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from pydantic import BaseModel | |
from models.features import Feature as FeatureModel | |
from typing import List | |
from helpers.f5_model import f5_model | |
class Feature(BaseModel): | |
feature: str | |
short_description: str | |
class Features(BaseModel): | |
features: List[Feature] | |
async def generate_features(requirements: str): | |
query = ( | |
"See the user requirements and propose him the features (it should be 20 features). Feature names should be short. " | |
"The user will then choose one or more needed features. \n" | |
"User Requirements:\n" | |
f"{requirements}" | |
) | |
response = await f5_model.generate_response(query) | |
# Parse the response into Features structure | |
# You might need to add additional parsing logic here | |
features_dict = parse_features_response(response) | |
return Features(**features_dict) | |
def parse_features_response(response: str) -> dict: | |
# Add parsing logic here to convert F5 model output to Features format | |
# This is a placeholder implementation | |
features_list = [] | |
# Parse the response and create Feature objects | |
return {"features": features_list} | |