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# coding: utf-8
# Copyright (c) 2025 inclusionAI.
import json
import os
import base64
import tempfile
import subprocess
from typing import Any, Dict, Tuple
from urllib.parse import urlparse
from pydantic import BaseModel
from aworld.config import ToolConfig
from examples.tools.tool_action import DocumentExecuteAction
from aworld.core.common import Observation, ActionModel, ActionResult
from aworld.core.tool.base import ToolFactory, Tool
from aworld.logs.util import logger
from examples.tools.document.utils import encode_image_from_file, encode_image_from_url
from aworld.utils import import_package, import_packages
from aworld.tools.utils import build_observation
class InputDocument(BaseModel):
document_path: str | None = None
@ToolFactory.register(name="document_analysis",
desc="document analysis",
supported_action=DocumentExecuteAction,
conf_file_name=f'document_analysis_tool.yaml')
class DocumentTool(Tool):
def __init__(self, conf: ToolConfig, **kwargs) -> None:
"""Init document tool."""
import_package('cv2', install_name='opencv-python')
import_packages(['xmltodict', 'pandas', 'docx2markdown', 'PyPDF2', 'numpy'])
super(DocumentTool, self).__init__(conf, **kwargs)
self.cur_observation = None
self.content = None
self.keyframes = []
self.init()
self.step_finished = True
def reset(self, *, seed: int | None = None, options: Dict[str, str] | None = None) -> Tuple[
Observation, dict[str, Any]]:
super().reset(seed=seed, options=options)
self.close()
self.step_finished = True
return build_observation(observer=self.name(),
ability=DocumentExecuteAction.DOCUMENT_ANALYSIS.value.name), {}
def init(self) -> None:
self.initialized = True
def close(self) -> None:
pass
def finished(self) -> bool:
return self.step_finished
def do_step(self, actions: list[ActionModel], **kwargs) -> Tuple[Observation, float, bool, bool, Dict[str, Any]]:
self.step_finished = False
reward = 0.
fail_error = ""
observation = build_observation(observer=self.name(),
ability=DocumentExecuteAction.DOCUMENT_ANALYSIS.value.name)
info = {}
try:
if not actions:
raise ValueError("actions is empty")
action = actions[0]
document_path = action.params.get("document_path", "")
if not document_path:
raise ValueError("document path invalid")
output, keyframes, error = self.document_analysis(document_path)
observation.content = output
observation.action_result.append(
ActionResult(is_done=True,
success=False if error else True,
content=f"{output}",
error=f"{error}",
keep=False))
info['key_frame'] = f"{keyframes}"
reward = 1.
except Exception as e:
fail_error = str(e)
finally:
self.step_finished = True
info["exception"] = fail_error
info.update(kwargs)
return (observation, reward, kwargs.get("terminated", False),
kwargs.get("truncated", False), info)
def document_analysis(self, document_path):
import xmltodict
error = None
# Initialize content to empty list to avoid None return
self.content = []
try:
if any(document_path.endswith(ext) for ext in [".jpg", ".jpeg", ".png"]):
parsed_url = urlparse(document_path)
is_url = all([parsed_url.scheme, parsed_url.netloc])
if not is_url:
base64_image = encode_image_from_file(document_path)
else:
base64_image = encode_image_from_url(document_path)
self.content = f"data:image/jpeg;base64,{base64_image}"
if any(document_path.endswith(ext) for ext in ["xls", "xlsx"]):
try:
try:
import pandas as pd
except ImportError:
error = "pandas library not found. Please install pandas: pip install pandas"
return self.content, self.keyframes, error
excel_data = {}
with pd.ExcelFile(document_path) as xls:
sheet_names = xls.sheet_names
for sheet_name in sheet_names:
df = pd.read_excel(xls, sheet_name=sheet_name)
sheet_data = df.to_dict(orient='records')
excel_data[sheet_name] = sheet_data
self.content = json.dumps(excel_data, ensure_ascii=False)
