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import requests
import json
import os
import os
from dotenv import load_dotenv
import streamlit as st
API_KEY = st.secrets["hf_token"]
def generate_schema(user_prompt):
""" Generates a synthetic dataset schema using Hugging Face API. """
system_prompt = """
You are an expert data scientist designing synthetic datasets.
For any given dataset description, generate:
- Column names
- Data types (string, int, float, date)
- Approximate row count
Output in **pure JSON** format like:
{
"columns": ["PatientID", "Age", "Gender", "Diagnosis"],
"types": ["int", "int", "string", "string"],
"size": 500
}
"""
payload = {
"inputs": system_prompt + "\n\nUser request: " + user_prompt,
"options": {"wait_for_model": True}
}
response = requests.post(HF_MODEL_URL, headers={"Authorization": f"Bearer {API_KEY}"}, json=payload)
if response.status_code == 200:
try:
output = response.json()[0]['generated_text']
schema = json.loads(output.strip()) # Convert to JSON
return schema
except json.JSONDecodeError:
return {"error": "Invalid JSON output from model. Try again."}
else:
return {"error": f"API request failed. Status Code: {response.status_code}"} |