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import gradio as gr
import tempfile
import os
import shutil
import subprocess
from pathlib import Path
SCRIPT_DIR = Path(__file__).resolve().parent
def run_cmd(cmd, cwd=None, env=None):
"""Run a command, print nice logs, and also save them to run.log in cwd."""
cwd = str(cwd or os.getcwd())
print(f"🟦 Running: {' '.join(cmd)} (cwd={cwd})")
proc = subprocess.run(
cmd,
cwd=cwd,
env=env,
capture_output=True,
text=True
)
if proc.stdout:
print("🟩 STDOUT:")
print(proc.stdout)
if proc.stderr:
print("🟥 STDERR:")
print(proc.stderr)
# Save to run.log for debugging
try:
runlog = Path(cwd) / "run.log"
with open(runlog, "a", encoding="utf-8") as f:
f.write(f"$ {' '.join(cmd)}\n")
if proc.stdout:
f.write(proc.stdout + "\n")
if proc.stderr:
f.write(proc.stderr + "\n")
print(f"🧾 Run log saved to: {runlog}")
except Exception as e:
print(f"⚠️ Could not write run.log: {e}")
if proc.returncode != 0:
# Let Gradio see the failure so it surfaces properly
raise subprocess.CalledProcessError(proc.returncode, cmd, proc.stdout, proc.stderr)
return proc
def _locate_pdf_json(temp_dir: str) -> str:
"""
Your extractor writes a JSON like <pdf_stem>_comprehensive_data.json.
Find it (and a few common fallbacks). Raise if not found.
"""
td = Path(temp_dir)
# Prefer exactly-named file if present
candidates = [
td / "pdf_data.json", # legacy name (if ever created)
td / "input_comprehensive_data.json", # most common from your logs
td / "comprehensive_data.json", # another common alias
td / "output.json", # generic
]
for p in candidates:
if p.exists():
print(f"✅ Using PDF JSON: {p}")
return str(p)
# Generic pattern: anything *_comprehensive_data.json
globs = list(td.glob("*_comprehensive_data.json"))
if globs:
print(f"✅ Using PDF JSON (glob): {globs[0]}")
return str(globs[0])
# If still not found, surface a helpful error
searched = ", ".join(str(p) for p in candidates) + ", " + str(td / "*_comprehensive_data.json")
raise FileNotFoundError(
f"PDF JSON not found. Looked for: {searched}\nTemp dir: {temp_dir}"
)
def process_files(pdf_file, word_file):
# Create a unique temporary directory for this run
temp_dir = tempfile.mkdtemp(prefix="hf_redtext_")
print(f"📂 Temp dir: {temp_dir}")
# Define standard filenames for use in the pipeline
pdf_path = os.path.join(temp_dir, "input.pdf")
word_path = os.path.join(temp_dir, "input.docx")
word_json_path = os.path.join(temp_dir, "word_data.json")
updated_json_path = os.path.join(temp_dir, "updated_word_data.json")
final_docx_path = os.path.join(temp_dir, "updated.docx")
# Copy the uploaded files to the temp directory
shutil.copy(pdf_file, pdf_path)
print(f"📄 PDF copied to: {pdf_path}")
shutil.copy(word_file, word_path)
print(f"📝 DOCX copied to: {word_path}")
# 1) PDF → JSON (extractor writes <stem>_comprehensive_data.json into cwd)
run_cmd(["python", str(SCRIPT_DIR / "extract_pdf_data.py"), pdf_path], cwd=temp_dir)
# Find the JSON produced by the extractor
pdf_json_path = _locate_pdf_json(temp_dir)
# 2) DOCX red text → JSON
run_cmd(["python", str(SCRIPT_DIR / "extract_red_text.py"), word_path, word_json_path], cwd=temp_dir)
# 3) Merge JSON (uses the resolved pdf_json_path)
run_cmd(["python", str(SCRIPT_DIR / "update_docx_with_pdf.py"), word_json_path, pdf_json_path, updated_json_path], cwd=temp_dir)
# 4) Apply updates to DOCX
run_cmd(["python", str(SCRIPT_DIR / "updated_word.py"), word_path, updated_json_path, final_docx_path], cwd=temp_dir)
# Return the final .docx file
return final_docx_path
iface = gr.Interface(
fn=process_files,
inputs=[
gr.File(label="Upload PDF File", type="filepath"),
gr.File(label="Upload Word File", type="filepath")
],
outputs=gr.File(label="Download Updated Word File"),
title="Red Text Replacer",
description="Upload a PDF and Word document. Red-colored text in the Word doc will be replaced by matching content from the PDF."
)
if __name__ == "__main__":
iface.launch() |