logger.info(f"Successfully processed Excel file: {document_path}")
logger.info(f"Found {len(sheet_names)} sheets: {', '.join(sheet_names)}")
except Exception as excel_error:
error = str(excel_error)
if any(document_path.endswith(ext) for ext in ["json", "jsonl", "jsonld"]):
with open(document_path, "r", encoding="utf-8") as f:
self.content = json.load(f)
f.close()
if any(document_path.endswith(ext) for ext in ["xml"]):
data = None
with open(document_path, "r", encoding="utf-8") as f:
data = f.read()
f.close()
try:
self.content = xmltodict.parse(data)
logger.info(f"The extracted xml data is: {self.content}")
except Exception as e:
logger.info(f"The raw xml data is: {data}")
error = str(e)
self.content = data
if any(document_path.endswith(ext) for ext in ["doc", "docx"]):
from docx2markdown._docx_to_markdown import docx_to_markdown
file_name = os.path.basename(document_path)
md_file_path = f"{file_name}.md"
docx_to_markdown(document_path, md_file_path)
with open(md_file_path, "r") as f:
self.content = f.read()
f.close()
if any(document_path.endswith(ext) for ext in ["pdf"]):
# try using pypdf to extract text from pdf
try:
from PyPDF2 import PdfReader
# Open file in binary mode for PdfReader
f = open(document_path, "rb")
reader = PdfReader(f)
extracted_text = ""
for page in reader.pages:
extracted_text += page.extract_text()
self.content = extracted_text
f.close()
except Exception as pdf_error:
error = str(pdf_error)
# audio
if any(document_path.endswith(ext.lower()) for ext in [".mp3", ".wav", ".wave"]):
try:
# audio-> base64
with open(document_path, "rb") as audio_file:
audio_bytes = audio_file.read()
audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
# ext
ext = os.path.splitext(document_path)[1].lower()
mime_type = "audio/mpeg" if ext == ".mp3" else "audio/wav"
# data URI
self.content = f"data:{mime_type};base64,{audio_base64}"
except Exception as audio_error:
error = str(audio_error)
logger.error(f"Error processing audio file: {error}")
# video
if any(document_path.endswith(ext.lower()) for ext in [".mp4", ".avi", ".mov", ".mkv", ".flv", ".wmv"]):
try:
try:
import cv2
import numpy as np
except ImportError:
error = "Required libraries not found. Please install opencv-python: pip install opencv-python"
return None, None, error
# create temp dir
temp_dir = tempfile.mkdtemp()
# 1.get audio -> base64
audio_path = os.path.join(temp_dir, "extracted_audio.mp3")
# get audio by ffmpeg
try:
subprocess.run([
"ffmpeg", "-i", document_path, "-q:a", "0",
"-map", "a", audio_path, "-y"
], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
# audio->base64
with open(audio_path, "rb") as audio_file:
audio_bytes = audio_file.read()
audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
audio_data_uri = f"data:audio/mpeg;base64,{audio_base64}"
except (subprocess.SubprocessError, FileNotFoundError) as e:
logger.warning(f"Failed to extract audio: {str(e)}")
audio_data_uri = None
# 2. get keyframes
cap = cv2.VideoCapture(document_path)
if not cap.isOpened():
raise ValueError(f"Could not open video file: {document_path}")
# get video message
fps = cap.get(cv2.CAP_PROP_FPS)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
duration = frame_count / fps if fps > 0 else 0
# keyframes policy- per duration/10s，max 10
keyframes_count = min(10, int(frame_count))
frames_interval = max(1, int(frame_count / keyframes_count))
self.keyframes = []
frame_index = 0
while True:
ret, frame = cap.read()
if not ret:
break
# per frames_interval save
if frame_index % frames_interval == 0:
# save JPEG -> base64
_, buffer = cv2.imencode(".jpg", frame)
img_base64 = base64.b64encode(buffer).decode('utf-8')
time_position = frame_index / fps if fps > 0 else 0
self.keyframes.append(f"data:image/jpeg;base64,{img_base64}")
if len(self.keyframes) >= keyframes_count:
break
frame_index += 1
cap.release()
self.content = audio_data_uri
