System Operational

Fruit & Vegetable
Recognizer API

Production-grade computer vision API for real-time identification of 30+ fruit and vegetable varieties. Optimized for industrial sorting and inventory management.

Endpoint Configuration
POST http://localhost:5000/api/v1/predict/fruit_and_vegatable_recognizer

Submit an image or video file for automated classification. The engine uses a fine-tuned ResNet50 architecture capable of sub-100ms inference on standard payloads.

Request Parameters
Parameter Type Requirement Description
image file Optional* Buffer of the image (JPG/PNG). Maximum 10MB.
video file Optional* Buffer of the video (MP4). Maximum 50MB.

* At least one of 'image' or 'video' must be provided in the multipart/form-data payload.

Response Schema

The API returns a JSON object containing global metadata and specific inference results. 

{
  "success": true,
  "predictions": [
    {
      "label": "Apples",
      "confidence": 0.9842,
      "box": [120, 45, 300, 410]
    }
  ],
  "execution_time": "84ms"
}
Testing & Deployment

Want to test the model without writing code? Use our visual test lab to upload files directly and see the results mapped in real-time.

Launch Visual Test Lab 
import requests

url = "http://localhost:5000/api/v1/predict/fruit_and_vegatable_recognizer"
files = {'image': open('fruit.jpg', 'rb')}

response = requests.post(url, files=files)
print(response.json())