Spaces:
Running
Running
| import gradio as gr | |
| import requests | |
| from bs4 import BeautifulSoup | |
| import json | |
| import time | |
| import zipfile | |
| import os | |
| import tempfile | |
| import mimetypes | |
| from tqdm import tqdm | |
| def fetch_content(url): | |
| """Fetch content from a given URL.""" | |
| try: | |
| response = requests.get(url, timeout=10) | |
| response.raise_for_status() | |
| return response.text | |
| except requests.RequestException as e: | |
| print(f"Error fetching {url}: {e}") | |
| return None | |
| def extract_text(html): | |
| """Extract text from HTML content.""" | |
| soup = BeautifulSoup(html, 'html.parser') | |
| for script in soup(["script", "style"]): | |
| script.decompose() | |
| text = soup.get_text() | |
| lines = (line.strip() for line in text.splitlines()) | |
| chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) | |
| return '\n'.join(chunk for chunk in chunks if chunk) | |
| def process_urls(urls): | |
| """Process a list of URLs and return their extracted text.""" | |
| dataset = [] | |
| for url in tqdm(urls, desc="Fetching URLs"): | |
| html = fetch_content(url) | |
| if html: | |
| text = extract_text(html) | |
| dataset.append({ | |
| "source": "url", | |
| "url": url, | |
| "content": text | |
| }) | |
| time.sleep(1) # Be polite to the server | |
| return dataset | |
| def process_file(file): | |
| """Process uploaded files (including zip files) and extract text.""" | |
| dataset = [] | |
| with tempfile.TemporaryDirectory() as temp_dir: | |
| if zipfile.is_zipfile(file.name): | |
| with zipfile.ZipFile(file.name, 'r') as zip_ref: | |
| zip_ref.extractall(temp_dir) | |
| # Process each extracted file | |
| for root, _, files in os.walk(temp_dir): | |
| for filename in files: | |
| filepath = os.path.join(root, filename) | |
| mime_type, _ = mimetypes.guess_type(filepath) | |
| if mime_type and mime_type.startswith('text'): | |
| with open(filepath, 'r', errors='ignore') as f: | |
| content = f.read() | |
| dataset.append({ | |
| "source": "file", | |
| "filename": filename, | |
| "content": content | |
| }) | |
| else: | |
| # For non-text files, just store the filename | |
| dataset.append({ | |
| "source": "file", | |
| "filename": filename, | |
| "content": "Binary file - content not extracted" | |
| }) | |
| else: | |
| mime_type, _ = mimetypes.guess_type(file.name) | |
| if mime_type and mime_type.startswith('text'): | |
| content = file.read().decode('utf-8', errors='ignore') | |
| dataset.append({ | |
| "source": "file", | |
| "filename": os.path.basename(file.name), | |
| "content": content | |
| }) | |
| else: | |
| # For non-text files, just store the filename | |
| dataset.append({ | |
| "source": "file", | |
| "filename": os.path.basename(file.name), | |
| "content": "Binary file - content not extracted" | |
| }) | |
| return dataset | |
| def process_text(text): | |
| """Process raw text input.""" | |
| return [{ | |
| "source": "text_input", | |
| "content": text | |
| }] | |
| def create_dataset(urls, file, text_input): | |
| """Create a combined dataset from URLs, uploaded files, and text input.""" | |
| dataset = [] | |
| if urls: | |
| dataset.extend(process_urls([url.strip() for url in urls.split(',') if url.strip()])) | |
| if file: | |
| dataset.extend(process_file(file)) | |
| if text_input: | |
| dataset.extend(process_text(text_input)) | |
| # Save the dataset as JSON | |
| output_file = 'combined_dataset.json' | |
| with open(output_file, 'w') as f: | |
| json.dump(dataset, f, indent=2) | |
| return output_file | |
| # Gradio Interface | |
| iface = gr.Interface( | |
| fn=create_dataset, | |
| inputs=[ | |
| gr.Textbox(lines=5, label="Enter comma-separated URLs"), | |
| gr.File(label="Upload file (including zip files)"), | |
| gr.Textbox(lines=10, label="Enter or paste large text") | |
| ], | |
| outputs=gr.File(label="Download Combined Dataset"), | |
| title="URL, File, and Text to Dataset Converter", | |
| description="Enter URLs, upload files (including zip files), and/or paste text to create a combined dataset for AI training.", | |
| ) | |
| # Launch the interface | |
| iface.launch() |