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AI Studio
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U
user@example.com
Free Plan
What will you build?
Push Gemini to the limits of what AI can do
Unit Testing
Add unit tests for a Python function.
Recipe List
List recipes in JSON format.
Bake to JSON
Image to recipe in JSON.
This experimental model is for feedback and testing only.
Prompt Editor
0 tokens
|
Unit Testing Results
Testing Python function output
Test Case #1: Basic Input Validation
Input: validate_input("test_string")
Expected: True
Actual: True
Time: 0.003sTest Case #2: Edge Case Handling
Input: validate_input("")
Expected: False
Actual: TypeError
Time: 0.002sError: TypeError: Expected string input, received empty string
Test Case #3: Performance Test
Input: validate_input("large_input_string" * 1000)
Expected: True
Actual: True
Time: 1.234sWarning: Execution time exceeded threshold (1.0s)
Total Tests
3
Passed
1
Failed
1
Warnings
1
API Reference
Complete documentation for the Gemini API
GET
/api/v1/generate
Generate AI Response
Generate an AI response based on the provided prompt and parameters.
Parameters
{
"prompt": "string",
"max_tokens": "integer",
"temperature": "float",
"top_p": "float"
}
Response
{
"id": "string",
"created": "timestamp",
"text": "string",
"tokens_used": "integer"
}
POST
/api/v1/train
Train Custom Model
Train a custom model using provided dataset and parameters.
Request Body
{
"dataset_url": "string",
"model_name": "string",
"epochs": "integer",
"batch_size": "integer"
}
Response
{
"model_id": "string",
"status": "string",
"training_time": "integer",
"metrics": {
"accuracy": "float",
"loss": "float"
}
}
Error Codes
400
Bad Request
401
Unauthorized
403
Forbidden
404
Not Found
429
Too Many Requests
500
Server Error
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Documentation
Learn how to build with Gemini AI