PodSift: Podcasts mit KI zusammenfassen

PodSift: Podcasts mit KI zusammenfassen

Table of Contents:

1. Introduction
2. Understanding the Problem
3. Approach and Thought Process
4. Key Aspects of the Solution
5. Backend and Frontend Scripts
6. Deployment with Streamlit
7. Walkthrough of the App
8. Podcast Summary
9. Target Audience
10. Recommendation Based on Favorite Podcast
11. Key Moments from the Podcast
12. Conclusion

Introduction

In this article, we will explore the process of creating an application that summarizes podcast episodes for busy listeners. The aim is to provide a summary of each episode so that consumers can decide whether they want to invest their time in listening to the full podcast or not. We will Delve into the problem, approach, and thought process behind building such a product.

Understanding the Problem

The problem is straightforward - there are millions of podcasts available and not everyone has the time or mental bandwidth to go through each one. Therefore, there is a need for a solution that can inform users whether an upcoming episode would be of interest to them or worth their time. This problem revolves around busy users who require a time-efficient solution to cater to their needs.

Approach and Thought Process

To tackle this problem, we need to convert the passive activity of listening to a podcast into an active activity of reading a podcast summary. However, this transformation increases the cognitive load on the user. Therefore, it is essential to minimize this load by providing glanceable information that can quickly inform the user about the relevance and significance of a podcast episode.

Building upon this approach, the podcast summary should be concise and provide deciding information at a glance. It should highlight the nuggets of the episode and cater to the user's liking and preferences. Additionally, the application should engage with the user in a refreshing way and be open to receiving feedback.

Key Aspects of the Solution

The solution is based on two scripts - a backend script and a frontend script. The backend script utilizes chat GPT to summarize the podcast episode, extract information about the podcast guest, and compare podcast highlights. This script is hosted on a Modal platform, a hosted GPU service capable of containerizing and hosting Python code within seconds.

The frontend script is responsible for deployment and utilizes Streamlit, a framework for building data apps. The app provides the podcast summary, information about the target audience, recommendations based on the user's favorite podcast, and key moments from the podcast.

Backend and Frontend Scripts

The backend script contains various functions hosted on the Modal platform. It utilizes the prompt logic to enable chat GPT to summarize the podcast episode, extract information about the podcast guest, compare podcast highlights, and provide recommendations. All the Prompts and logic are contained within this script.

The frontend script is responsible for visualizing the app using Streamlit. It communicates with the backend script to retrieve the necessary information and present it to the user in an easily understandable format.

Deployment with Streamlit

The Streamlit framework is used to deploy the app. The code provided in the project utilizes the boilerplate code provided by the instructors. The app consists of different sections, including the podcast summary, information about the target audience, recommendations based on the user's favorite podcast, and key moments from the podcast.

Walkthrough of the App

The app starts by displaying the podcast summary. The summary is limited to around 70 words to ensure it is not too bulky for users to Read. This allows users to quickly read a Paragraph and decide whether they want to listen to the podcast. The next important section provides information about the target audience. This podcast specifically deals with the intersection of biology, AI, and machine learning, making it suitable for researchers and professionals in these fields.

Podcast Summary

The podcast summary is followed by a recommendation based on the user's favorite podcast. Users can select one of the pre-computed podcasts from the dropdown menu, simulating input to the model. The app then compares the selected podcast's transcript to the user's favorite podcast transcript, providing a recommendation score between one and ten. The higher the score, the higher the chance that the user would enjoy the selected podcast.

Target Audience

The application caters to researchers and professionals in the field of machine learning, computational biology, and clinical medicine. These individuals are more likely to find the podcast topic and content Relevant to their interests and professional development.

Recommendation Based on Favorite Podcast

The app allows the user to select their favorite podcast, which helps the recommendation system match their preferences. By comparing the transcripts of the selected podcast and the user's favorite podcast, the app provides a recommendation score. Additionally, the app provides an interpretation of the similarity or dissimilarity between the podcasts and explains why the new podcast may appeal or not appeal to the user.

Key Moments from the Podcast

Finally, the app presents some key moments from the podcast. These moments provide additional insights and highlights from the episode. However, they are kept at the end since the main focus is to cater to busy users, and consuming these key moments can be time-consuming.

Conclusion

In conclusion, this article has discussed the process of building an application that summarizes podcast episodes for busy listeners. It has explained the problem, approach, and thought process behind creating such a product. The article also provided details about the backend and frontend scripts, deployment with Streamlit, and a walkthrough of the app's features. By providing concise summaries, relevant information, and recommendations, this application aims to assist busy users in making informed decisions about podcast episodes.

Highlights:

1. An application to summarize podcast episodes for busy listeners
2. Providing a summary to help users decide whether to listen to the full podcast
3. Minimizing the cognitive load by providing glanceable information
4. Backend and frontend scripts for hosting and visualization
5. Recommendations based on the user's favorite podcast
6. Key moments from the podcast to provide additional insights

FAQ:

Q: How does the application summarize podcast episodes? A: The application utilizes chat GPT to summarize the podcast episodes. It converts the audio content into written summaries.

Q: What is the purpose of the target audience section? A: The target audience section provides information about the specific group of individuals for whom the podcast is most relevant. It helps users determine whether the podcast aligns with their interests and professional backgrounds.

Q: How does the app recommend podcasts based on the user's favorite? A: The app compares the transcripts of the selected podcast and the user's favorite podcast. It provides a recommendation score based on the similarity between the two. Higher scores indicate a higher chance of the user enjoying the selected podcast.

Q: Can users provide feedback on the application? A: Yes, the application is designed to be interactive and open to user feedback. Users can provide their opinions and suggestions to further enhance the recommendation system.

Q: Why are the key moments from the podcast kept at the end? A: The key moments section is placed at the end to prioritize the time efficiency of busy users. The main focus is to provide a quick summary and relevant information upfront. The key moments are provided for users who have more time to explore the episode further.