hf_extractor / app.py
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import os
import subprocess
import gradio as gr
def clone_repo(url, repo_dir):
env = os.environ.copy()
env['GIT_LFS_SKIP_SMUDGE'] = '1'
result = subprocess.run(["git", "clone", url, repo_dir], env=env, capture_output=True, text=True)
if result.returncode != 0:
return False, result.stderr
return True, None
def get_file_summary(file_path):
size = os.path.getsize(file_path)
file_type = "binary" if size > 1024 * 1024 else "text"
return {
"name": os.path.relpath(file_path),
"type": file_type,
"size": size,
}
def read_file_content(file_path):
with open(file_path, "r") as file:
return file.read()
def extract_repo_content(url):
repo_dir = "./temp_repo"
if os.path.exists(repo_dir):
subprocess.run(["rm", "-rf", repo_dir])
success, error = clone_repo(url, repo_dir)
if not success:
return [{"header": {"name": "Error", "type": "error", "size": 0}, "content": error}]
extracted_content = []
for root, _, files in os.walk(repo_dir):
for file in files:
file_path = os.path.join(root, file)
file_summary = get_file_summary(file_path)
content = {"header": file_summary}
if file_summary["type"] == "text" and file_summary["size"] <= 1024 * 1024:
try:
content["content"] = read_file_content(file_path)
except Exception as e:
content["content"] = f"Failed to read file content: {str(e)}"
else:
content["content"] = "File too large or binary, content not captured."
extracted_content.append(content)
return extracted_content
def format_output(extracted_content):
formatted_output = ""
for file_data in extracted_content:
if isinstance(file_data, dict) and 'header' in file_data:
formatted_output += f"### File: {file_data['header']['name']}\n"
formatted_output += f"**Type:** {file_data['header']['type']}\n"
formatted_output += f"**Size:** {file_data['header']['size']} bytes\n"
formatted_output += "#### Content:\n"
formatted_output += f"```\n{file_data['content']}\n```\n\n"
else:
formatted_output += "Error in file data format.\n"
return formatted_output
def extract_and_display(url):
extracted_content = extract_repo_content(url)
formatted_output = format_output(extracted_content)
return formatted_output
app = gr.Blocks()
with app:
gr.Markdown("# Gradio Space/Model Content Extractor")
url_input = gr.Textbox(label="Hugging Face Space/Model URL")
output_display = gr.Textbox(show_copy_button=True, lines=20, placeholder="Output will be displayed here...")
extract_button = gr.Button("Extract Content")
extract_button.click(fn=extract_and_display, inputs=url_input, outputs=output_display)
app.launch()