CharlesFeng0314 / JEV_sees

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# JEV Sees
### We gave JEV vision.
**JEV is fast, structured, and built to make decisions.**
JEV is the first **System One Model** from TypeSafe AI, built for fast, structured decisions inside software. JEV Sees is an independent open-source project built on JEV and `typesafe-sdk`; it is not an official TypeSafe AI vision product.
JEV Sees works with images, video, and RGB-D cameras. It finds the objects in view, keeps their IDs consistent across video frames, and asks JEV your question about each relevant object.
 Image · bus color 
 Video · pedestrian risk 
 Video · animal motion 
 Question: What color is the bus? Answer: blue 
 Question: Is this pedestrian currently involved in a car accident? Answer: one probability per pedestrian 
 Question: What is this animal's current motion state? Answer: walking 
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## Quick start
### 1. Install
 [code] 
JEV Sees requires Python 3.10+. The first vision run downloads the default Florence-2 and CLIP weights if they are not already available locally.
### 2. Add your JEV API key
Create a key at console.typesafe.ai/keys.
macOS / Linux:
 [code] 
PowerShell:
 [code] 
### 3. Ask your first visual question
This is a complete program, not a wrapper around one of the repository examples:
 [code] 
Typical output:
 [code] 
The saved image includes the detected bus, its stable object ID, and JEV's answer.
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## Included examples
All three examples use the same flow: media + a question + an explicit answer type. Each script calls JEV and prints structured output in the terminal.
### Bus color
- **Input:** `assets/bus.jpg`
- **Question:** “What color is the bus?”
- **Answer type:** `Choice` from colors supplied by the application
 [code] 
 [code] 
Source: `examples/bus_color.py`
### Pedestrian accident probability
- **Input:** the 42–50 second segment of `assets/traffic.mp4`
- **Question:** “Is this object currently involved in a car accident?”
- **Answer type:** probability for every pedestrian in each sampled frame
 [code] 
 [code] 
This asks about what is happening in the current frame; it is not a future-accident forecast. Source: `examples/traffic_relations.py`. Video attribution: `assets/CREDITS.md`.
### Animal motion
- **Input:** `assets/black_cat_walking.mp4`
- **Question:** “What is this animal's current motion state?”
- **Answer type:** `Choice` from `stationary`, `walking`, `running`, and `uncertain`
 [code] 
 [code] 
Source: `examples/animal_motion.py`
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## Choose the answer you need
JEV Sees does not guess the answer format from the wording of a question. Your code chooses it explicitly:
- `probability(...)` returns an independent value from 0 to 1 for every selected object. It uses JEV's official `Noul` question type.
- `choice(...)` selects from options supplied by your application and returns the selected option, confidence, and probability distribution.
- `score(...)` evaluates every selected object against a rubric supplied by your application.
 [code] 
Call `Sees().probability(..