logger.info(f"Successfully processed video file: {document_path}")
logger.info(f"Extracted {len(self.keyframes)} keyframes and audio track")
# clean tmp files
try:
os.remove(audio_path)
os.rmdir(temp_dir)
except Exception as cleanup_error:
logger.warning(f"Error cleaning up temp files: {str(cleanup_error)}")
except Exception as video_error:
error = str(video_error)
logger.error(f"Error processing video file: {error}")
if any(document_path.endswith(ext) for ext in ["pptx"]):
try:
# Initialize content list and empty keyframes
self.content = []
self.keyframes = []
# Check if file exists
if not os.path.exists(document_path):
error = f"File does not exist: {document_path}"
return self.content, self.keyframes, error
# Check if file is readable
if not os.access(document_path, os.R_OK):
error = f"File is not readable: {document_path}"
return self.content, self.keyframes, error
# Check file size
try:
file_size = os.path.getsize(document_path)
if file_size == 0:
error = "File is empty"
return self.content, self.keyframes, error
except Exception as size_error:
logger.warning(f"Cannot get file size: {str(size_error)}")
try:
# Import required libraries
from pptx import Presentation
from PIL import Image, ImageDraw, ImageFont
import io
except ImportError as import_error:
error = f"Missing required libraries: {str(import_error)}. Please install: pip install python-pptx Pillow"
return self.content, self.keyframes, error
# Create temporary directory for images
try:
temp_dir = tempfile.mkdtemp()
except Exception as temp_dir_error:
error = f"Failed to create temporary directory: {str(temp_dir_error)}"
return self.content, self.keyframes, error
# Open presentation
try:
presentation = Presentation(document_path)
# Get total slides count
total_slides = len(presentation.slides)
if total_slides == 0:
error = "PPTX file does not contain any slides"
return self.content, self.keyframes, error
# Process each slide
for i, slide in enumerate(presentation.slides):
# Generate temporary file path for current slide
img_path = os.path.join(temp_dir, f"slide_{i + 1}.jpg")
# Get slide dimensions
try:
slide_width = presentation.slide_width
slide_height = presentation.slide_height
# PPTX dimensions are in EMU (English Metric Unit)
# 1 inch = 914400 EMU, 1 cm = 360000 EMU
# Convert to pixels (assuming 96 DPI)
slide_width_px = int(slide_width / 914400 * 96 * 10)
slide_height_px = int(slide_height / 914400 * 96 * 10)
# Ensure dimensions are reasonable positive integers
slide_width_px = max(1, min(slide_width_px, 4000)) # Limit max width to 4000px
slide_height_px = max(1, min(slide_height_px, 3000)) # Limit max height to 3000px
except Exception as size_error:
# Use default dimensions
slide_width_px = 960 # Default width 960px
slide_height_px = 720 # Default height 720px
# Create blank image
try:
# Log operation start
# Create blank image
try:
slide_img = Image.new('RGB', (slide_width_px, slide_height_px), 'white')
draw = ImageDraw.Draw(slide_img)
except Exception as img_create_error:
logger.error(
f"Slide {i + 1} blank image creation failed: {str(img_create_error) or 'Unknown error'}")
raise
# Draw slide number
try:
font = ImageFont.load_default()
draw.text((20, 20), f"Slide {i + 1}/{total_slides}", fill="black", font=font)
except Exception as font_error:
logger.warning(f"Failed to draw slide number: {str(font_error) or 'Unknown error'}")
# Record shape count
try:
shape_count = len(slide.shapes)
except Exception as shape_count_error:
logger.warning(
f"Failed to get slide {i + 1} shape count: {str(shape_count_error) or 'Unknown error'}")
shape_count = 0
# Try to render shapes on image
shape_success_count = 0
shape_fail_count = 0
try:
for j, shape in enumerate(slide.shapes):
try:
shape_type = type(shape).__name__
# Process images
if hasattr(shape, 'image') and shape.image:
try:
# Extract image from shape
image_stream = io.BytesIO(shape.image.blob)
