Sweep: Add comments for parse_pdf

[simpx]

Add comments to explain the mechanism of function parse_pdf in app.py

[sweep-ai]

Here's the PR! #3.

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• Install Sweep Configs: Pull Request

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Step 1: 🔍 Code Search

I found the following snippets in your repository. I will now analyze these snippets and come up with a plan.

Some code snippets I looked at (click to expand). If some file is missing from here, you can mention the path in the ticket description.

chatdemo/app.py

Lines 1 to 136 in 910cdd4

	from PyPDF2 import PdfReader
	import gradio as gr
	import openai
	import os
	# The first line contains the OpenAI key, while the second line provides the OpenAI URL, which is useful when the OpenAI server is hidden behind a proxy server.
	# eg. first line "sk-xxxxxxxxxx", second line "http://PROXY-URL"
	if os.path.isfile('config'):
	config = open("config").readlines()
	else:
	config = ""
	api_key_from_config = ""
	if len(config) > 0 and len(config[0].strip()) > 0:
	api_key_from_config = config[0].strip()
	if len(config) > 1 and len(config[1].strip()) > 0:
	openai.api_base = config[1].strip()
	# config
	DEBUG = True
	'''
	gradio: [['first question', 'No'], ['second question', 'Yes']]
	openai: [{"role": "user", "content": "first question"}, {"role": "assistant", "content": "No"}
	{"role": "user", "content": "second question"}, {"role": "assistant", "content": "Yes"}]
	'''
	def gradio_messages_to_openai_messages(g):
	result = []
	for pair in g:
	result.append({"role": "user", "content": pair[0]})
	result.append({"role": "assistant", "content": pair[1]})
	return result
	def respond(chat_history, message, system_message, key_txt, url_txt, model, temperature):
	messages = [
	{"role": "system", "content": system_message},
	*gradio_messages_to_openai_messages(chat_history),
	{"role": "user", "content": message}
	]
	openai.api_key = key_txt if key_txt else api_key_from_config
	if url_txt:
	openai.api_base = url_txt
	if DEBUG:
	print("messages:", messages)
	print("model:", model)
	print("temperature:", temperature)
	completion = openai.ChatCompletion.create(
	model=model,
	messages=messages,
	temperature=temperature,
	)
	if DEBUG:
	print("completion:", completion)
	response = completion['choices'][0]['message']['content']
	result = chat_history + [[message, response]]
	return result
	def parse_pdf(prompt, pdfs, system_message, key_txt, url_txt, model, temperature):
	result = ""
	full_text = ""
	for pdf in pdfs:
	print("parse: ", pdf)
	text = ""
	reader = PdfReader(pdf.name)
	for page in reader.pages:
	text = text + page.extract_text()
	full_text = text + "\n----------\n"
	messages = [
	{"role": "system", "content": system_message},
	{"role": "user", "content": prompt + "\n\n###\n\n " + full_text}
	]
	openai.api_key = key_txt if key_txt else api_key_from_config
	if url_txt:
	openai.api_base = url_txt
	if DEBUG:
	print("messages:", messages)
	print("model:", model)
	print("temperature:", temperature)
	completion = openai.ChatCompletion.create(
	model=model,
	messages=messages,
	temperature=temperature,
	)
	if DEBUG:
	print("completion:", completion)
	response = completion['choices'][0]['message']['content']
	return response
	with gr.Blocks() as demo:
	with gr.Tab("Config"):
	with gr.Row():
	key_txt = gr.Textbox(label = "Openai Key", placeholder="Enter openai key 'sk-xxxx'%s" %
	(", Leave empty to use value from config file" if api_key_from_config else ""))
	url_txt = gr.Textbox(label = "Openai API Base URL", placeholder="Enter openai base url 'https://xxx', Leave empty to use value '%s'" % openai.api_base)
	system_message = gr.Textbox(label = "System Message:", value = "You are an assistant who gives brief and concise answers.")
	model = gr.Dropdown(label="Model", choices=["gpt-3.5-turbo", "gpt-3.5-turbo-0301", "gpt-4"], multiselect=False, value="gpt-3.5-turbo", type="value")
	temperature = gr.Slider(0, 2, value=1, label="Temperature", step=0.1, info="What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.")
	with gr.Tab("Chat"):
	gr.Markdown("## Chat with GPT")
	chatbot = gr.Chatbot()
	message = gr.Textbox(label = "Message:", placeholder="Enter text and press 'Send'")
	message.submit(
	respond,
	[chatbot, message, system_message, key_txt, url_txt, model, temperature],
	chatbot,
	)
	with gr.Row():
	clear = gr.Button("Clear")
	clear.click(lambda: None, None, chatbot)
	send = gr.Button("Send")
	send.click(
	respond,
	[chatbot, message, system_message, key_txt, url_txt, model, temperature],
	chatbot,
	)
	with gr.Tab("PDF"):
	gr.Markdown("## Parse PDF with GPT")
	prompt = gr.Text(label="Prompt")
	pdfs = gr.File(label="Upload PDF", file_count="multiple", file_types=[".pdf"])
	markdown = gr.Markdown(label="Output")
	with gr.Row():
	clear = gr.Button("Clear")
	clear.click(lambda: None, None, markdown)
	submit = gr.Button("Upload")
	submit.click(
	parse_pdf,
	[prompt, pdfs, system_message, key_txt, url_txt, model, temperature],
	markdown
	)
	if __name__ == "__main__":
	demo.launch()
	else:
	# run with "gradio app.py"
	demo.launch(server_name="0.0.0.0")

