OpenAI Python Colab to Summarize and Chat with PDF

Hacker News

Found some open source code from a medium article https://medium.com/@kapildevkhatik2/the-ultimate-guide-to-pd... . The article uses python code and the openai davinci model to summarize a pdf document. I copied the code into a Google Colab and extended the code to allow for new 3.5 Turbo chat completion functionality so you can chat with a pdf document. There are a lot of different tools out there that can give you the ability to chat with documents but I think having the code in a Colab has advantages for people interested in learning programming and Colab's are great. Obviously there are a lot more features that could have been added but I wanted to show how easy it is to get started making your AI tools. Start to finish this took about 15 minutes to get configured. Plan on doing more of these this year so send any feedback!

Visit product View on Hacker NewsAug 22, 2023

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: model, google, new · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Simple PDF Annotations in Python60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Simple PDF Annotations in Python

Hacker News4

Summarize and Chat with Any Video32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Summarize and Chat with Any Video

Hacker News2

A simple Python abstraction for Llama2 Chat51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A simple Python abstraction for Llama2 Chat

Hacker News2

PDF.chat48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PDF.chat

Hacker News1

PdfGPT24%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Chat wih pdf

Indie Hackers

Attorch – PyTorch's nn module written in Python using OpenAI's Triton67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Attorch – PyTorch's nn module written in Python using OpenAI's Triton

Hacker News4

Get Kevin Hart to summarize your readings52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Get Kevin Hart to summarize your readings

Hacker News2

Wordcab, Summarize Every Call42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Wordcab, Summarize Every Call

Hacker News1

Clipped – Summarize Anything42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Clipped – Summarize Anything

Hacker News2

CustomStringParser ( Python)54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

CustomStringParser ( Python)

Hacker News1

Browse all products Analyze your launch