img = Image.open(image_stream)
# Calculate position
left = shape.left
top = shape.top
# Paste image onto slide
slide_img.paste(img, (left, top))
shape_success_count += 1
except Exception as img_error:
logger.warning(
f"Failed to process image {j + 1} in slide {i + 1}: {str(img_error) or 'Unknown error'}")
if not str(img_error):
import traceback
logger.warning(
f"Image processing stack: {traceback.format_exc()}")
shape_fail_count += 1
# Process text
elif hasattr(shape, 'text') and shape.text:
try:
text = shape.text[:30] + "..." if len(
shape.text) > 30 else shape.text
# Simple text rendering
text_left = shape.left
text_top = shape.top
draw.text((text_left, text_top), shape.text, fill="black",
font=font)
shape_success_count += 1
except Exception as text_error:
logger.warning(
f"Failed to process text {j + 1} in slide {i + 1}: {str(text_error) or 'Unknown error'}")
if not str(text_error):
import traceback
logger.warning(
f"Text processing stack: {traceback.format_exc()}")
shape_fail_count += 1
else:
logger.info(
f"Shape {j + 1} in slide {i + 1} is neither image nor text, skipping")
except Exception as shape_error:
if not str(shape_error):
import traceback
logger.warning(f"Shape processing stack: {traceback.format_exc()}")
shape_fail_count += 1
except Exception as shapes_iteration_error:
logger.error(
f"Failed while iterating through shapes in slide {i + 1}: {str(shapes_iteration_error) or 'Unknown error'}")
if not str(shapes_iteration_error):
import traceback
logger.error(f"Shape iteration stack: {traceback.format_exc()}")
# Save slide image
try:
slide_img.save(img_path, 'JPEG')
# Check if image was saved successfully
if not os.path.exists(img_path):
raise ValueError(f"Saved image file does not exist: {img_path}")
file_size = os.path.getsize(img_path)
if file_size == 0:
raise ValueError(
f"Saved image file is empty: {img_path}, size: {file_size} bytes")
# Convert to base64
try:
base64_image = encode_image_from_file(img_path)
self.content.append(f"data:image/jpeg;base64,{base64_image}")
except Exception as base64_error:
error_msg = str(base64_error) or "Unknown base64 conversion error"
if not str(base64_error):
import traceback
logger.error(f"Base64 conversion stack: {traceback.format_exc()}")
raise ValueError(f"Base64 conversion error: {error_msg}")
except Exception as save_error:
error_msg = str(save_error) or "Unknown save error"
logger.error(f"Failed to save slide {i + 1} as image: {error_msg}")
if not str(save_error):
import traceback
logger.error(f"Image save stack: {traceback.format_exc()}")
raise ValueError(f"Image save error: {error_msg}")
except Exception as slide_render_error:
error_msg = str(slide_render_error) or "Unknown rendering error"
logger.error(f"Failed to render slide {i + 1}: {error_msg}")
if not str(slide_render_error):
import traceback
logger.error(f"Slide rendering stack: {traceback.format_exc()}")
# Continue processing next slide, don't interrupt the entire process
continue
except Exception as pptx_error:
error = f"Failed to process PPTX file: {str(pptx_error)}"
import traceback
# Clean up temporary files
try:
for file in os.listdir(temp_dir):
try:
file_path = os.path.join(temp_dir, file)
os.remove(file_path)
except Exception as file_error:
logger.warning(f"Failed to delete temporary file: {str(file_error)}")
os.rmdir(temp_dir)
except Exception as cleanup_error:
logger.warning(f"Failed to clean up temporary files: {str(cleanup_error)}")
if len(self.content) > 0:
logger.info(f"Extracted {len(self.content)} slides")
else:
error = error or "Could not extract any slides from PPTX file"
logger.error(error)
except Exception as outer_error:
error = f"Error occurred during PPTX file processing: {str(outer_error)}"
import traceback
return self.content, self.keyframes, error
finally:
pass
return self.content, self.keyframes, error
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