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import json
import os
import shutil
from collections import defaultdict
from jinja2 import Template
import src.llms as llms
from src.model_utils import get_cluster, get_image_embedding, images_cosine_similarity
from src.presentation import Presentation
from src.utils import Config, pexists, pjoin, tenacity
class SlideInducter:
"""
Stage I: Presentation Analysis.
This stage is to analyze the presentation: cluster slides into different layouts, and extract content schema for each layout.
"""
def __init__(
self,
prs: Presentation,
ppt_image_folder: str,
template_image_folder: str,
config: Config,
image_models: list,
):
"""
Initialize the SlideInducter.
Args:
prs (Presentation): The presentation object.
ppt_image_folder (str): The folder containing PPT images.
template_image_folder (str): The folder containing normalized slide images.
config (Config): The configuration object.
image_models (list): A list of image models.
"""
self.prs = prs
self.config = config
self.ppt_image_folder = ppt_image_folder
self.template_image_folder = template_image_folder
assert (
len(os.listdir(template_image_folder))
== len(prs)
== len(os.listdir(ppt_image_folder))
)
self.image_models = image_models
self.slide_induction = defaultdict(lambda: defaultdict(list))
model_identifier = llms.get_simple_modelname(
[llms.language_model, llms.vision_model]
)
self.output_dir = pjoin(config.RUN_DIR, "template_induct", model_identifier)
self.split_cache = pjoin(self.output_dir, f"split_cache.json")
self.induct_cache = pjoin(self.output_dir, f"induct_cache.json")
os.makedirs(self.output_dir, exist_ok=True)
def layout_induct(self):
"""
Perform layout induction for the presentation.
"""
if pexists(self.induct_cache):
return json.load(open(self.induct_cache))
content_slides_index, functional_cluster = self.category_split()
for layout_name, cluster in functional_cluster.items():
for slide_idx in cluster:
content_type = self.prs.slides[slide_idx - 1].get_content_type()
self.slide_induction[layout_name + ":" + content_type]["slides"].append(
slide_idx
)
for layout_name, cluster in self.slide_induction.items():
cluster["template_id"] = cluster["slides"][-1]
functional_keys = list(self.slide_induction.keys())
function_slides_index = set()
for layout_name, cluster in self.slide_induction.items():
function_slides_index.update(cluster["slides"])
used_slides_index = function_slides_index.union(content_slides_index)
for i in range(len(self.prs.slides)):
if i + 1 not in used_slides_index:
content_slides_index.add(i + 1)
self.layout_split(content_slides_index)
if self.config.DEBUG:
for layout_name, cluster in self.slide_induction.items():
cluster_dir = pjoin(self.output_dir, "cluster_slides", layout_name)
os.makedirs(cluster_dir, exist_ok=True)
for slide_idx in cluster["slides"]:
shutil.copy(
pjoin(self.ppt_image_folder, f"slide_{slide_idx:04d}.jpg"),
pjoin(cluster_dir, f"slide_{slide_idx:04d}.jpg"),
)
self.slide_induction["functional_keys"] = functional_keys
json.dump(
self.slide_induction,
open(self.induct_cache, "w"),
indent=4,
ensure_ascii=False,
)
return self.slide_induction
def category_split(self):
"""
Split slides into categories based on their functional purpose.
"""
if pexists(self.split_cache):
split = json.load(open(self.split_cache))
return set(split["content_slides_index"]), split["functional_cluster"]
category_split_template = Template(open("prompts/category_split.txt").read())
functional_cluster = llms.language_model(
category_split_template.render(slides=self.prs.to_text()),
return_json=True,
)
functional_slides = set(sum(functional_cluster.values(), []))
content_slides_index = set(range(1, len(self.prs) + 1)) - functional_slides
json.dump(
{
"content_slides_index": list(content_slides_index),
"functional_cluster": functional_cluster,
},
open(self.split_cache, "w"),
indent=4,
ensure_ascii=False,
)
return content_slides_index, functional_cluster
def layout_split(self, content_slides_index: set[int]):
"""
Cluster slides into different layouts.
"""
embeddings = get_image_embedding(self.template_image_folder, *self.image_models)
assert len(embeddings) == len(self.prs)
template = Template(open("prompts/ask_category.txt").read())
content_split = defaultdict(list)
for slide_idx in content_slides_index:
slide = self.prs.slides[slide_idx - 1]
content_type = slide.get_content_type()
layout_name = slide.slide_layout_name
content_split[(layout_name, content_type)].append(slide_idx)
for (layout_name, content_type), slides in content_split.items():
sub_embeddings = [
embeddings[f"slide_{slide_idx:04d}.jpg"] for slide_idx in slides
]
similarity = images_cosine_similarity(sub_embeddings)
for cluster in get_cluster(similarity):
slide_indexs = [slides[i] for i in cluster]
template_id = max(
slide_indexs,
key=lambda x: len(self.prs.slides[x - 1].shapes),
)
cluster_name = (
llms.vision_model(
template.render(
existed_layoutnames=list(self.slide_induction.keys()),
),
pjoin(self.ppt_image_folder, f"slide_{template_id:04d}.jpg"),
)
+ ":"
+ content_type
)
self.slide_induction[cluster_name]["template_id"] = template_id
self.slide_induction[cluster_name]["slides"] = slide_indexs
@tenacity
def content_induct(self):
"""
Perform content schema extraction for the presentation.
"""
self.slide_induction = self.layout_induct()
content_induct_prompt = Template(open("prompts/content_induct.txt").read())
for layout_name, cluster in self.slide_induction.items():
if "template_id" in cluster and "content_schema" not in cluster:
schema = llms.language_model(
content_induct_prompt.render(
slide=self.prs.slides[cluster["template_id"] - 1].to_html(
element_id=False, paragraph_id=False
)
),
return_json=True,
)
for k in list(schema.keys()):
if "data" not in schema[k]:
raise ValueError(f"Cannot find `data` in {k}\n{schema[k]}")
if len(schema[k]["data"]) == 0:
print(f"Empty content schema: {schema[k]}")
schema.pop(k)
assert len(schema) > 0, "No content schema generated"
self.slide_induction[layout_name]["content_schema"] = schema
json.dump(
self.slide_induction,
open(self.induct_cache, "w"),
indent=4,
ensure_ascii=False,
)
return self.slide_induction
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