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"""Slides parser.
Contains parsers for .pptx files.
"""
import os
from pathlib import Path
from typing import Dict
from gpt_index.readers.file.base_parser import BaseParser
class PptxParser(BaseParser):
"""Powerpoint parser.
Extract text, caption images, and specify slides.
"""
def _init_parser(self) -> Dict:
"""Init parser."""
try:
from pptx import Presentation # noqa: F401
except ImportError:
raise ImportError(
"The package `python-pptx` is required to read Powerpoint files: "
"`pip install python-pptx`"
)
try:
import torch # noqa: F401
except ImportError:
raise ImportError(
"The package `pytorch` is required to caption images: "
"`pip install torch`"
)
try:
from transformers import (
AutoTokenizer,
VisionEncoderDecoderModel,
ViTFeatureExtractor,
)
except ImportError:
raise ImportError(
"The package `transformers` is required to caption images: "
"`pip install transformers`"
)
try:
from PIL import Image # noqa: F401
except ImportError:
raise ImportError(
"PIL is required to read image files: " "`pip install Pillow`"
)
model = VisionEncoderDecoderModel.from_pretrained(
"nlpconnect/vit-gpt2-image-captioning"
)
feature_extractor = ViTFeatureExtractor.from_pretrained(
"nlpconnect/vit-gpt2-image-captioning"
)
tokenizer = AutoTokenizer.from_pretrained(
"nlpconnect/vit-gpt2-image-captioning"
)
return {
"feature_extractor": feature_extractor,
"model": model,
"tokenizer": tokenizer,
}
def caption_image(self, tmp_image_file: str) -> str:
"""Generate text caption of image."""
import torch
from PIL import Image
model = self.parser_config["model"]
feature_extractor = self.parser_config["feature_extractor"]
tokenizer = self.parser_config["tokenizer"]
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
max_length = 16
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
i_image = Image.open(tmp_image_file)
if i_image.mode != "RGB":
i_image = i_image.convert(mode="RGB")
pixel_values = feature_extractor(
images=[i_image], return_tensors="pt"
).pixel_values
pixel_values = pixel_values.to(device)
output_ids = model.generate(pixel_values, **gen_kwargs)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
return preds[0].strip()
def parse_file(self, file: Path, errors: str = "ignore") -> str:
"""Parse file."""
from pptx import Presentation
presentation = Presentation(file)
result = ""
for i, slide in enumerate(presentation.slides):
result += f"\n\nSlide #{i}: \n"
for shape in slide.shapes:
if hasattr(shape, "image"):
image = shape.image
# get image "file" contents
image_bytes = image.blob
# temporarily save the image to feed into model
image_filename = f"tmp_image.{image.ext}"
with open(image_filename, "wb") as f:
f.write(image_bytes)
result += f"\n Image: {self.caption_image(image_filename)}\n\n"
os.remove(image_filename)
if hasattr(shape, "text"):
result += f"{shape.text}\n"
return result
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