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from fastapi import APIRouter, HTTPException
from starfish.common.logger import get_logger
from starfish import StructuredLLM, data_factory
from web.api.storage import save_dataset, list_datasets_from_storage, get_dataset_from_storage
logger = get_logger(__name__)
router = APIRouter(prefix="/dataset", tags=["dataset"])
@data_factory()
async def default_eval(input_data):
if not isinstance(input_data, str):
input_data = input_data
eval_llm = StructuredLLM(
prompt="Given input data {{input_data}} please give it score from 1 to 10",
output_schema=[{"name": "quality_score", "type": "int"}],
model_name="gpt-4o-mini",
)
eval_response = await eval_llm.run(input_data=input_data)
return eval_response.data
@router.post("/evaluate")
async def evaluate_dataset(request: dict):
"""
Evaluate a dataset with the given inputs.
This endpoint evaluates a dataset with the given inputs and returns the output.
Returns:
The result of evaluating the dataset
"""
try:
# logger.info(f"Evaluating dataset: {request}")
result = request["evaluatedData"]
input_data = []
for item in result:
input_data.append(str(item))
processed_data = default_eval.run(input_data=input_data)
processed_data_index = default_eval.get_index_completed()
for i in range(len(processed_data_index)):
result[processed_data_index[i]]["quality_score"] = processed_data[i]["quality_score"]
# for item in result:
# item["quality_score"] = 6
return result
except Exception as e:
logger.error(f"Error evaluating dataset: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error evaluating dataset: {str(e)}")
@router.post("/save")
async def save_dataset_api(request: dict):
"""
Save a dataset with the given inputs.
This endpoint saves a dataset with the given inputs and returns the output.
"""
try:
# logger.info(f"Saving dataset: {request}")
await save_dataset(request["projectId"], request["datasetName"], request["data"])
return request
except Exception as e:
logger.error(f"Error saving dataset: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error saving dataset: {str(e)}")
@router.post("/list")
async def list_datasets(request: dict):
"""
List all datasets for a given project.
"""
try:
datasets = await list_datasets_from_storage(request["projectId"], request["datasetType"])
return datasets
except Exception as e:
logger.error(f"Error listing datasets: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error listing datasets: {str(e)}")
@router.post("/get")
async def get_dataset(request: dict):
"""
Get a dataset with the given inputs.
"""
try:
dataset = await get_dataset_from_storage(request["projectId"], request["datasetName"])
return dataset
except Exception as e:
logger.error(f"Error getting dataset: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error getting dataset: {str(e)}")
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