说明
只是为了速记一下这个实践过程。整体上说，这个结果并不是那么好用，但有一些可以借鉴的地方。
先看结果：
生成的PPT
说的直白点，就是用大模型生成了一堆没太有意义的文字，然后做成ppt。所以实用是不成的，但是里面有一些过程可以借鉴。
内容
1 项目地址
整个项目没有几个文件，感觉也就是一个原型实验。
需要的环境是本地ollama(恰好我有），然后拉一个模型
项目里还漏了 requirement.txt，我的环境下，主要再安装一个包就可以了
python
pip3 install python-pptx -i https://mirrors.aliyun.com/pypi/simple/2 使用
启动 streamlit run main.py,然后会弹出一个web页面，输入一个主题，然后就返一个ppt给你。整体过程大概1~2分钟，考虑是笔记本，而且也看到什么资源占用，所以可以认为是不太占资源的。
3 核心代码
使用langchain加载了ollama模型，生成数据
python3
def slide_data_gen(topic):
llm = Ollama(model="dolphin2.1-mistral",
temperature="0.4")
slide_data = []
point_count = 5
slide_data.append(extract_items(llm(f"""
You are a text summarization and formatting specialized model that fetches relevant information
For the topic "{topic}" suggest a presentation title and a presentation subtitle it should be returned in the format :
<< "title" | "subtitle >>
example :
<< "Ethics in Design" | "Integrating Ethics into Design Processes" >>
""")))
slide_data.append(extract_items(llm(f"""
You are a text summarization and formatting specialized model that fetches relevant information
For the presentation titled "{slide_data[0][0]}" and with subtitle "{slide_data[0][1]}" for the topic "{topic}"
Write a table of contents containing the title of each slide for a 7 slide presentation
It should be of the format :
<< "slide1" | "slide2" | "slide3" | ... | >>
example :
<< "Introduction to Design Ethics" | "User-Centered Design" | "Transparency and Honesty" | "Data Privacy and Security" | "Accessibility and Inclusion" | "Social Impact and Sustainability" | "Ethical AI and Automation" | "Collaboration and Professional Ethics" >>
""")))
for subtopic in slide_data[1]:
data_to_clean = llm(f"""
You are a content generation specialized model that fetches relevant information and presents it in clear concise manner
For the presentation titled "{slide_data[0][0]}" and with subtitle "{slide_data[0][1]}" for the topic "{topic}"
Write the contents for a slide with the subtopic {subtopic}
Write {point_count} points. Each point 10 words maximum.
Make the points short, concise and to the point.
""")
cleaned_data = llm(f"""
You are a text summarization and formatting specialized model that fetches relevant information and formats it into user specified formats
Given below is a text draft for a presentation slide containing {point_count} points , extract the {point_count} sentences and format it as :
<< "point1" | "point2" | "point3" | ... | >>
example :
<< "Foster a collaborative and inclusive work environment." | "Respect intellectual property rights and avoid plagiarism." | "Uphold professional standards and codes of ethics." | "Be open to feedback and continuous learning." >>
-- Beginning of the text --
{data_to_clean}
-- End of the text --
""")
slide_data.append([subtopic] + extract_items(cleaned_data))
return slide_data根据数据生成ppt
python
def ppt_gen(slide_data):
ppt = Presentation()
# Setting Background
slide_master = ppt.slide_master
slide_master.background.fill.solid()
slide_master.background.fill.fore_color.rgb = RGBColor(0, 0, 0)
# Title Screen
curr_slide = ppt.slides.add_slide(ppt.slide_layouts[0])
curr_slide.shapes.title.text = slide_data[0][0]
curr_slide.shapes.title.text_frame.auto_size = MSO_AUTO_SIZE.TEXT_TO_FIT_SHAPE
curr_slide.shapes.title.text_frame.paragraphs[0].runs[0].font.color.rgb = RGBColor(
255, 255, 255)
curr_slide.shapes.placeholders[1].text = slide_data[0][1]
curr_slide.shapes.placeholders[1].text_frame.auto_size = MSO_AUTO_SIZE.TEXT_TO_FIT_SHAPE
curr_slide.shapes.placeholders[1].text_frame.paragraphs[0].runs[0].font.color.rgb = RGBColor(
255, 255, 255)
# Overview
curr_slide = ppt.slides.add_slide(ppt.slide_layouts[1])
curr_slide.shapes.title.text = "Overview"
curr_slide.shapes.title.text_frame.auto_size = MSO_AUTO_SIZE.TEXT_TO_FIT_SHAPE
curr_slide.shapes.title.text_frame.paragraphs[0].runs[0].font.color.rgb = RGBColor(
255, 255, 255)
for content in slide_data[1]:
tframe = curr_slide.shapes.placeholders[1].text_frame
tframe.auto_size = MSO_AUTO_SIZE.TEXT_TO_FIT_SHAPE
para = tframe.add_paragraph()
para.text = content
para.level = 1
para.font.color.rgb = RGBColor(
255, 255, 255)
# Content Slides
for curr_slide_data in slide_data[2:]:
curr_slide = ppt.slides.add_slide(ppt.slide_layouts[1])
curr_slide.shapes.title.text = curr_slide_data[0]
curr_slide.shapes.title.text_frame.auto_size = MSO_AUTO_SIZE.TEXT_TO_FIT_SHAPE
curr_slide.shapes.title.text_frame.paragraphs[0].font.color.rgb = RGBColor(
255, 255, 255)
for content in curr_slide_data[1:]:
tframe = curr_slide.shapes.placeholders[1].text_frame
tframe.auto_size = MSO_AUTO_SIZE.TEXT_TO_FIT_SHAPE
para = tframe.add_paragraph()
para.text = content
para.level = 1
para.font.color.rgb = RGBColor(
255, 255, 255)
# Thank You Screen
curr_slide = ppt.slides.add_slide(ppt.slide_layouts[2])
curr_slide.shapes.placeholders[1].text = "Thank You"
curr_slide.shapes.placeholders[1].text_frame.paragraphs[0].font.color.rgb = RGBColor(
255, 255, 255)
curr_slide.shapes.placeholders[1].text_frame.paragraphs[0].font.size = Pt(
96)
curr_slide.shapes.placeholders[1].text_frame.paragraphs[0].alignment = PP_ALIGN.CENTER
# f"{sanitize_string(slide_data[0][0])}.pptx"
ppt_stream = io.BytesIO()
ppt.save(ppt_stream)
ppt_stream.seek(0)
return ppt_stream4 总结
- 1 可以参考langchain，进行增强。langchain本身还有agent的能力，继续融合后，是有可能完全不一样的。例如当你提到一个论点，agent可以自动查询实时数据，找到论据来support.
- 2 ppt生成（及读取）。之前比较少用python操作ppt,现在看来，读和简单写应该是没问题的。