Project Info
This project did not submit a demo video on Devpost.
Inspiration
We were inspired by the movie theater and the hume ai table. seeing the emotion scores updating in real time instantly lit a fire to explore the new technology.
What it does
TrueReviews uses hume to analyze peoples reactions when watching movies and based on the emotions and reactions we create a movie review with a rating. this is then stored with postgres and sql. The reviews are then used to give people movie recommendations
How we built it
we take videos of reactions to movies and split into 15 second segments. Send this to hume and get the reactions and emotions from the viewer and create a top three emotions list. Then using postgresql db we store these reactions. This is then used to create a movie rating system and movie recommendations.
Challenges we ran into
Some issues we ran into is the wifi was slow and so my api requests with hume took too long and the timeout of 300ms according to some code on hume's github would stop my job before the predictions can be created. wifi made it so that queries were not able to download 10,000 videos.
Accomplishments we're proud of
attended a lot of the workshops and learned a lot of new skills
What we learned
We need to commit to making a project right away and move swiftly with brainstorming, decision making, and execution.
What's next
create more features and connect hume to postgresql db to the user.
True-Review
System to give recommendations for content consumption based off of content consumed and emotional response to that content.
Project started on 10/28/2023 in CalHacks. Outline for MVP:
- Collect data of movies that have existed
- Make automatic download query for video's of people's reactions to content consumed
- Query Hume AI to return JSON that will be processed into a recommendation for users of True Review
Analysis
View
Metric
- 5
- 1
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- PythonIn code
- PostgreSQLClaimed
- SQLClaimed
1 of 3 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
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Codebase size
Source size
11 KB
Source files
11
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
pormonto/True-Review
14 files · 40 KB · @ eac16bf
Structure
Application logic
13 files · 93%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- Python93%
- Markdown4%
- Shell3%
Share of indexed source by file size. Binary and vendored files are excluded.
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