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import gradio as gr
from langchain.document_loaders import PyMuPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document
from typing import List
import logging
from pathlib import Path
import requests
import base64
import io
import fitz
from PIL import Image
from datasets import Dataset
from huggingface_hub import HfApi
import os
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Original chunk_pdf function (slightly modified for Gradio)
def chunk_pdf(
file_path: str,
chunk_size: int = 1000,
chunk_overlap: int = 200,
encoding: str = "utf-8",
preserve_numbering: bool = True
) -> List[Document]:
if chunk_size <= 0:
raise ValueError("chunk_size must be positive")
if chunk_overlap < 0:
raise ValueError("chunk_overlap cannot be negative")
if chunk_overlap >= chunk_size:
raise ValueError("chunk_overlap must be less than chunk_size")
try:
temp_file = None
if file_path.startswith(("http://", "https://")):
logger.info(f"Downloading PDF from {file_path}")
response = requests.get(file_path, stream=True, timeout=10)
response.raise_for_status()
temp_file = Path("temp.pdf")
with open(temp_file, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
file_path = str(temp_file)
elif not Path(file_path).exists():
raise FileNotFoundError(f"PDF file not found at: {file_path}")
logger.info(f"Loading PDF from {file_path}")
loader = PyMuPDFLoader(file_path)
pages = loader.load()
if not pages:
logger.warning(f"No content extracted from {file_path}")
return []
separators = (
["\n\d+\.\s+", "\n\n", "\n", ".", " ", ""]
if preserve_numbering
else ["\n\n", "\n", ".", " ", ""]
)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
length_function=len,
separators=separators,
keep_separator=True,
add_start_index=True,
is_separator_regex=preserve_numbering
)
logger.info(f"Splitting {len(pages)} pages into chunks")
chunks = text_splitter.split_documents(pages)
if preserve_numbering:
merged_chunks = []
current_chunk = None
for chunk in chunks:
content = chunk.page_content.strip()
if current_chunk is None:
current_chunk = chunk
elif content.startswith(tuple(f"{i}." for i in range(10))):
if current_chunk:
merged_chunks.append(current_chunk)
current_chunk = chunk
else:
current_chunk.page_content += "\n" + content
current_chunk.metadata["end_index"] = chunk.metadata["start_index"] + len(content)
if current_chunk:
merged_chunks.append(current_chunk)
chunks = merged_chunks
logger.info(f"Created {len(chunks)} chunks")
return chunks
except Exception as e:
logger.error(f"Error processing PDF {file_path}: {str(e)}")
raise
finally:
if temp_file and temp_file.exists():
temp_file.unlink()
# Custom function to convert PDF page to base64
def pdf_page_to_base64(pdf_path: str, page_number: int):
pdf_document = fitz.open(pdf_path)
page = pdf_document.load_page(page_number - 1) # input is one-indexed
pix = page.get_pixmap()
img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
buffer = io.BytesIO()
img.save(buffer, format="PNG")
return base64.b64encode(buffer.getvalue()).decode("utf-8")
# Function to process PDF and create dataset
def process_pdf_and_save(pdf_file, chunk_size, chunk_overlap, preserve_numbering, hf_token, repo_name):
try:
# Save uploaded file temporarily
pdf_path = pdf_file.name
chunks = chunk_pdf(pdf_path, chunk_size, chunk_overlap, "utf-8", preserve_numbering)
# Prepare dataset
data = {
"chunk_id": [],
"content": [],
"metadata": [],
"page_image": []
}
for i, chunk in enumerate(chunks):
data["chunk_id"].append(i)
data["content"].append(chunk.page_content)
data["metadata"].append(chunk.metadata)
page_num = chunk.metadata.get("page", 1)
img_base64 = pdf_page_to_base64(pdf_path, page_num)
data["page_image"].append(img_base64)
# Create Hugging Face dataset
dataset = Dataset.from_dict(data)
# Push to Hugging Face
api = HfApi()
api.create_repo(repo_id=repo_name, token=hf_token, repo_type="dataset", exist_ok=True)
dataset.push_to_hub(repo_name, token=hf_token)
return f"Dataset created with {len(chunks)} chunks and saved to Hugging Face at {repo_name}"
except Exception as e:
return f"Error: {str(e)}"
# Gradio Interface
with gr.Blocks(title="PDF Chunking and Dataset Creator") as demo:
gr.Markdown("# PDF Chunking and Dataset Creator")
gr.Markdown("Upload a PDF, configure chunking parameters, and save the dataset to Hugging Face.")
with gr.Row():
with gr.Column():
pdf_input = gr.File(label="Upload PDF")
chunk_size = gr.Slider(500, 2000, value=1000, step=100, label="Chunk Size")
chunk_overlap = gr.Slider(0, 500, value=200, step=50, label="Chunk Overlap")
preserve_numbering = gr.Checkbox(label="Preserve Numbering", value=True)
hf_token = gr.Textbox(label="Hugging Face Token", type="password")
repo_name = gr.Textbox(label="Hugging Face Repository Name (e.g., username/dataset-name)")
submit_btn = gr.Button("Process and Save")
with gr.Column():
output = gr.Textbox(label="Result")
submit_btn.click(
fn=process_pdf_and_save,
inputs=[pdf_input, chunk_size, chunk_overlap, preserve_numbering, hf_token, repo_name],
outputs=output
)
demo.launch(
share=True,
) |