Update app.py

app.py

@@ -1,71 +1,123 @@
-from typing import List
-from setuptools import find_packages, setup
+import gradio as gr
+from huggingface_hub import hf_hub_download
 import subprocess
+import tempfile
+import shutil
+import os
+import spaces
 
-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
-)
+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():
+    # Install Apex in editable mode from the specified GitHub repository
+    cmd = [
+        'pip', 'install', '--no-cache-dir', '--no-build-isolation',
+        '--config-settings', 'build-option=--cpp_ext', '--config-settings',
+        'build-option=--cuda_ext', '-e', 'git+https://github.com/NVIDIA/apex.git'
+    ]
+    subprocess.run(cmd, check=True)
+
+try:
+    import apex
+except ModuleNotFoundError:
+    print("Apex not found, installing...")
+    install_apex()
+    # Try to import Apex again after installation
+    import apex
+    
+
+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()