Performance & cost
This session
- Delivery time · median
- —
- Live model compute
- —
- Live input tokens
- 0
- Est. cost · USD
- $0.00
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Model compute is the average per measured request. Delivery time is the median browser time for successful requests, including queue, network, and retries. Cached delivery speed is not inference speed. Model compute can exceed browser time when one request processes many inputs.
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Pick a produce photo and classify it.
Build with the SDKTypeScript · Python · dm1
One request from this demo. Copy it into your app.
Uses your MS_API_KEY and quota. Keep the key on your server.
npm install @cloudraker/milliseconds
export MS_API_KEY="your-api-key"import { readFile } from "node:fs/promises";
import { DecisionMachine } from "@cloudraker/milliseconds";
// Run on your server; reads MS_API_KEY from the environment.
// The image carries the input; the leading text is optional context.
const dm = new DecisionMachine();
const { result, usage } = await dm.classify(
await readFile("image.jpg"),
{
"sound": "Produce with a visually sound exterior: typical color and shape, without obvious decay, extensive discoloration, mold or severe shriveling.",
"deteriorated": "Produce with visible deterioration: brown or dark patches, mold, pronounced wrinkling, shriveling or substantial discoloration. Inspect the exterior appearance only."
},
{ detail: "low" }
).withUsage();
console.log(result);
console.log({ inputTokens: usage.inputTokens, modelMs: usage.inferenceMs });pip install cloudraker-milliseconds
export MS_API_KEY="your-api-key"from pathlib import Path
from milliseconds import DecisionMachine
# Reads MS_API_KEY from the environment.
# The image carries the input; the leading text is optional context.
dm = DecisionMachine()
result = dm.classify(
Path("image.jpg").read_bytes(),
{
"sound": "Produce with a visually sound exterior: typical color and shape, without obvious decay, extensive discoloration, mold or severe shriveling.",
"deteriorated": "Produce with visible deterioration: brown or dark patches, mold, pronounced wrinkling, shriveling or substantial discoloration. Inspect the exterior appearance only."
},
detail="low"
)
print(result)npm install -g @cloudraker/milliseconds
export MS_API_KEY="your-api-key"# Reads MS_API_KEY. The image and the other fields are read from disk.
cat > classify-labels.json <<'DM1_LABELS'
{
"sound": "Produce with a visually sound exterior: typical color and shape, without obvious decay, extensive discoloration, mold or severe shriveling.",
"deteriorated": "Produce with visible deterioration: brown or dark patches, mold, pronounced wrinkling, shriveling or substantial discoloration. Inspect the exterior appearance only."
}
DM1_LABELS
dm1 classify --image image.jpg --detail low --labels @classify-labels.json --json --usageUnder the hood How it works & limitations
API calls/classify
A visual first pass
Send produce with visible discoloration, wrinkling or damage to a person for a closer look.
This checks appearance only. It cannot determine edibility, freshness inside the produce or food safety. Ripening and lighting can resemble deterioration.
Real photographs, traceable labels
Six photographs from the bitter-gourd, capsicum and tomato classes. The publisher includes rotated/augmented photos; those borders are preserved. Fresh/stale source labels map to sound/deteriorated appearance for this demo and are not food-safety ground truth. Resized and JPEG-compressed.
Photographs: Raghav R Potdar, Adithya Shrivastava, Rahul Sohandani and Naren Khatwani (Food Aayush). Fresh and Stale Images of Fruits and Vegetables, under CC0 1.0.
These examples demonstrate the API; they are not an accuracy benchmark. Compare the model reading with the source label and review uncertain results.