chatdemo/README.md

Lines 1 to 50 in 910cdd4

	---
	title: Chatdemo
	emoji: 🌍
	colorFrom: green
	colorTo: purple
	sdk: gradio
	sdk_version: 3.20.1
	app_file: app.py
	pinned: false
	license: mit
	---
	Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
	# ChatDemo
	This is a basic ChatGPT demo based on ChatAPI and gradio framework
	## Installation and Usage
	```bash
	git clone https://github.com/simpx/chatdemo.git
	cd chatdemo
	pip install -r requirements.txt
	```
	#### Start Directly
	Access through `http://127.0.0.1:7860`
	```bash
	python app.py
	```
	#### Start with gradio
	Automatically reload after modify the code, Default listening on 0.0.0.0 allows hosting in the cloud
	```bash
	sh run.sh
	```
	#### Chat
	1. Configure on the `Config` tab, or create the 'config' file in the same directory as app.py.
	2. Chat on the `Chat` tab
	#### Hosting with HuggingFace Spaces
	1. Create a new Space at https://huggingface.co/new-space and choose `Gradio` as the Space SDK.
	2. Push this project to the newly created Space.
	3. Wait for the build to complete, then access the Space page.
	see example: https://huggingface.co/spaces/simpx/chatdemo

chatdemo/requirements.txt

Lines 1 to 3 in 910cdd4

	openai
	gradio
	PyPDF2

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Step 2: 🧐 Snippet Analysis

From looking through the relevant snippets, I decided to make the following modifications:

File Path

Proposed Changes

app.py

Add comments to the parse_pdf function to explain its purpose and mechanism. Begin with a general comment at the start of the function to explain what it does. Then, add comments throughout the function to explain the purpose of each significant line or block of code. For example, explain the purpose of the for loop that iterates over the pages of the PDF, the creation of the messages list, and the call to openai.ChatCompletion.create.

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Step 3: 📝 Planning

I have created a plan for writing the pull request. I am now working my plan and coding the required changes to address this issue. Here is the planned pull request:

Add comments for parse_pdf function in app.py
sweep/add-comments-parse-pdf

Description

This PR adds comments to the parse_pdf function in the app.py file. The function is responsible for parsing the text from a PDF file and using it as input for the OpenAI GPT model. The lack of comments made it difficult for other developers to understand the function's mechanism and purpose. The added comments provide a clear explanation of the function's overall purpose and the purpose of each significant line or block of code within the function.

Summary of Changes

• Added a general comment at the beginning of the parse_pdf function to explain its purpose.
• Added comments throughout the function to explain the purpose of each significant line or block of code.
• Explained the purpose of the for loop that iterates over the pages of the PDF.
• Explained the creation of the messages list and its role in generating a response from the GPT model.
• Explained the call to openai.ChatCompletion.create and its role in generating a response from the GPT model.

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Step 4: ⌨️ Coding

Here is my review for my PR:

Here is the 1th review

Thanks for your contribution. The comments added to the parse_pdf function in app.py are helpful. However, there are a couple of potential issues that need to be addressed:

• On lines 54-71, it seems like the PdfReader class is being used without being imported. If it's not imported elsewhere in the code, please add the appropriate import statement at the top of the file. For example, if PdfReader is part of the pdfplumber package, you would add from pdfplumber import PdfReader.

• On lines 72-92, the variable api_key_from_config is used but it's not clear where it's defined. If it's defined in another file, please make sure to import it. If it's supposed to be a local variable, please define it within the function.

Please make these changes and update the pull request. If you have any questions, feel free to ask.

Here is the 2th review

Hi there,

Great job on your recent changes to the parse_pdf function in app.py. The added comments make the code much easier to understand. However, there are a few changes that need to be made:

• In app.py, the PdfReader class is used on line 60, but it doesn't seem to be imported anywhere. This could lead to a NameError at runtime. Please add the necessary import statement at the beginning of the file.

Keep up the good work!

Here is the 3th review

Thanks for your contribution. There's just one small change that needs to be made:

• In app.py, on lines 60-63, you're overwriting the full_text variable in each iteration of the loop. If you want to concatenate the text from all PDF files, you should use the += operator instead of =. So, the line should be full_text += text + "\n----------\n".

Please make this change and update the pull request.

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Step 5: 🔁 Code Review

Success! 🚀

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