--nproc_per_node", "1",
-        "scripts/inference.py", temp_config_path,
-        "--ckpt-path", ckpt_path
-    ]
-    subprocess.run(cmd)
-
-    save_dir = "./outputs/samples/"  # Örneğin, inference.py tarafından kullanılan kayıt dizini
-    list_of_files = glob.glob(f'{save_dir}/*')
-    if list_of_files:
-        latest_file = max(list_of_files, key=os.path.getctime)
-        return latest_file
-    else:
-        print("No files found in the output directory.")
-        return None
-
-    # Clean up the temporary files
-    os.remove(temp_file.name)
-    os.remove(prompt_file.name)
-
-def main():
-    gr.Interface(
-        fn=run_inference,
-        inputs=[
-            gr.Dropdown(choices=[
-                "OpenSora-v1-16x256x256.pth",
-                "OpenSora-v1-HQ-16x256x256.pth",
-                "OpenSora-v1-HQ-16x512x512.pth"
-            ], 
-            value="OpenSora-v1-16x256x256.pth",
-            label="Model Selection"),
-            gr.Textbox(label="Prompt Text", value="Enter prompt text here")
-        ],
-        outputs=gr.Video(label="Output Video"),
-        title="Open-Sora Inference",
-        description="Run Open-Sora Inference with Custom Parameters",
-    ).launch()
-
-if __name__ == "__main__":
-    main()
+def fetch_requirements(path) -> List[str]:
+    """
+    This function reads the requirements file.
+
+    Args:
+        path (str): the path to the requirements file.
+
+    Returns:
+        The lines in the requirements file.
+    """
+    with open(path, "r") as fd:
+        return [r.strip() for r in fd.readlines()]
+
+def fetch_readme() -> str:
+    """
+    This function reads the README.md file in the current directory.
+
+    Returns:
+        The lines in the README file.
+    """
+    with open("README.md", encoding="utf-8") as f:
+        return f.read()
+
+setup(
+    name="opensora",
+    version="1.0.0",
+    packages=find_packages(
+        exclude=(
+            "assets",
+            "configs",
+            "docs",
+            "outputs",
+            "pretrained_models",
+            "scripts",
+            "tests",
+            "tools",
+            "*.egg-info",
+        )
+    ),
+    description="Democratizing Efficient Video Production for All",
+    long_description=fetch_readme(),
+    long_description_content_type="text/markdown",
+    license="Apache Software License 2.0",
+    install_requires=fetch_requirements("requirements.txt"),
+    python_requires=">=3.6",
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: Apache Software License",
+        "Environment :: GPU :: NVIDIA CUDA",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Topic :: System :: Distributed Computing",
+    ],
+)
+
+install_options = [
+    "--disable-pip-version-check",
+    "--no-cache-dir",
+    "--no-build-isolation",
+    "--config-settings", "--build-option=--cpp_ext",
+    "--config-settings", "--build-option=--cuda_ext"
+]
+
+subprocess.run(
+    ["pip", "install", "-v"] + install_options + ["git+https://github.com/kadirnar/apex.git"],
+    check=True,
+    capture_output=True
+)